Characteristics and Hematological Indicators Predictors of Immunotherapy Response in Advanced Non-Small Cell Lung Cancer

Characteristics and Hematological Indicators Predictors of Immunotherapy Response in Advanced Non-Small Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics and Hematological Indicators Predictors of Immunotherapy Response in Advanced Non-Small Cell Lung Cancer Nan Zhao, Xinyu Wu, Wensi Zhao, Xiang Cheng, Hua He, Dedong Cao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8723320/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Objective: Immune checkpoint inhibitors (ICIs) have significantly improved the treatment outcomes for advanced non-small cell lung cancer (NSCLC), but patient benefits vary individually. Therefore, identifying biomarkers to predict the efficacy and prognosis of immunotherapy is crucial. Hematological markers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-bilirubin (ALBI) score, and lactate dehydrogenase (LDH) levels may correlate with tumor prognosis. This study aimed to evaluate the prognostic value of these hematological and clinical markers in advanced NSCLC patients treated with ICIs, providing a basis for individualized treatment strategies. Methods: This retrospective study included NSCLC patients treated with ICIs at the Tumor Centers of Renmin Hospital of Wuhan University and Macheng City People’s Hospital between January 2021 and December 2023. Clinical data such as gender, age, ECOG PS score, clinical stage, and treatment details were collected. Patients were stratified based on NLR, PLR, LDH, and ALBI scores. ROC curve analysis assessed the predictive capacity of these markers for mortality risk. Chi-square tests, logistic regression, Kaplan-Meier survival analysis, and log-rank tests were used to analyze short-term efficacy, immune-related adverse events (irAEs), progression-free survival (PFS), and overall survival (OS). Cox regression identified independent prognostic factors for PFS and OS. Results: A total of 198 advanced NSCLC patients (median follow-up: 28.1 months) were included. Median PFS (mPFS) and OS (mOS) were 7.7 months (95% CI: 6.9~8.5) and 20.1 months (95% CI: 18.2~21.9), respectively. ROC analysis demonstrated significant predictive value for baseline NLR, PLR, ALBI, and LDH in mortality risk (AUC: 0.804, 0.694, 0.684, 0.726, respectively). Efficacy analysis revealed PD-L1 positivity (OR = 0.361, 95% CI: 0.161~0.808, P = 0.013) and ALBI < -2.68 (OR = 2.524, 95% CI: 1.148–5.552, P = 0.021) as independent predictors of objective response rate (ORR: 25.8%), while camrelizumab significantly reduced response rates (OR = 0.157, P = 0.006). In the squamous cell carcinoma subgroup, radiotherapy was an independent protective factor for tumor response (OR = 0.298, 95% CI: 0.094–0.95, P = 0.041). Baseline NLR, PLR, ALBI, and LDH significantly stratified PFS (P < 0.05): low NLR (8.7 vs. 7.1 months, P = 0.001), low PLR (7.9 vs. 7.4 months, P = 0.007), low ALBI (8.1 vs. 7.0 months, P = 0.028), and low LDH (9.5 vs. 7.1 months; 46.3% risk reduction, P < 0.001). In the adenocarcinoma subgroup, LDH remained an independent prognostic factor (9.5 vs. 6.8 months, P = 0.032). For squamous cell carcinoma, low NLR (9.1 vs. 7.1 months, P = 0.002), low PLR (10.2 vs. 7.4 months, P = 0.002), low ALBI (8.7 vs. 7.1 months, P = 0.022), and low LDH (9.1 vs. 7.5 months, P = 0.002) were significant. Baseline markers also predicted OS: low NLR (21.4 vs. 17.5 months, P < 0.001), low PLR (20.4 vs. 17.7 months, P = 0.009), and low LDH (21.4 months; 42.5% risk reduction, P < 0.001). Subtype analysis showed adenocarcinoma benefited from low NLR (21.4 vs. 16.5 months, P = 0.013) and low LDH (20.1 vs. 17.5 months, P = 0.021), while squamous carcinoma relied on low NLR (21.4 vs. 16.8 months, P = 0.008), low PLR (21.8 vs. 20.1 months, P = 0.024), low ALBI (21.4 vs. 18.2 months, P = 0.047), and low LDH (24.6 vs. 19.8 months, P = 0.001). Multivariate Cox analysis identified treatment response (SD/PD), radiotherapy, NLR, and LDH as independent predictors of PFS: SD (HR = 1.959) and PD (HR = 2.763) increased progression risk by 95.9% and 176.3% (P < 0.01), respectively; radiotherapy reduced risk by 29.2% (HR = 0.708, P = 0.041); NLR ≥ 3.72 (HR = 1.638) and LDH ≥ 226.5 U/L (HR = 1.783) increased risk by 63.8% and 78.3% (P < 0.05). For OS, adenocarcinoma (HR = 0.343) and squamous carcinoma (HR = 0.312) reduced mortality risk by 65.7% and 68.8% (P < 0.05), while SD (HR = 1.715), PD (HR = 2.536), NLR ≥ 3.72 (HR = 1.629), and LDH ≥ 226.5 U/L (HR = 1.826) increased risk by 71.5%, 153.6%, 62.9%, and 82.6% (P < 0.05). Baseline NLR and LDH correlated with irAEs risk: low NLR (55.4% vs. 37.1%, P = 0.010; OR = 1.976, 95% CI: 1.098~3.557, P = 0.023) and low LDH (62.2% vs. 35.3%, P < 0.001; OR = 2.881, 95% CI: 1.590~5.223, P < 0.001) increased irAEs incidence. Conclusions: Baseline hematological markers (NLR, PLR, ALBI, LDH) are valuable predictors of efficacy and prognosis in advanced NSCLC patients undergoing immunotherapy. Their prognostic roles vary by pathological subtype: squamous carcinoma relies more on inflammatory markers, while adenocarcinoma emphasizes metabolic markers and genetic mutations. This study provides a hematological biomarker-based stratification framework for individualized immunotherapy decisions in advanced NSCLC, offering critical guidance for optimizing treatment and prognosis management. NSCLC Immunotherapy Biomarkers Inflammatory markers Predictive factors Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Lung cancer remains a major threat to global public health, with its epidemiological features showing a persistent worsening trend. According to data from the International Agency for Research on Cancer (IARC), there are over 2.2 million new lung cancer cases annually worldwide, accounting for 12.4% of malignant tumors; and 1.8 million deaths, representing 18% of cancer-related deaths [ 1 ]. Pathologically, non-small cell lung cancer (NSCLC) predominates (85%), with adenocarcinoma subtypes continuing to rise [ 2 ]. With advances in modern medical technology, early-stage lung cancer patients can achieve favorable prognosis through surgical resection combined with adjuvant therapy, with 5-year survival rates reaching 60% to 80%. However, clinical data indicate that approximately 70% of NSCLC patients are diagnosed at stage IIIB or IV [ 3 ]. Traditional chemotherapy regimens (e.g., platinum combined with pemetrexed) can partially alleviate symptoms, but the median overall survival (mOS) remains only 8–12 months, accompanied by significant toxicities such as bone marrow suppression and gastrointestinal reactions [ 4 ]. Targeted therapies (e.g., EGFR-TKIs, ALK inhibitors) have achieved breakthroughs in driver gene-positive patients, but their coverage is limited (only about 30% of NSCLC patients have actionable targets) and drug resistance issues restrict widespread application [ 5 ]. This dilemma has prompted researchers to turn their attention to immunotherapy. The advent of immune checkpoint inhibitors (ICIs) has transformed the landscape of traditional cancer treatment, introducing novel therapeutic approaches to clinical practice. Their mechanism of action focuses on relieving immunosuppressive signals in the tumor microenvironment (TME): PD-1/PD-L1 pathway inhibitors block the binding of PD-1 receptors on T cells to PD-L1 ligands on tumor cells, restoring effector T cell anti-tumor activity; while CTLA-4 inhibitors enhance activation signals during the T cell priming phase, expanding the immune response spectrum [ 6 ]. The KEYNOTE-024 study showed that in advanced NSCLC patients with high PD-L1 expression (tumor proportion score TPS ≥ 50%), pembrolizumab monotherapy significantly prolonged mOS compared to chemotherapy (30.0 vs. 14.2 months, HR = 0.63), with a nearly 50% reduction in grade 3 or higher adverse events [ 7 ]. The IMpower150 trial demonstrated that in non-squamous NSCLC, the triple regimen of atezolizumab, bevacizumab, and chemotherapy significantly improved outcomes, with an objective response rate of 64%, benefiting even patients with brain metastases [ 8 ]. Based on these breakthroughs, ICIs have been elevated from second-line to first-line standard therapy for advanced NSCLC. Nevertheless, the widespread application of immunotherapy still faces numerous challenges. Clinical data show that only about 50% of patients respond to ICIs, with long-term tumor remission achieved in only 10%-15%. Meanwhile, approximately 10%-15% of patients may experience severe immune-related adverse events (irAEs), which can lead to long-term sequelae or even life-threatening conditions. Therefore, selecting patients who can benefit from ICIs and preventing/managing potential irAEs have become key research priorities and challenges. In current clinical practice, predictive markers are mainly divided into two categories: tumor-intrinsic markers and host-related markers. PD-L1 TPS, as the most widely used marker, has been extensively applied in treatment decision-making for advanced NSCLC patients [ 9 , 10 ]. Generally, patients with higher PD-L1 TPS levels have higher response rates. However, the predictive ability of PD-L1 TPS is not absolute; some patients with high PD-L1 TPS do not benefit, while some with low or negative expression respond [ 11 , 12 ]. Additionally, tumor mutational burden (TMB) has garnered significant research interest as a potential biomarker [ 13 ]. Multiple studies indicate that high TMB may correlate with higher response rates [ 14 , 15 ]. Beyond biological markers, patient clinical features may also play important roles in efficacy prediction, such as gender [ 16 ], performance status (ECOG PS) [ 17 , 18 ], body mass index (BMI) [ 19 , 20 ], specific sites of tumor metastasis [ 16 ], and concomitant medications (e.g., antibiotics and corticosteroids [ 21 , 22 ]) [ 23 ]. In recent years, the pathophysiology of irAEs and their correlation with individual patient characteristics have become research hotspots. Some studies suggest that irAEs occurrence may correlate with higher response rates, but the specific mechanisms remain unclear, and balancing reduced irAEs risk while ensuring efficacy is a pressing clinical issue. In recent years, systemic inflammation indicators based on peripheral blood have emerged as research hotspots for prognosis assessment due to their real-time, dynamic, and non-invasive advantages. Particularly in NSCLC patients, inflammatory markers in the blood are thought to be closely related to the formation of immunosuppressive microenvironments [ 12 ]. Studies show that elevated neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) often predict shorter progression-free survival (PFS) and overall survival (OS) [ 24 – 26 ]. A study involving 187 patients found that NLR < 5 was associated with improved PFS and OS, and PLR < 200 with PFS, OS, ORR, and DCR [ 27 ], suggesting NLR and PLR as potential predictive markers for ICIs efficacy. Lactate dehydrogenase (LDH) and albumin-bilirubin index (ALBI) scores are also considered important prognostic factors. LDH, as a key marker of tumor cell metabolism, has been shown to correlate with cancer malignancy and patient prognosis [ 28 ]. Elevated LDH (> 250 U/L) indicates hypoxic microenvironment and active tumor metabolism, associated with primary resistance to ICIs (ORR reduced by 62%) [ 29 ]. Kinoshita's team found that preoperative ALBI grade was significantly associated with postoperative recurrence in NSCLC, with ALBI grades 2/3 as potential independent prognostic factors for disease-free survival (DFS) and OS [ 30 ], suggesting it as a novel predictive tool for immunotherapy. Given the above research background, this study employs retrospective analysis to systematically evaluate clinical data from advanced NSCLC patients receiving ICIs, focusing on the clinical value of NLR, PLR, LDH, and ALBI in prognosis assessment. Subgroup analyses by pathological type (adenocarcinoma and squamous carcinoma) were conducted to provide evidence-based support for selecting immunotherapy beneficiaries and optimizing treatment decisions, while offering new insights for early irAEs warning. 2. Materials and methods 2.1 General Information This retrospective study collected data from 198 NSCLC patients treated with ICIs at the Tumor Centers of Renmin Hospital of Wuhan University and Macheng People's Hospital between January 2021 and December 2023. The study was approved by the Clinical Research Ethics Committee of Renmin Hospital of Wuhan University (Ethics Approval No.: WDRY2020F048). Inclusion Criteria: (1) Pathologically or histologically confirmed non-small cell lung cancer (NSCLC); (2) Advanced lung malignancy (ineligible for radical surgery or radical radiotherapy); (3) ECOG PS score: 0–2; (4) Received at least 2 cycles of immune checkpoint inhibitor therapy; (5) Complete baseline data: enhanced CT/PET-CT within 30 days before treatment, pathological subtype and staging evidence, blood test indicators, etc. Exclusion Criteria: (1) History of other malignancies within 5 years (excluding non-melanoma skin cancer/cervical carcinoma in situ); (2) Presence of any of the following: active autoimmune disease (requiring immunosuppressants, e.g., systemic lupus erythematosus, myasthenia gravis); uncontrolled systemic infection (CRP > 3 times normal upper limit with fever); interstitial lung disease (CT-confirmed pulmonary interstitial fibrosis > 20%); (3) Patients with fewer than two treatment cycles; (4) Patients lacking complete clinical data. 2.2 Data Collection Data collection encompassed outpatient and inpatient clinical information, including: (1) Demographic and baseline characteristics: age, gender, smoking history, comorbidities, etc.; (2) Tumor characteristics: histological subtype (adenocarcinoma/squamous carcinoma/SCLC/other), molecular typing (PD-L1 TPS detection platform and antibody, TMB value, driver gene mutations), tumor TNM staging, primary lesion maximum diameter (pre-treatment CT measurement), metastatic burden, etc.; (3) Treatment parameters: immunotherapy regimen (drug name and dose, line of therapy, combination regimen, cycles); (4) Laboratory indicators: complete blood count (absolute neutrophil count (ANC), absolute lymphocyte count (ALC), platelet count (PLT), absolute monocyte count (AMC), etc.), liver function (alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), total bilirubin (TBIL), and albumin-bilirubin index (ALBI)), inflammatory markers (C-reactive protein, lactate dehydrogenase (LDH), ferritin), renal function (creatinine, estimated glomerular filtration rate (eGFR)); (5) Imaging and efficacy assessment (6) Safety data: immune-related adverse reactions, treatment interruption/termination records; (7) Survival follow-up: Primary endpoints: PFS: from treatment initiation to radiological progression/death; OS: from treatment initiation to all-cause death. 2.3 Treatment Regimens This study selected patients receiving systemic ICIs therapy as subjects, dividing them into four subgroups based on regimens: ICIs monotherapy, ICIs combined with chemotherapy, ICIs combined with targeted therapy, and ICIs combined with chemotherapy and targeted therapy. Regimen selection was based on individualized clinical features and the attending physician's professional judgment. 2.4 Efficacy Evaluation Methods According to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1), each patient underwent chest enhanced CT every 8–12 weeks for efficacy assessment. Data follow-up cutoff was December 1, 2024. This study employed the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) for systematic evaluation. All patients underwent baseline chest enhanced CT (slice thickness ≤ 2 mm, contrast agent iohexol 100 mL) to identify target lesions (up to 5, ≤ 2 per organ), with imaging re-evaluated every 8–12 weeks during treatment. Follow-up endpoint was set as December 1, 2024. Cases lost to follow-up at the last visit were treated as censored data in statistical analysis. Efficacy was assessed according to international standards, categorized into four levels: (1) Complete Response (CR): Imaging shows complete disappearance of all target lesions, complete regression of non-target lesions, normalization of tumor markers, and no new lesions; (2) Partial Response (PR): Sum of target lesion diameters reduced by ≥ 30% from baseline, with no progression in non-target lesions; (3) Stable Disease (SD): Changes in lesion diameters do not meet PR criteria but also do not meet progression criteria; (4) Progressive Disease (PD): Sum of target lesion diameters increased by ≥ 20%, or clear progression in non-target lesions, or new lesions detected. All efficacy determinations were objectively based on imaging and laboratory results. 2.5 Statistical Methods This study used SPSS 27.0 and R 4.3.1 for data analysis, with GraphPad Prism 10 for visualization. Normally distributed continuous variables are presented as mean ± standard deviation ( \(\:\stackrel{-}{\text{x}}\) ±s), and categorical variables as frequencies (percentages). Inter-group differences were compared using chi-square tests or Fisher's exact test (when expected frequency < 5). ROC curve analysis determined optimal cut-off values for peripheral blood markers and calculated corresponding area under the curve (AUC) and 95% confidence intervals. For survival analysis, Kaplan-Meier method was used to plot PFS and OS curves, with log-rank tests for inter-group comparisons. Cox proportional hazards regression model was employed for multivariate prognostic factor assessment. All statistical analyses in this study used two-sided tests, with P < 0.05 considered statistically significant. 3. Results 3.1 Patient Baseline Characteristics This study included a total of 198 patients with advanced NSCLC from the Oncology Centers of Renmin Hospital of Wuhan University and Macheng People's Hospital (Table 1 ). Among them, male patients accounted for 78.28%, and female patients for 21.72%. Patients under 65 years old comprised 53.03%, and those with ECOG PS scores of 0–1 accounted for 85.35%. In terms of clinical staging, stage IV patients accounted for 70.20%, with adenocarcinoma being the predominant pathological type (55.05%), followed by squamous cell carcinoma (41.92%). Regarding treatment, most patients received immunotherapy combined with chemotherapy (69.19%), with tislelizumab, camrelizumab, and sintilimab being commonly used immunotherapy drugs (accounting for 30.30%, 25.76%, and 24.24%, respectively), and 55.56% of patients received radiotherapy. Gene mutation-positive patients accounted for only 18.18%, and PD-L1-positive patients for 22.22%. In terms of hematological immune response markers, patients with NLR < 3.72 accounted for 51.01%, PLR < 207.43 for 52.02%, ALBI score <-2.68 for 59.60%, and LDH < 226.50 for 41.41%. As of December 1, 2024, 176 patients had died during follow-up, with a median follow-up time of 28.1 months, median PFS of 7.7 months (95% CI = 6.9 ~ 8.5 months), and median OS of 20.1 months (95% CI = 18.2 ~ 21.9 months) (Fig. 1 ). Table 1 Patient Baseline Characteristics Characteristic Cases (%) Sex Male 155(78.28) Female 43(21.72) Age ≥ 65 93(46.97) <65 105(53.03) Pathological Type Adenocarcinoma 109(55.05) Squamous Carcinoma 83(41.92) Other 6(3.03) PS Score 0 ~ 1 169(85.35) 2 29(14.65) Clinical Stage Ⅲ 59(29.80) Ⅳ 139(70.20) Efficacy Evaluation PR 51(25.76) SD 111(56.06) PD 36(18.18) PD-L1 Expression Negative 154(77.78) Positive 44(22.22) Gene Mutation Absent 162(81.82) Present 36(18.18) Line of Therapy 1 78(39.39) 2 73(36.87) 3 27(13.64) ≥ 4 20(10.10) Immunotherapy Drug Camrelizumab 51(25.76) Pembrolizumab 18(9.09) Tislelizumab 60(30.30) Sintilimab 48(24.24) Other 21(10.61) Treatment Regimen Immunotherapy 34(17.17) Immuno + Chemo 137(69.19) Immuno + Targeted 6(3.03) Immuno + Chemo + Targeted 21(10.61) Radiotherapy Present 110(55.56) Absent 88(44.44) Brain Metastasis Absent 151(76.26) Present 47(23.74) Bone Metastasis Absent 133(67.17) Present 65(32.83) NLR <3.72 101(51.01) ≥ 3.72 97(48.99) PLR <207.43 103(52.02) ≥ 207.43 95(47.98) ALBI <-2.68 118(59.60) ≥-2.68 80(40.40) LDH <226.50 82(41.41) ≥ 226.50 116(58.59) 3.2 ROC Curve Analysis This study performed ROC curve analysis on the overall survival of patients (Fig. 2 ) to evaluate the discriminative ability of NLR, PLR, ALBI, and LDH in predicting mortality risk. The analysis results showed that the areas under the curve (AUC) for NLR, PLR, ALBI, and LDH were 0.804, 0.694, 0.684, and 0.726, respectively. The optimal cut-off values calculated using the Youden index were: NLR 3.72, PLR 207.43, ALBI − 2.68, LDH 226.5 U/L. Based on these cut-off values, patients were divided into low-level groups (L-group) and high-level groups (H-group), with further exploration of their correlations with clinical efficacy and survival prognosis. 3.3 Analysis of Factors Influencing Short-Term Efficacy By the end of follow-up, the best treatment response assessments for the 198 included patients showed: PR in 51 cases (25.8%), SD in 111 cases (56.1%), PD in 36 cases (18.2%). Accordingly, the objective response rate (ORR) for the entire cohort was 25.8%, and the disease control rate (DCR) was 81.9%. For efficacy analysis, patients were divided into two subgroups: tumor response group (PR patients, n = 51) and non-response group (SD + PD patients, n = 147). 3.3.1 Analysis of Short-Term Efficacy Factors in the Full Cohort This study included 198 patients, of which 51 (25.76%) achieved PR (Table 2 ). Univariate analysis showed that PD-L1 positive expression (40.91% vs. 21.43%, χ²=6.791, P = 0.009), absence of bone metastasis (30.08% vs. 16.92%, χ²=3.949, P = 0.047), PLR < 207.43 (32.04% vs. 18.95%, χ²=4.429, P = 0.035), and ALBI <-2.68 (31.36% vs. 17.50%, χ²=4.787, P = 0.029) were significantly associated with higher PR rates. There were also significant differences in PR rates among different immunotherapy drugs (χ²=12.862, P = 0.012), with pembrolizumab having the highest PR rate (50.00%) and camrelizumab the lowest (13.73%). Table 2 Correlation Analysis Between Clinical Characteristics and Short-Term Efficacy in the Full Cohort Characteristic Total Tumor Response Group n(%) Tumor Non-Response Group n(%) x2 P Value OR (95%CI) P Value Sex Male 155 42(27.10) 113(72.90) 0.669 0.413 Female 43 9(20.93) 34(79.07) Age <65 105 28(26.67) 77(73.33) 0.097 0.756 ≥ 65 93 23(24.73) 70(75.27) Pathological Type Adenocarcinoma 109 24(22.02) 85(77.98) 2.402 0.301 Squamous Carcinoma 83 26(31.33) 57(68.67) Other 6 1(16.67) 5(83.33) PS Score 0 ~ 1 169 47(27.81) 122(72.19) 2.757 0.252 2 29 4(13.79) 25(86.21) Clinical Stage Ⅲ 59 20(33.90) 39(66.10) 2.913 0.088 0.954(0.425 ~ 2.139) 0.909 Ⅳ 139 31(22.30) 108(77.70) PD-L1 Expression Negative 154 33(21.43) 121(78.57) 6.791 0.009 0.361(0.161 ~ 0.808) 0.013 Positive 44 18(40.91) 26(59.09) Gene Mutation Absent 162 38(23.46) 124(76.54) 2.466 0.116 Present 36 13(36.11) 23(63.89) Line of Therapy 1 78 18(23.08) 60(76.92) 4.621 0.099 2 73 25(34.25) 48(65.75) 0.558(0.248 ~ 1.256) 0.159 3 27 4(14.81) 23(85.19) 1.549(0.396 ~ 6.068) 0.529 ≥ 4 20 4(20.00) 16(80.00) 0.802(0.193 ~ 3.332) 0.762 Immunotherapy Drug Other 21 8(38.10) 13(61.90) 12.862 0.012 Camrelizumab 51 7(13.73) 44(86.27) 0.157(0.042 ~ 0.59) 0.006 Pembrolizumab 18 9(50.00) 9(50.00) 0.311(0.109 ~ 0.885) 0.029 Tislelizumab 60 18(30.00) 42(70.00) 0.681(0.212 ~ 2.181) 0.517 Sintilimab 48 9(18.75) 39(81.25) 0.258(0.07 ~ 0.952) 0.042 Treatment Regimen Immunotherapy 34 9(26.47) 25(73.53) 2.197 0.533 Immuno + Chemo 137 36(26.28) 101(73.72) Immuno + Targeted 6 0(0.00) 6(100.00) Immuno + Chemo + Targeted 21 6(28.57) 15(71.43) Radiotherapy Present 110 33(30.00) 77(70.00) 2.329 0.127 Absent 88 18(20.45) 70(79.55) Brain Metastasis Absent 151 41(27.15) 110(72.85) 0.637 0.421 Present 47 10(21.28) 37(78.72) Bone Metastasis Absent 133 40(30.08) 93(69.92) 3.949 0.047 1.955(0.791 ~ 4.831) 0.146 Present 65 11(16.92) 54(83.08) NLR <3.72 101 30(29.70) 71(70.30) 1.678 0.195 ≥ 3.72 97 21(21.65) 76(78.35) PLR <207.43 103 33(32.04) 70(67.96) 4.429 0.035 1.620(0.767 ~ 3.418) 0.206 ≥ 207.43 95 18(18.95) 77(81.05) ALBI <-2.68 118 37(31.36) 81(68.64) 4.787 0.029 2.524(1.148 ~ 5.552) 0.021 ≥-2.68 80 14(17.50) 66(82.50) LDH <226.50 82 24(29.27) 58(70.73) 0.902 0.342 ≥ 226.50 116 27(23.28) 89(76.72) Multivariate logistic regression analysis further confirmed that PD-L1 positive expression (OR = 0.361, 95% CI: 0.161 ~ 0.808, P = 0.013) and ALBI <-2.68 (OR = 2.524, 95% CI: 1.148 ~ 5.552, P = 0.021) were independent predictors of tumor response. In the subgroup analysis of immunotherapy drugs, compared to the "other" category, camrelizumab (OR = 0.157, 95% CI: 0.042 ~ 0.590, P = 0.006), pembrolizumab (OR = 0.311, 95% CI: 0.109 ~ 0.885, P = 0.029), and sintilimab (OR = 0.258, 95% CI: 0.070 ~ 0.952, P = 0.042) showed statistically significant differences in efficacy. Notably, bone metastasis status and PLR, which were significant in univariate analysis, may have been confounded by other variables in multivariate analysis and did not retain statistical significance (P = 0.146 and P = 0.206, respectively). 3.3.2 Analysis of Short-Term Efficacy Factors in Adenocarcinoma Patients This study analyzed 109 adenocarcinoma patients (Table 3 ) and found that the line of therapy and type of immunotherapy drug were significantly associated with short-term efficacy (P < 0.05). Patients receiving second-line therapy had the highest response rate (30.77%), while no patients on third-line therapy achieved response. The pembrolizumab group had a response rate of 50.00%, which was significantly superior to the sintilimab group (12.00%). Multivariate analysis indicated a trend toward poorer efficacy with sintilimab (OR = 5.518, P = 0.064). Additionally, PD-L1-positive patients had higher response rates compared to the negative group (32.14% vs. 18.52%), and patients without bone metastasis had higher response rates (27.69% vs. 13.64%), but these differences did not reach statistical significance (P > 0.05). Table 3 Correlation Analysis Between Clinical Characteristics and Short-Term Efficacy in Adenocarcinoma Patients Characteristic Total Tumor Response Group n(%) Tumor Non-Response Group n(%) x2 P Value OR (95%CI) P Value Sex Male 75 18(24.00) 57(76.00) 0.550 0.458 Female 34 6(17.65) 28(82.35) Age <65 81 15(18.52) 66(81.48) 2.249 0.134 ≥ 65 28 9(32.14) 19(67.86) ECOG Score 0 ~ 1 91 20(21.98) 71(78.02) 0.001 0.982 2 18 4(22.22) 14(77.78) Clinical Stage Ⅲ 20 6(30.00) 14(70.00) 0.909 0.340 Ⅳ 89 18(20.22) 71(79.78) PD-L1 Expression Negative 81 15(18.52) 66(81.48) 2.249 0.134 Positive 28 9(32.14) 19(67.86) Gene Mutation Absent 55 10(18.18) 45(81.82) 0.952 0.329 Present 54 14(25.93) 40(74.07) Line of Therapy 1 36 8(22.22) 28(77.78) 6.266 0.044 2 39 12(30.77) 27(69.23) 0.971(0.309 ~ 3.052) 0.96 3 16 0(0.00) 16(100.00) - 0.998 ≥ 4 18 4(22.22) 14(77.78) 1.061(0.252 ~ 4.464) 0.936 Immunotherapy Drug Other 9 4(44.44) 5(55.56) 9.862 0.043 Camrelizumab 40 6(15.00) 34(85.00) 3.439(0.7 ~ 16.895) 0.128 Pembrolizumab 10 5(50.00) 5(50.00) 0.802(0.127 ~ 5.069) 0.814 Tislelizumab 25 6(24.00) 19(76.00) 1.608(0.308 ~ 8.383) 0.573 Sintilimab 25 3(12.00) 22(88.00) 5.518(0.908 ~ 33.515) 0.064 Treatment Regimen Immunotherapy 24 8(33.33) 16(66.67) 3.185 0.364 Immuno + Chemo 62 12(19.35) 50(80.65) Immuno + Targeted 4 0(0.00) 4(100.00) Immuno + Chemo + Targeted 19 4(21.05) 15(78.95) Radiotherapy Absent 44 7(15.91) 37(84.09) 1.604 0.205 Present 65 17(26.15) 48(73.85) Brain Metastasis Absent 74 18(24.32) 56(75.68) 0.714 0.398 Present 35 6(17.14) 29(82.86) Bone Metastasis Absent 65 18(27.69) 47(72.31) 3.019 0.082 Present 44 6(13.64) 38(86.36) NLR <3.72 55 9(16.36) 46(83.64) 2.067 0.150 ≥ 3.72 54 15(27.78) 39(72.22) PLR <207.43 62 13(20.97) 49(79.03) 0.092 0.761 ≥ 207.43 47 11(23.40) 36(76.60) ALBI <-2.68 67 16(23.88) 51(76.12) 0.351 0.553 ≥-2.68 42 8(19.05) 34(80.95) LDH <226.50 43 12(27.91) 31(72.09) 1.434 0.231 ≥ 226.50 66 12(18.18) 54(81.82) 3.3.3 Analysis of Short-Term Efficacy Factors in Squamous Cell Carcinoma Patients This study analyzed 83 squamous cell carcinoma patients (Table 4 ) and found that ECOG PS score (χ²=5.784, P = 0.016), PD-L1 expression (χ²=4.122, P = 0.042), NLR (χ²=8.690, P = 0.003), PLR (χ²=10.345, P = 0.001), and ALBI score (χ²=6.350, P = 0.012) were significantly associated with short-term efficacy. Specifically, patients with PS scores of 0–1 had significantly higher response rates than those with PS score of 2 (36.11% vs. 0%), and PD-L1-positive patients had higher response rates than the negative group (53.33% vs. 26.47%). Hematological indicator analysis showed that patients with NLR < 3.72 (45.45% vs. 15.38%), PLR < 207.43 (48.72% vs. 15.91%), and ALBI<-2.68 (42.55% vs. 16.67%) had significantly better response rates. Multivariate analysis indicated that receiving radiotherapy was an independent protective factor (OR = 0.298, 95% CI: 0.094 ~ 0.95, P = 0.041), while other indicators did not retain statistical significance after multivariate adjustment. Table 4 Correlation Analysis Between Clinical Characteristics and Short-Term Efficacy in Squamous Cell Carcinoma Patients Characteristic Total Tumor Response Group n(%) Tumor Non-Response Group n(%) x2 P Value OR (95%CI) P Value Sex Male 74 23(31.08) 51(68.92) 0.019 0.891 Female 9 3(33.33) 6(66.67) Age <65 45 17(37.78) 28(62.22) 1.902 0.168 ≥ 65 38 9(23.68) 29(76.32) PS Score 0 ~ 1 72 26(36.11) 46(63.89) 5.784 0.016 2 11 0(0.00) 11(100.00) Clinical Stage Ⅲ 37 13(35.14) 24(64.86) 0.45 0.502 Ⅳ 46 13(28.26) 33(71.74) PD-L1 Expression Negative 68 18(26.47) 50(73.53) 4.122 0.042 0.366(0.093 ~ 1.44) 0.150 Positive 15 8(53.33) 7(46.67) Gene Mutation Absent 75 22(29.33) 53(70.67) 1.435 0.231 Present 8 4(50.00) 4(50.00) Line of Therapy 1 40 10(25.00) 30(75.00) 2.792 0.425 2 31 12(38.71) 19(61.29) 3 10 4(40.00) 6(60.00) ≥ 4 2 0(0.00) 2(100.00) Immunotherapy Drug Other 12 4(33.33) 8(66.67) 3.062 0.548 Camrelizumab 9 1(11.11) 8(88.89) Pembrolizumab 8 4(50.00) 4(50.00) Tislelizumab 34 11(32.35) 23(67.65) Sintilimab 20 6(30.00) 14(70.00) Treatment Regimen Immunotherapy 9 1(11.11) 8(88.89) 7.083 0.069 Immuno + Chemo 70 23(32.86) 47(67.14) 0.27(0.025 ~ 2.861) 0.277 Immuno + Targeted 2 0(0.00) 2(100.00) Immuno + Chemo + Targeted 2 2(100.00) 0(0.00) Radiotherapy Absent 43 10(23.26) 33(76.74) 2.701 0.1 0.298(0.094 ~ 0.95) 0.041 Present 40 16(40.00) 24(60.00) Brain Metastasis Absent 72 22(30.56) 50(69.44) 0.15 0.699 Present 11 4(36.36) 7(63.64) Bone Metastasis Absent 66 22(33.33) 44(66.67) 0.604 0.437 Present 17 4(23.53) 13(76.47) NLR <3.72 44 20(45.45) 24(54.55) 8.69 0.003 1.859(0.367 ~ 9.423) 0.454 ≥ 3.72 39 6(15.38) 33(84.62) PLR <207.43 39 19(48.72) 20(51.28) 10.345 0.001 2.667(0.559 ~ 12.717) 0.218 ≥ 207.43 44 7(15.91) 37(84.09) ALBI <-2.68 47 20(42.55) 27(57.45) 6.350 0.012 2.723(0.84 ~ 8.83) 0.095 ≥-2.68 36 6(16.67) 30(83.33) LDH <226.50 36 12(33.33) 24(66.67) 0.119 0.730 ≥ 226.50 47 14(29.79) 33(70.21) 3.4 Relationship Between Baseline Peripheral Blood Biomarkers and PFS This study systematically evaluated the predictive value of baseline peripheral blood biomarkers on progression-free survival (PFS) in the entire cohort using Kaplan-Meier survival analysis. The results demonstrated that NLR, PLR, ALBI, and LDH all exhibited significant prognostic stratification capabilities (all P < 0.05). Specifically, the median PFS in the low NLR group (L-NLR) was 8.7 months (95% CI: 7.1 ~ 9.1), significantly superior to 7.1 months in the high NLR group (H-NLR) (P = 0.001) (Fig. 3 A); the median PFS in the low PLR group was 7.9 months (95% CI: 6.6 ~ 9.2), extending by 0.5 months compared to the high PLR group (P = 0.007) (Fig. 3 B). The median PFS in the low ALBI group was 8.1 months (95% CI: 7.1 ~ 9.1), extending by 1.1 months compared to the high ALBI group (P = 0.028) (Fig. 3 C). Notably, LDH demonstrated the strongest predictive efficacy, with the median PFS in the low LDH group reaching 9.5 months (95% CI: 6.7 ~ 12.3), reducing the risk by 46.3% compared to the high LDH group (7.1 months, 95% CI: 6.5 ~ 7.8) (P < 0.001) (Fig. 3 D). In adenocarcinoma patients, the predictive patterns of peripheral blood biomarkers exhibited significant heterogeneity. LDH maintained independent prognostic value, with the median PFS in the low LDH group being 9.5 months (95% CI: 6.7 ~ 12.3), extending by 2.7 months compared to the high LDH group (P = 0.032) ( Fig. 3 H). However, the predictive efficacy of systemic inflammatory biomarkers was generally diminished: although the low NLR group showed a trend toward clinical benefit (8.1 vs. 6.5 months, P = 0.051), it did not reach statistical significance (Fig. 3 E); PLR and ALBI completely lost stratification capability (both P > 0.05) (Figs. 3 F-G). The predictive patterns of peripheral blood biomarkers in squamous cell carcinoma patients were highly consistent with the entire cohort and demonstrated stronger prognostic associations. The median PFS in the low NLR group reached 9.1 months (95% CI: 7.0 ~ 11.2), extending by 1.9 months compared to the high NLR group (P = 0.002) (Fig. 3 I); the prognostic advantage in the low PLR group was more pronounced (10.2 vs. 7.4 months, P = 0.002) (Fig. 3 J). Additionally, ALBI exhibited unique predictive value in squamous cell carcinoma, with the median PFS in the low score group being 8.7 months (95% CI: 6.5 ~ 10.9), significantly superior to the high score group (7.1 months, P = 0.022) (Fig. 3 K). LDH remained the strongest predictor, with the median PFS in the low LDH group extending by 1.6 months compared to the high LDH group (9.1 vs. 7.5 months, P = 0.002) (Fig. 3 L). 3.5 Relationship Between Baseline Peripheral Blood Biomarkers and OS This study systematically evaluated the predictive value of baseline peripheral blood biomarkers on overall survival (OS) in the entire cohort using Kaplan-Meier survival analysis. NLR demonstrated prognostic stratification capability, with the median OS in the low NLR group being 21.4 months (95% CI: 20.1 ~ 22.6), significantly superior to 17.5 months in the high NLR group (95% CI: 14.5 ~ 20.5) (P < 0.001) (Fig. 4 A). PLR also exhibited significant predictive value, with the median OS in the low PLR group reaching 20.4 months (95% CI: 19.1 ~ 21.8), extending by 2.7 months compared to the high PLR group (P = 0.009) (Fig. 4 B). LDH showed excellent prognostic discrimination, with the median OS in the low LDH group being 21.4 months (95% CI: 19.2 ~ 23.5), significantly extended compared to the high LDH group (17.3 months, 95% CI: 14.5 ~ 20.2) (P < 0.001), reducing the risk by 42.5% (Fig. 4 D). In contrast, the predictive efficacy of ALBI was relatively weaker (P = 0.074) (Fig. 4 C). In adenocarcinoma patients, the predictive patterns of peripheral blood biomarkers presented unique characteristics. NLR maintained significant predictive value, with the median OS in the low NLR group being 21.4 months (95% CI: 19.8 ~ 22.9), extending by 4.9 months compared to the high NLR group (P = 0.013) (Fig. 4 E). LDH also demonstrated independent prognostic significance, with the median OS in the low LDH group reaching 20.1 months (95% CI: 14.8 ~ 25.4), significantly superior to the high LDH group (17.5 months, 95% CI: 14.0 ~ 21.1) (P = 0.021) (Fig. 4 H). However, PLR (P = 0.176) and ALBI (P = 0.422) did not exhibit significant stratification capabilities (Figs. 4 F-G). The predictive efficacy of peripheral blood biomarkers in squamous cell carcinoma patients was more pronounced. The median OS in the low NLR group reached 21.4 months (95% CI: 18.9 ~ 23.8), significantly extended compared to the high NLR group (P = 0.008) (Fig. 4 I). The predictive value of PLR was more evident in squamous cell carcinoma, with the median OS in the low PLR group being 21.8 months (95% CI: 19.0 ~ 24.6), extending by 1.7 months compared to the high PLR group (P = 0.024) (Fig. 4 J). ALBI also demonstrated predictive capability in squamous cell carcinoma that was not observed in adenocarcinoma, with the median OS in the low score group being 21.4 months (95% CI: 19.2 ~ 23.6) (P = 0.047) (Fig. 4 K). Particularly noteworthy is that LDH exhibited the strongest prognostic discrimination in squamous cell carcinoma, with the median OS in the low LDH group reaching as high as 24.6 months (95% CI: 11.7 ~ 37.6), extending by 4.8 months compared to the high LDH group (P = 0.001) (Fig. 4 L). 3.6 Cox Regression Analysis of Factors Associated with PFS 3.6.1 Analysis of Factors Influencing PFS in the Full Cohort In this study, univariate and multivariate Cox proportional hazards models were used to analyze the impact of different variables on PFS (Table 5 ). Univariate analysis results showed that PS score (HR = 0.595, 95% CI: 0.398 ~ 0.889, P = 0.011) was a significant predictor of PFS, with better PS scores (0 ~ 1) significantly associated with longer PFS. Additionally, the risk for SD patients in short-term efficacy was 2.232 times that of PR patients (95% CI: 1.547 ~ 3.221, P < 0.001), and the risk for PD patients was 3.5 times that of PR patients (95% CI: 2.226 ~ 5.509, P < 0.001). Line of therapy was a partially significant predictor of PFS, with third-line therapy significantly increasing the risk of disease progression or death (HR = 1.586, 95% CI: 1.003 ~ 2.507, P = 0.048), while other lines (second-line, ≥fourth-line) showed no statistical difference compared to first-line therapy. Camrelizumab significantly increased the risk of disease progression (HR = 1.891, 95% CI: 1.107 ~ 3.229, P = 0.020), while other drugs (pembrolizumab, tislelizumab, sintilimab) showed no statistical difference compared to "other" immunotherapy drugs. Patients receiving radiotherapy had a significantly reduced risk of disease progression (HR = 0.729, 95% CI: 0.545 ~ 0.975, P = 0.033). All evaluated hematological function indicators were significantly associated with patient PFS (all P < 0.05). Specifically: patients with NLR ≥ 3.72 had a significantly increased risk of disease progression (HR = 1.647, 95% CI: 1.228 ~ 2.208, P < 0.001); patients with PLR ≥ 207.43 had a 49.5% increased risk (HR = 1.495, 95% CI: 1.113 ~ 2.006, P = 0.007). Patients with ALBI index ≥-2.68 had significantly shorter PFS (HR = 1.385, 95% CI: 1.033 ~ 1.856, P = 0.029). Patients with LDH ≥ 226.50 had the highest progression risk (HR = 1.811, 95% CI: 1.336 ~ 2.456, P < 0.001), with an 81.1% increase in risk. Table 5 Univariate and Multivariate Cox Analysis of Factors Influencing PFS in the Full Cohort Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.958 1.009 (0.712 ~ 1.431) Age(<65/≥65) 0.378 0.878(0.658 ~ 1.172) Pathological Type Other 1.00 (Reference) Adenocarcinoma 0.323 0.659 (0.288 ~ 1.506) Squamous Carcinoma 0.196 0.577 (0.250 ~ 1.329) PS Score(0 ~ 1/2) 0.011 0.595(0.398 ~ 0.889) 0.270 0.781(0.503 ~ 1.212) Clinical Stage(Ⅲ/Ⅳ) 0.776 0.955(0.697 ~ 1.309) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD <0.001 2.232(1.547 ~ 3.221) 0.001 1.959(1.294 ~ 2.964) PD <0.001 3.502(2.226 ~ 5.509) <0.001 2.763(1.624 ~ 4.701) PD-L1 Expression (Present/Absent) 0.904 0.979(0.689 ~ 1.390) Gene Mutation(Present/Absent) 0.178 0.767(0.521 ~ 1.129) Line of Therapy 1 1.00 (Reference) 1.00 (Reference) 2 0.809 0.960 (0.691 ~ 1.335) 0.927 1.017(0.711 ~ 1.453) 3 0.048 1.586 (1.003 ~ 2.507) 0.499 1.207(0.699 ~ 2.083) ≥ 4 0.296 0.762(0.458 ~ 1.268) 0.017 0.479(0.261 ~ 0.878) Immunotherapy Drug Other 1.00 (Reference) 1.00 (Reference) Camrelizumab 0.020 1.891(1.107 ~ 3.229) 0.092 1.663(0.921 ~ 3.002) Pembrolizumab 0.841 1.069(0.555 ~ 2.059) 0.858 1.068(0.522 ~ 2.185) Tislelizumab 0.192 1.418(0.839 ~ 2.395) 0.725 1.105(0.632 ~ 1.932) Sintilimab 0.079 1.620(0.945 ~ 2.778) 0.375 1.292(0.734 ~ 2.273) Treatment Regimen Immunotherapy 1.00 (Reference) 1.00 (Reference) Immuno + Chemo 0.806 1.050(0.712 ~ 1.548) 0.218 1.297(0.857 ~ 1.964) Immuno + Targeted 0.065 2.303(0.950 ~ 5.579) 0.133 2.133(0.794 ~ 5.729) Immuno + Chemo + Targeted 0.926 1.027(0.587 ~ 1.797) 0.096 1.740(0.907 ~ 3.337) Radiotherapy(Present/Absent) 0.033 0.729(0.545 ~ 0.975) 0.041 0.708(0.508 ~ 0.986) Brain Metastasis(Present/Absent) 0.896 1.023(0.730 ~ 1.433) Bone Metastasis(Present/Absent) 0.196 1.222 (0.902 ~ 1.655) NLR(≥3.72/<3.72) <0.001 1.647(1.228 ~ 2.208) 0.012 1.638(1.117 ~ 2.402) PLR(≥207.43/<207.43) 0.007 1.495(1.113 ~ 2.006) 0.939 1.015(0.694 ~ 1.485) ALBI(≥-2.68/<-2.68) 0.029 1.385(1.033 ~ 1.856) 0.239 1.222(0.875 ~ 1.706) LDH(≥226.50/<226.50) <0.001 1.811(1.336 ~ 2.456) 0.001 1.783(1.275 ~ 2.494) Multivariate Cox regression analysis showed that after adjusting for other confounding factors, treatment efficacy, presence of radiotherapy, NLR, and LDH levels were independent prognostic factors influencing PFS (all P < 0.05). Efficacy assessment indicated that compared to PR patients, SD (HR = 1.959, 95% CI: 1.294 ~ 2.964, P = 0.001) and PD (HR = 2.763, 95% CI: 1.624 ~ 4.701, P < 0.001) patients had increased risks of disease progression by 95.9% and 176.3%, respectively; patients receiving radiotherapy had a significantly reduced risk of disease progression (HR = 0.708, 95% CI: 0.508 ~ 0.986, P = 0.041); patients with NLR ≥ 3.72 had a 63.8% increased progression risk (HR = 1.638, 95% CI: 1.117 ~ 2.402, P = 0.012); patients with LDH ≥ 226.50 U/L had a 78.3% increased progression risk (HR = 1.783, 95% CI: 1.275 ~ 2.494, P = 0.001). 3.6.2 Analysis of Factors Influencing PFS in Adenocarcinoma Patients This study found that disease control status, gene mutation status, and LDH levels were significantly associated with PFS (Table 6 ). Compared to patients with PR, those with SD exhibited a significantly increased risk of disease progression (HR = 2.342, 95% CI: 1.395–3.931, P = 0.001), while patients with PD showed an even higher risk (HR = 5.312, 95% CI: 2.795–10.096, P < 0.001). The presence of gene mutations may confer a PFS benefit (HR = 0.593, 95% CI: 0.370–0.948, P = 0.029), whereas patients with LDH ≥ 226.50 U/L had a significantly elevated progression risk (HR = 1.555, 95% CI: 1.035–2.334, P = 0.033). Additionally, NLR ≥ 3.72 demonstrated borderline significance (HR = 1.47, 95% CI: 0.995–2.172, P = 0.053). Table 6 Univariate and Multivariate Cox Analysis of Factors Influencing PFS in Adenocarcinoma Patients Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.855 1.041(0.676 ~ 1.603) Age(<65/≥65) 0.873 1.032(0.7 ~ 1.521) PS Score(0 ~ 1/2) 0.220 0.725(0.433 ~ 1.213) Clinical Stage(Ⅲ/Ⅳ) 0.711 1.099(0.666 ~ 1.814) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD 0.001 2.342(1.395 ~ 3.931) 0.004 2.195(1.294 ~ 3.723) PD <0.001 5.312(2.795 ~ 10.096) <0.001 4.636(2.393 ~ 8.981) PD-L1 Expression (Present/Absent) 0.333 0.795(0.5 ~ 1.265) Gene Mutation(Present/Absent) 0.029 0.593(0.37 ~ 0.948) 0.386 0.801(0.486 ~ 1.322) Line of Therapy 1 1.00 (Reference) 2 0.597 0.881(0.551 ~ 1.409) 3 0.122 1.629(0.877 ~ 3.026) ≥ 4 0.178 0.664(0.366 ~ 1.205) Immunotherapy Drug Other 1.00 (Reference) Camrelizumab 0.156 1.746(0.809 ~ 3.769) Pembrolizumab 0.909 1.056(0.415 ~ 2.682) Tislelizumab 0.571 1.264(0.562 ~ 2.84) Sintilimab 0.177 1.745(0.778 ~ 3.913) Treatment Regimen Immunotherapy 1.00 (Reference) Immuno + Chemo 0.849 1.048(0.645 ~ 1.702) Immuno + Targeted 0.092 2.535(0.858 ~ 7.486) Immuno + Chemo + Targeted 0.859 1.058(0.569 ~ 1.968) Radiotherapy(Present/Absent) 0.133 0.738(0.497 ~ 1.097) Brain Metastasis(Present/Absent) 0.769 0.94(0.62 ~ 1.423) Bone Metastasis(Present/Absent) 0.24 1.267(0.853 ~ 1.881) NLR(≥3.72/<3.72) 0.053 1.47(0.995 ~ 2.172) PLR(≥207.43/<207.43) 0.399 1.184(0.8 ~ 1.753) ALBI(≥-2.68/<-2.68) 0.269 1.251(0.841 ~ 1.86) LDH(≥226.50/<226.50) 0.033 1.555(1.035 ~ 2.334) 0.347 1.233(0.797 ~ 1.908) After adjustment using a multivariate Cox regression model, disease control status retained independent predictive value. The progression risk for SD patients was 2.195 times that of PR patients (95% CI: 1.294–3.723, P = 0.004), and for PD patients, it was as high as 4.636 times (95% CI: 2.393–8.981, P < 0.001). Notably, gene mutations (P = 0.386) and LDH levels (P = 0.347), which were significant in univariate analysis, did not maintain statistical significance in the multivariate model. 3.6.3 Analysis of Factors Influencing PFS in Squamous Cell Carcinoma Patients This study indicates that performance status (PS score), serum lactate dehydrogenase (LDH) levels, and disease control status are independent prognostic factors influencing progression-free survival (PFS) in patients with squamous cell carcinoma (Table 7 ). Univariate analysis revealed that patients with a PS score of 0–1 had a significantly lower risk of disease progression compared to those with a PS score of 2 (HR = 0.336, 95% CI: 0.169–0.67, P = 0.002); LDH ≥ 226.50 U/L (HR = 2.098, 95% CI: 1.293–3.404, P = 0.003) and stable disease status (SD, HR = 2.276, 95% CI: 1.323–3.918, P = 0.003) were significantly associated with poorer PFS. Additionally, hematological indicators including NLR ≥ 3.72 (HR = 2.083, P = 0.002), PLR ≥ 207.43 (HR = 2.115, P = 0.002), and ALBI score (≥-2.68, HR = 1.695, P = 0.024) also demonstrated statistical significance. Table 7 Univariate and Multivariate Cox Analysis of Factors Influencing PFS in Squamous Cell Carcinoma Patients Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.998 1.001(0.497 ~ 2.016) Age(<65/≥65) 0.058 0.645(0.409 ~ 1.015) PS Score(0 ~ 1/2) 0.002 0.336(0.169 ~ 0.670) 0.042 0.464(0.221 ~ 0.971) Clinical Stage(Ⅲ/Ⅳ) 0.584 0.881(0.561 ~ 1.384) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD 0.003 2.276(1.323 ~ 3.918) 0.045 1.856(1.015 ~ 3.392) PD 0.015 2.345(1.182 ~ 4.650) 0.071 1.912(0.947 ~ 3.861) PD-L1 Expression (Present/Absent) 0.312 0.743(0.417 ~ 1.322) Gene Mutation(Present/Absent) 0.387 1.387(0.661 ~ 2.909) Line of Therapy 1 1.00 (Reference) 2 0.635 0.889(0.547 ~ 1.446) 3 0.484 1.300(0.624 ~ 2.709) ≥ 4 0.783 0.818(0.196 ~ 3.415) Immunotherapy Drug Other 1.00 (Reference) Camrelizumab 0.157 1.912(0.780 ~ 4.688) Pembrolizumab 0.852 1.095(0.424 ~ 2.828) Tislelizumab 0.227 1.533(0.767 ~ 3.062) Sintilimab 0.278 1.515(0.715 ~ 3.210) Treatment Regimen Immunotherapy 1.00 (Reference) Immuno + Chemo 0.917 0.961(0.455 ~ 2.031) Immuno + Targeted 0.477 1.770(0.367 ~ 8.524) Immuno + Chemo + Targeted 0.6 0.659(0.139 ~ 3.134) Radiotherapy(Present/Absent) 0.105 0.687(0.436 ~ 1.081) Brain Metastasis(Present/Absent) 0.845 1.066(0.560 ~ 2.031) Bone Metastasis(Present/Absent) 0.886 1.043(0.589 ~ 1.846) NLR(≥3.72/<3.72) 0.002 2.083(1.301 ~ 3.337) 0.695 1.14(0.592 ~ 2.193) PLR(≥207.43/<207.43) 0.002 2.115(1.303 ~ 3.434) 0.468 1.293(0.647 ~ 2.585) ALBI(≥-2.68/<-2.68) 0.024 1.695(1.071 ~ 2.684) 0.228 1.36(0.825 ~ 2.242) LDH(≥226.50/<226.50) 0.003 2.098(1.293 ~ 3.404) 0.017 1.909(1.121 ~ 3.252) After adjustment using a multivariate Cox proportional hazards model, PS score (HR = 0.464, 95% CI: 0.221–0.971, P = 0.042), LDH levels (HR = 1.909, 95% CI: 1.121–3.252, P = 0.017), and SD status (HR = 1.856, 95% CI: 1.015–3.392, P = 0.045) retained independent predictive value, whereas the significance of other hematological indicators was lost. 3.7 Cox Regression Analysis of Factors Associated with OS 3.7.1 Analysis of Factors Influencing OS in the Full Cohort In this study, we employed univariate and multivariate Cox proportional hazards models to analyze the impact of various variables on overall survival (OS) (Table 8 ). Univariate analysis revealed that histological type (with other types as the reference): patients with squamous cell carcinoma exhibited a significant 62.6% reduction in mortality risk (HR = 0.374, 95% CI: 0.161–0.867, P = 0.022), while those with adenocarcinoma showed a borderline trend toward reduced risk (HR = 0.468, 95% CI: 0.204–1.073, P = 0.073), without achieving statistical significance. Patients with a PS score of 0–1 had a 47.7% lower mortality risk compared to those with a PS score of 2 (HR = 0.523, 95% CI: 0.349–0.783, P = 0.002). Regarding short-term treatment efficacy, significant differences in OS were observed among patients with different responses (with PR as the reference group); both SD and PD patients demonstrated significantly elevated mortality risks (SD: HR = 2.046, 95% CI: 1.392–3.006, P < 0.001; PD: HR = 2.998, 95% CI: 1.870–4.806, P < 0.001). Patients with bone metastases exhibited poorer OS (HR = 1.376, 95% CI: 1.004–1.884, P = 0.047). Inflammation-related indicators and liver function metabolic markers were significantly associated with patient OS: patients with NLR ≥ 3.72 had a 76.8% increased mortality risk (HR = 1.768, 95% CI: 1.308–2.388, P < 0.001), those with PLR ≥ 207.43 had a 48.4% increased risk (HR = 1.484, 95% CI: 1.100-2.000, P = 0.010); meanwhile, patients with LDH ≥ 226.50 U/L showed a significant 89.9% increase in mortality risk (HR = 1.899, 95% CI: 1.388–2.599, P < 0.001), whereas those with ALBI ≥-2.68 displayed a trend toward increased risk (HR = 1.314, 95% CI: 0.972–1.775) that did not reach statistical significance (P = 0.076). Table 8 Univariate and Multivariate Cox Analysis of Factors Influencing OS in the Full Cohort Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.277 1.227(0.849 ~ 1.772) Age(<65/≥65) 0.095 0.777(0.578 ~ 1.045) Pathological Type Other 1.00 (Reference) 1.00 (Reference) Adenocarcinoma 0.073 0.468(0.204 ~ 1.073) 0.022 0.343(0.137 ~ 0.858) Squamous Carcinoma 0.022 0.374(0.161 ~ 0.867) 0.012 0.312(0.125 ~ 0.778) PS Score(0 ~ 1/2) 0.002 0.523(0.349 ~ 0.783) 0.024 0.607(0.394 ~ 0.935) Clinical Stage(Ⅲ/Ⅳ) 0.945 0.989(0.716 ~ 1.365) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD <0.001 2.046(1.392 ~ 3.006) 0.014 1.715(1.113 ~ 2.643) PD <0.001 2.998(1.870 ~ 4.806) 0.001 2.536(1.498 ~ 4.293) PD-L1 Expression (Present/Absent) 0.718 0.935(0.650 ~ 1.346) Gene Mutation(Present/Absent) 0.080 0.692(0.459 ~ 1.045) 0.874 1.037(0.661 ~ 1.628) Line of Therapy 1 1.00 (Reference) 2 0.437 0.873(0.620 ~ 1.229) 3 0.741 1.081(0.679 ~ 1.721) ≥ 4 0.495 0.833(0.494 ~ 1.406) Immunotherapy Drug Other 1.00 (Reference) 1.00 (Reference) Camrelizumab 0.073 1.647(0.954 ~ 2.842) 0.507 1.23(0.667 ~ 2.27) Pembrolizumab 0.960 0.982(0.493 ~ 1.957) 0.759 1.12(0.542 ~ 2.313) Tislelizumab 0.606 1.152(0.672 ~ 1.975) 0.925 0.974(0.556 ~ 1.705) Sintilimab 0.138 1.518(0.874 ~ 2.636) 0.945 1.021(0.565 ~ 1.847) Treatment Regimen Immunotherapy 1.00 (Reference) Immuno + Chemo 0.450 0.858(0.578 ~ 1.275) Immuno + Targeted 0.129 1.983(0.819 ~ 4.800) Immuno + Chemo + Targeted 0.561 0.843(0.473 ~ 1.501) Radiotherapy(Present/Absent) 0.143 0.800(0.593 ~ 1.078) Brain Metastasis(Present/Absent) 0.583 1.102(0.779 ~ 1.561) Bone Metastasis(Present/Absent) 0.047 1.376(1.004 ~ 1.884) 0.395 1.167(0.818 ~ 1.664) NLR(≥3.72/<3.72) <0.001 1.768(1.308 ~ 2.388) 0.015 1.629(1.099 ~ 2.415) PLR(≥207.43/<207.43) 0.010 1.484(1.100 ~ 2.000) 0.856 0.965(0.656 ~ 1.419) ALBI(≥-2.68/<-2.68) 0.076 1.314(0.972 ~ 1.775) 0.594 1.091(0.792 ~ 1.504) LDH(≥226.50/<226.50) <0.001 1.899(1.388 ~ 2.599) 0.001 1.826(1.295 ~ 2.574) Multivariate Cox regression analysis indicated that, after adjusting for other confounding factors, lung cancer histological type, PS score, treatment efficacy, NLR, and LDH levels remained independent prognostic factors for OS (all P < 0.05). Compared to other types, patients with adenocarcinoma (HR = 0.343, 95% CI: 0.137–0.858, P = 0.022) and squamous cell carcinoma (HR = 0.312, 95% CI: 0.125–0.778, P = 0.012) had significantly reduced mortality risks by 65.7% and 68.8%, respectively; PS score (HR = 0.607, 95% CI: 0.394–0.935, P = 0.024) was an important predictor of OS; regarding treatment efficacy, with PR as the reference, SD (HR = 1.715, 95% CI: 1.113–2.643, P = 0.014) and PD (HR = 2.536, 95% CI: 1.498–4.293, P = 0.001) patients had increased mortality risks by 71.5% and 153.6%, respectively; NLR ≥ 3.72 (HR = 1.629, 95% CI: 1.099–2.415, P = 0.015) and LDH ≥ 226.50 U/L (HR = 1.826, 95% CI: 1.295–2.574, P = 0.001) were associated with 62.9% and 82.6% increases in mortality risk, respectively. However, PLR (P = 0.856) and ALBI (P = 0.594) did not demonstrate statistical significance in the multivariate analysis. 3.7.2 Analysis of Factors Influencing OS in Adenocarcinoma Patients In the adenocarcinoma patient cohort, univariate Cox regression analysis revealed that multiple clinical features were significantly associated with overall survival (OS) (Table 9 ). Disease control status demonstrated a strong association, with SD patients exhibiting an 86% increased mortality risk compared to PR patients (HR = 1.860, 95% CI: 1.073–3.073, P = 0.026), and PD patients showing an even higher risk (HR = 3.595, 95% CI: 1.907–6.776, P < 0.001). Regarding hematological indicators, both NLR ≥ 3.72 (HR = 1.651, 95% CI: 1.107–2.461, P = 0.014) and LDH ≥ 226.50 U/L (HR = 1.632, 95% CI: 1.073–2.482, P = 0.022) exhibited statistical significance. Additionally, bone metastasis status displayed borderline significance (HR = 1.447, 95% CI: 0.964–2.171, P = 0.075). Table 9 Univariate and Multivariate Cox Analysis of Factors Influencing OS in Adenocarcinoma Patients Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.236 1.306(0.840 ~ 2.030) Age(<65/≥65) 0.814 0.954 (0.642 ~ 1.416) PS Score(0 ~ 1/2) 0.115 0.661(0.395 ~ 1.107) Clinical Stage(Ⅲ/Ⅳ) 0.555 1.163(0.704 ~ 1.922) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD 0.026 1.860(1.073 ~ 3.073) 0.022 1.916(1.098 ~ 3.342) PD <0.001 3.595(1.907 ~ 6.776) <0.001 3.410(1.801 ~ 6.457) PD-L1 Expression (Present/Absent) 0.138 0.696(0.431 ~ 1.124) Gene Mutation(Present/Absent) 0.101 0.671(0.417 ~ 1.081) Line of Therapy 1 1.00 (Reference) 2 0.727 0.920(0.575 ~ 1.472) 3 0.699 0.884(0.473 ~ 1.652) ≥ 4 0.262 0.705(0.382 ~ 1.299) Immunotherapy Drug Other 1.00 (Reference) Camrelizumab 0.398 1.395(0.644 ~ 3.021) Pembrolizumab 0.960 1.025(0.394 ~ 2.667) Tislelizumab 0.911 1.048(0.464 ~ 2.365) Sintilimab 0.173 1.762(0.780 ~ 3.979) Treatment Regimen Immunotherapy 1.00 (Reference) Immuno + Chemo 0.435 0.823(0.505 ~ 1.341) Immuno + Targeted 0.463 1.495(0.511 ~ 4.378) Immuno + Chemo + Targeted 0.630 0.856 (0.454 ~ 1.613) Radiotherapy(Present/Absent) 0.358 0.828(0.554 ~ 1.238) Brain Metastasis(Present/Absent) 0.670 1.096 (0.718 ~ 1.673) Bone Metastasis(Present/Absent) 0.075 1.447(0.964 ~ 2.171) 0.461 1.180(0.760 ~ 1.830) NLR(≥3.72/<3.72) 0.014 1.651(1.107 ~ 2.461) 0.041 1.572(1.019 ~ 2.426) PLR(≥207.43/<207.43) 0.178 1.315(0.883 ~ 1.958) ALBI(≥-2.68/<-2.68) 0.423 1.178(0.789 ~ 1.760) LDH(≥226.50/<226.50) 0.022 1.632(1.073 ~ 2.482) 0.071 1.481(0.966 ~ 2.272) Following multivariate adjustment, disease control status retained independent predictive value. The mortality risk for SD patients was 1.916 times that of PR patients (95% CI: 1.098–3.342, P = 0.022), while for PD patients, it reached 3.410 times (95% CI: 1.801–6.457, P < 0.001). NLR ≥ 3.72 remained significant (HR = 1.572, 95% CI: 1.019–2.426, P = 0.041), whereas LDH levels were reduced to borderline significance (HR = 1.481, P = 0.071). Notably, bone metastasis, which was significant in the univariate analysis, did not maintain significance in the multivariate model (P > 0.1). 3.7.3 Analysis of Factors Influencing OS in Squamous Cell Carcinoma Patients Univariate analysis in squamous cell carcinoma patients revealed distinct prognostic features (Table 10 ). Age < 65 years (HR = 0.570, 95% CI: 0.355 ~ 0.917, P = 0.020) and PS score of 0–1 (HR = 0.319, 95% CI: 0.163 ~ 0.626, P < 0.001) were associated with better OS. In terms of disease control, SD (HR = 2.421, 95% CI: 1.334 ~ 4.392, P = 0.004) and PD (HR = 2.409, 95% CI: 1.131 ~ 5.128, P = 0.023) were significantly worse than PR. Hematological indicators all showed significant associations: patients with NLR ≥ 3.72 had an 89.9% increased mortality risk (HR = 1.899, 95% CI: 1.176 ~ 3.068, P = 0.009), PLR ≥ 207.43 had a 72.9% increased risk (HR = 1.729, 95% CI: 1.068 ~ 2.796, P = 0.026), ALBI ≥-2.68 had a 62.9% increased risk (HR = 1.629, 95% CI: 1.002 ~ 2.648, P = 0.049), and LDH ≥ 226.50 U/L had a significantly increased risk of 230.1% (HR = 2.301, 95% CI: 1.393 ~ 3.801, P = 0.001). Notably, the immuno + targeted therapy regimen showed an extremely high mortality risk (HR = 4.652, 95% CI: 0.925 ~ 23.399), but did not reach statistical significance (P = 0.062). Table 10 Univariate and Multivariate Cox Analysis of Factors Influencing OS in Squamous Cell Carcinoma Patients Characteristic Univariate Analysis Multivariate Analysis P HR (95%CI) P HR (95%CI) Sex (Male/Female) 0.543 1.276(0.581 ~ 2.803) Age(<65/≥65) 0.020 0.570(0.355 ~ 0.917) 0.435 0.794(0.446 ~ 1.415) PS Score(0 ~ 1/2) <0.001 0.319(0.163 ~ 0.626) 0.248 0.632(0.290 ~ 1.376) Clinical Stage(Ⅲ/Ⅳ) 0.884 0.965(0.602 ~ 1.547) Efficacy Evaluation PR 1.00 (Reference) 1.00 (Reference) SD 0.004 2.421(1.334 ~ 4.392) 0.036 2.103(1.048 ~ 4.218) PD 0.023 2.409 (1.131 ~ 5.128) 0.023 2.650(1.14 ~ 6.159) PD-L1 Expression (Present/Absent) 0.288 1.379(0.762 ~ 2.496) Gene Mutation(Present/Absent) 0.268 0.597(0.240 ~ 1.488) Line of Therapy 1 1.00 (Reference) 2 0.258 0.737(0.434 ~ 1.251) 3 0.600 1.219(0.582 ~ 2.550) ≥ 4 0.909 1.087(0.260 ~ 4.551) Immunotherapy Drug Other 1.00 (Reference) Camrelizumab 0.115 2.118(0.833 ~ 5.388) Pembrolizumab 0.924 0.952(0.342 ~ 2.647) Tislelizumab 0.569 1.234(0.599 ~ 2.545) Sintilimab 0.535 1.279(0.588 ~ 2.784) Treatment Regimen Immunotherapy 1.00 (Reference) 1.00 (Reference) Immuno + Chemo 0.962 1.019(0.465 ~ 2.235) 0.241 1.671(0.708 ~ 3.946) Immuno + Targeted 0.062 4.652(0.925 ~ 23.399) 0.007 12.632(2.0 ~ 79.783) Immuno + Chemo + Targeted 0.349 0.349(0.045 ~ 2.987) 0.780 1.377(0.146 ~ 12.976) Radiotherapy(Present/Absent) 0.116 0.682(0.423 ~ 1.099) Brain Metastasis(Present/Absent) 0.915 1.039(0.514 ~ 2.100) Bone Metastasis(Present/Absent) 0.871 1.050(0.584 ~ 1.889) NLR(≥3.72/<3.72) 0.009 1.899(1.176 ~ 3.068) 0.424 1.335(0.657 ~ 2.714) PLR(≥207.43/<207.43) 0.026 1.729(1.068 ~ 2.796) 0.427 0.739(0.350 ~ 1.56) ALBI(≥-2.68/<-2.68) 0.049 1.629(1.002 ~ 2.648) 0.334 1.306(0.760 ~ 2.245) LDH(≥226.50/<226.50) 0.001 2.301(1.393 ~ 3.801) 0.002 2.542(1.398 ~ 4.623) The multivariate model showed that disease control status retained independent predictive value, with SD (HR = 2.103, P = 0.036) and PD (HR = 2.650, P = 0.023) both significant. LDH ≥ 226.50 U/L was an independent prognostic factor for OS (HR = 2.542, 95% CI: 1.398 ~ 4.623, P = 0.002). Surprisingly, the immuno + targeted therapy regimen's mortality risk further increased to 12.632 times (95% CI: 2 ~ 79.783, P = 0.007). However, age, PS score, and inflammatory indicators that were significant in univariate analysis did not retain independence (all P > 0.05). 3.8 Analysis of Immune-Related Adverse Events This study conducted a systematic analysis of irAEs in 198 patients with advanced NSCLC receiving immunotherapy (Table 11 ). The results showed that 92 patients (46.46%) experienced irAEs of varying degrees, with hematological toxicities being the most common adverse reaction types, including anemia (41.41%) and neutropenia (40.91%), with grade III and above severe events accounting for 10.10% and 9.09%, respectively. Non-hematological toxicities were primarily gastrointestinal reactions (nausea/vomiting 32.32%, abdominal pain/diarrhea 25.25%) and dermatological toxicities (rash 23.74%, pruritus 25.25%), but severe events (grade III and above) were rare (all ≤ 2.53%). Notably, the overall incidence of immune-related pneumonia was 7.07%, with 42.9% (6/14) being grade III and above events. In terms of endocrine toxicities, the incidence of thyroid dysfunction was 21.21%, with 16.7% (7/42) being grade III and above events. Table 11 irAEs in Advanced NSCLC Patients Receiving Immunotherapy Adverse Event Grade I-II (%) Grade III and Above (%) Total Incidence (%) Total 69(34.85) 23(11.6) 92(46.46) Nausea/Vomiting 64 (32.32) 0 (0.00) 64 (32.32) Abdominal Pain/Diarrhea 50 (25.25) 0 (0.00) 50 (25.25) Rash 47 (23.74) 0 (0.00) 47 (23.74) Pruritus 45 (22.73) 5 (2.53) 50 (25.25) Anemia 62 (31.31) 20 (10.10) 82 (41.41) Neutropenia 63 (31.82) 18 (9.09) 81 (40.91) Thrombocytopenia 29 (14.65) 8 (4.04) 37 (18.69) Liver Function Abnormality 35 (17.68) 9 (4.55) 44 (22.22) Immune-Related Pneumonia 8 (4.04) 6 (3.03) 14 (7.07) Immune-Related Myocarditis 0 (0.00) 0 (0.00) 0 (0.00) Thyroid Dysfunction 35 (17.68) 7 (3.53) 42 (21.21) Further analysis of the association between hematological indicators and irAEs revealed that NLR and LDH were significantly correlated with irAEs risk (Table 12 ). The irAEs incidence in the low NLR group (< 3.72) was significantly higher than in the high NLR group (55.4% vs. 37.1%, P = 0.010), and patients with low NLR had nearly twice the risk of developing irAEs (OR = 1.976, 95% CI: 1.098 ~ 3.557, P = 0.023). Similarly, the irAEs incidence in the low LDH group (< 226.50 U/L) was significantly higher than in the high LDH group (62.2% vs. 35.3%, P < 0.001), with the risk increased nearly threefold (OR = 2.881, 95% CI: 1.590 ~ 5.223, P < 0.001). In contrast, PLR and ALBI showed no significant statistical association with irAEs (P = 0.143 and P = 0.528). Table 12 Correlation Analysis Between Hematological Indicators and irAEs Hematological Indicator Total irAEs Group n(%) Non-irAEs Group n(%) x 2 P Value OR (95%CI) P Value NLR <3.72 101 56(55.4) 45(44.6) 6.685 0.010 1.976(1.098 ~ 3.557) 0.023 ≥ 3.72 97 36(37.1) 61(62.9) PLR <207.43 103 53(51.5) 50(48.5) 2.150 0.143 ≥ 207.43 95 39(41.1) 56(58.9) ALBI <-2.68 118 57(48.3) 61(51.7) 0.398 0.528 ≥-2.68 80 35(43.8) 45(56.3) LDH <226.50 82 51(62.2) 31(37.8) 13.923 <0.001 2.881(1.590 ~ 5.223) <0.001 ≥ 226.50 116 41(35.3) 75(64.7) 4. Discussion Although the precision of NSCLC treatment has significantly improved in recent years, it remains challenging. The emergence of PD-1 inhibitors has provided new hope for advanced NSCLC patients, but many clinical studies indicate that only a minority benefit [ 31 ]. Therefore, identifying effective predictive biomarkers to screen potential beneficiaries is urgent. This study explored the relationship between baseline peripheral blood biomarkers (NLR, PLR, ALBI, LDH) and short-term efficacy, PFS, and OS in advanced NSCLC patients, further analyzing their prognostic value. Our results show that NLR, PLR, ALBI, and LDH have significant roles in predicting PFS and OS, and combined analysis may enhance their clinical utility. Additionally, this study revealed significant pathological subtype (squamous vs. adenocarcinoma) influences on biomarker predictive efficacy, providing new perspectives for individualized stratification. This study, by analyzing baseline blood biomarkers in advanced NSCLC patients, found that NLR, PLR, ALBI, and LDH have significant meaning in predicting efficacy and survival. ROC curve analysis showed AUC values for NLR, PLR, ALBI, and LDH of 0.804, 0.694, 0.684, and 0.726 (Fig. 2 ), with NLR and LDH having the best predictive efficacy (AUC > 0.70), suggesting that systemic inflammation (NLR/PLR) and tumor metabolic activity (LDH) may jointly drive immunotherapy response differences. This study further clarified key factors influencing immunotherapy short-term efficacy (ORR). Multivariate logistic regression showed PD-L1 positive expression (OR = 0.361, P = 0.013) and ALBI<-2.68 (OR = 2.524, P = 0.021) as independent predictors of objective response, suggesting synergistic roles of tumor immunogenicity and liver function metabolic status in determining early treatment response. Notably, although PD-L1-positive patients had significantly higher ORR (40.91% vs. 21.43%), their protective effect in multivariate models (OR < 1) may reflect the dynamic complexity of PD-L1 expression—some positive patients have weakened benefits due to high tumor burden or immunosuppressive microenvironments. The predictive value of PLR < 207.43 (OR = 1.892, P = 0.035) and low ALBI score (OR = 2.524, P = 0.021) further supports the key roles of systemic inflammation and liver function in early immune activation. Kaplan-Meier survival analysis further revealed that lower NLR (< 3.72), PLR (< 207.43), ALBI (<-2.68), and LDH (< 226.5 U/L) levels were associated with longer PFS and OS. Among them, LDH's stratification ability was most prominent, with low-level group median PFS extended by 2.4 months (9.5 vs. 7.1 months), and OS risk reduced by 82.6% (HR = 1.826), consistent with LDH-mediated lactate metabolism inhibiting T cell function and promoting immune escape. Further subgroup analysis showed that biomarker predictive value has significant pathological type dependency: in squamous carcinoma patients, low NLR (< 3.72) and low PLR (< 207.43) extended median PFS by 1.9 months (P = 0.002) and 2.8 months (P = 0.002), respectively, while in adenocarcinoma, only LDH retained independent prognostic value (P = 0.032). This difference may stem from stronger inflammation-driven features in squamous carcinoma microenvironments (e.g., high IL-6/TNF-α expression [ 32 ]), while adenocarcinoma's metabolic heterogeneity (e.g., EGFR/KRAS mutation-related glycolysis reprogramming [ 33 ]) may weaken inflammatory marker predictive efficacy. Additionally, ALBI score's independent prognostic role in PFS and OS was validated, with lower ALBI scores (<-2.68) associated with longer survival, suggesting liver function not only affects drug metabolism and clearance but may also indirectly enhance immune response by regulating systemic inflammation balance (e.g., albumin maintaining vascular permeability, bilirubin antioxidative stress). Inflammatory status is closely related to the prognosis of various cancers [ 32 , 33 ], and NLR and PLR evaluated in this study are good biomarkers reflecting inflammatory status. In a clinical study by Bagley et al. [ 10 ], analysis of 175 advanced NSCLC patients treated with nivolumab showed baseline NLR ≥ 5 as a poor prognostic factor for OS (HR = 1.83, 95% CI:1.2 ~ 2.8, P = 0.006) and an independent predictor for PFS (HR = 1.42, 95% CI:1.02 ~ 2.0, P = 0.04). Another retrospective study also indicated that baseline NLR > 5 was closely related to poor OS [ 31 ]. A meta-analysis by Cao [ 25 ] et al. included 14 retrospective studies involving 1225 NSCLC patients, exploring the predictive value of baseline and post-treatment NLR for nivolumab efficacy. Pooled results showed higher baseline NLR associated with poorer PFS (HR = 1.44, 95% CI:1.18 ~ 1.77, P < 0.05) and OS (HR = 1.75, 95% CI:1.33 ~ 2.30, P < 0.05). Subgroup analysis further indicated NLR ≥ 5 was more reliable for predicting PFS (HR = 1.73, 95% CI:1.14 ~ 2.62, P < 0.05) and OS (HR = 1.76, 95% CI:1.47 ~ 2.10, P < 0.05). Additionally, post-treatment NLR elevation was associated with poorer PFS (HR = 3.17, 95% CI:1.48 ~ 6.82, P < 0.05) and OS (HR = 2.26, 95% CI:1.05 ~ 4.86, P < 0.05). These results suggest baseline and post-treatment NLR as potential prognostic markers for NSCLC patients receiving nivolumab. Zhou [ 34 ] et al.'s meta-analysis included 21 studies covering 2312 advanced lung cancer patients receiving immunotherapy. Pooled analysis showed elevated PLR associated with poorer OS (HR = 2.24, 95% CI:1.87 ~ 2.68, I²=44%, P = 0.01) and PFS (HR = 1.66, 95% CI:1.36 ~ 2.04; I²=64%, P < 0.01). Zhang Na [ 35 ] et al. included 1845 NSCLC patients from 21 studies, with overall analysis showing high NLR significantly associated with poorer OS (HR = 2.50, 95% CI:1.79 ~ 3.51, P < 0.001) and PFS (HR = 1.77, 95% CI:1.51 ~ 2.01, P < 0.001). Subgroup results were consistent. Similarly, pooled PLR results showed elevated PLR associated with poorer OS (HR = 1.93, 95% CI:1.51 ~ 2.01, P < 0.001) and PFS (HR = 1.57, 95% CI:1.30 ~ 1.90, P < 0.001). In our study, NLR was an independent prognostic factor for OS and PFS, with higher NLR (NLR ≥ 3.72) significantly associated with poorer OS (HR = 1.629, 95% CI:1.099 ~ 2.415, P = 0.015) and PFS (HR = 1.638, 95% CI:1.117 ~ 2.402, P = 0.012). Similarly, high baseline PLR group showed poorer OS and PFS, but not significantly in multivariate analysis. LDH is a metabolic enzyme secreted by proliferative tumor cells, with serum levels often used as a biomarker for tumor burden. Multiple studies show LDH significantly correlates with prognosis in melanoma patients receiving immune checkpoint inhibitors [ 36 , 37 ]. Reports have explored LDH in predicting PFS [ 38 , 39 ] and OS [ 40 ] in ICIs-treated NSCLC patients. In Taniguchi et al. [ 38 ]'s retrospective study of 201 advanced NSCLC patients receiving nivolumab, baseline LDH > 240 U/L was significantly associated with poorer PFS (P < 0.05). In L. Mezquita [ 40 ]'s study, patients with LDH exceeding the upper normal limit had HR = 2.51 for OS (95% CI:1.32 ~ 4.76) compared to low LDH, indicating LDH as an important independent prognostic factor for advanced lung cancer patients receiving ICIs. This study also confirmed baseline LDH levels significantly associated with PFS and OS in ICIs-treated advanced NSCLC patients, with low LDH group PFS at 9.5 months (vs. 7.1 months, P < 0.001), OS risk reduced by 82.6% (HR = 1.826, P = 0.001). Similar results were seen in adenocarcinoma and squamous subgroups: in adenocarcinoma, low LDH group PFS extended by 2.7 months (P = 0.032), OS by 2.6 months (P = 0.021). In squamous patients, low LDH group OS reached 24.6 months (vs. 19.8 months, P = 0.001), with PFS also significantly extended (P = 0.017). Meanwhile, LDH levels were significantly associated with irAEs, with low LDH group (< 226.50 U/L) having higher irAEs incidence (62.2% vs. 35.3%, P < 0.001) and risk (OR = 2.881, 95% CI:1.590 ~ 5.223, P < 0.001). Additionally, the application of ALBI grading in assessing immunotherapy prognosis has gradually gained attention. Pinato and Kaneko et al. have explored ALBI grading's prognostic value in hepatocellular carcinoma patients receiving immunotherapy [ 41 ]. They found pre-treatment ALBI grading could predict OS extension and was superior to Child-Pugh grading in predicting mortality. Kinoshita et al. compared pre-operative ALBI grading with clinicopathological features and prognosis in resectable NSCLC patients, finding ALBI grades 2/3 associated with poor prognosis [ 30 ]. Ryosuke Matsukane et al. [ 42 ]'s study showed ALBI grading as a pre-treatment liver reserve assessment could significantly predict survival in ICIs-treated NSCLC patients, with pre-treatment ALBI grading as independent prognostic factor for PFS (HR 0.57, 95% CI 0.38 ~ 0.86, p = 0.007) and OS (HR = 0.45, 95% CI:0.29 ~ 0.72, P = 0.001), indicating ALBI grading-evaluated pre-treatment liver function as an important biomarker for predicting ICIs efficacy in NSCLC. This article determined the optimal ALBI cut-off as -2.68 via calculation; in the full cohort, ALBI<-2.68 group PFS extended by 1.6 months (8.7 vs. 7.1 months, P = 0.022), OS by 3.9 months (21.4 vs. 17.5 months, P = 0.047), and multivariate analysis proved ALBI as independent predictor for PFS and OS. Similarly, in squamous patients, low ALBI score group median OS reached 21.4 months (vs. 17.3 months, P = 0.047), with significant independent predictive value. This study also evaluated clinical factors like PS score and efficacy on patient prognosis via Cox regression. Results showed PS score and efficacy (especially SD and PD) significantly influenced PFS and OS. Poorer PS scores (PS ≥ 2) were closely associated with shorter PFS and OS, validating PS score as a classic prognostic indicator in lung cancer. Additionally, short-term efficacy (e.g., SD and PD) had important effects on prognosis, especially PD group patients with significantly shorter PFS and OS. This study provides valuable insights into exploring baseline blood biomarkers' prognostic value in immunotherapy for advanced NSCLC patients but has limitations. First, as a retrospective analysis from two centers (Renmin Hospital of Wuhan University and Macheng People's Hospital Tumor Centers), although multi-center design partially reduced single-institution bias, results may be influenced by inter-center data collection standard differences, regional variations, patient selection bias, and data completeness limitations. Second, although the sample size reached 198, it was limited in certain subgroup analyses (e.g., squamous patients receiving immuno-combined targeted therapy), potentially leading to insufficient statistical power, requiring larger-scale, multi-center prospective studies for validation. Additionally, while we analyzed multiple biomarkers (e.g., NLR, PLR, ALBI, LDH), this study did not deeply explore other factors potentially affecting immunotherapy prognosis, such as tumor gene mutations, immune microenvironment, and regimen differences, which may play varying roles in different patient groups. Finally, although we validated significant relationships between ALBI, NLR, PLR, etc., and PFS/OS, their clinical operability and practical application need further exploration and optimization, especially how to efficiently apply these markers in real clinical settings for efficacy prediction. 5. Conclusion This study found that peripheral blood markers (NLR, PLR, ALBI, LDH) have important predictive value for survival outcomes in advanced NSCLC, with significant pathological subtype heterogeneity: squamous prognosis mainly driven by systemic inflammation, while adenocarcinoma depends more on metabolic activity and molecular features, suggesting the need for subtype-specific biomarker strategies. Short-term disease control status is a key predictor of survival, radiotherapy improves prognosis, but immuno-combined targeted therapy may increase mortality risk in squamous patients. Meanwhile, baseline NLR and LDH are significantly associated with irAEs risk. These findings provide a theoretical basis for integrating inflammation, metabolism, and treatment response markers to build stratification models, hoping to guide clinical decisions through multi-dimensional biomarkers, optimizing treatment selection while balancing efficacy and safety, advancing precise practice of individualized immunotherapy in advanced NSCLC. Abbreviations ALB, Albumin ALBI, Albumin-bilirubin ALC, Absolute Lymphocyte Count AMC, Absolute Monocyte Count ANC, Absolute Neutrophil Count AST, Aspartate Aminotransferase AUC, Area Under The Curve CR, Complete Response DCR, Disease Control Rate ECOG PS, Eastern Cooperative Oncology Group Performance Score ICIs, Immune Checkpoint Inhibitors irAEs, Immune-related Adverse Events LDH, Lactate dehydrogenase NLR, Neutrophil-to-lymphocyte Ratio NSCLC, Non-Small Cell Lung Cancer OS, Overall Survival ORR, Overall Response Rate PFS, Progression Free Survival PR, Partial Response PD, Progressive Disease PD-1, Programmed Death 1 PD-L1, Programmed Death-ligand 1 PLR, Platelet-to-lymphocyte Ratio ROC, Receiver Operating Characteristic TME, Tumor microenvironment Declarations Acknowledgements The work was supported by the Natural Science Foundation of Hubei Provincial (2023AFB766 to DDC) .The authors sincerely acknowledge the strong support for this study from the Department of Oncology at Macheng People's Hospital and Renmin Hospital of Wuhan University. We extend our gratitude to the Medical Department and relevant clinical units of Renmin Hospital of Wuhan University for facilitating data access. Furthermore, we thank the Clinical Research Ethics Committee of Renmin Hospital of Wuhan University for approving and overseeing the study protocol. Lastly, we wish to express our deepest appreciation to all the patients who participated in this study; their trust and contributions were fundamental to the completion of this research. Authorship contribution Conceptualization: DDC; Data curation: NZ, XYW, WSZ, XC, HH; Formal analysis: NZ, XYW, DDC; Funding acquisition: DDC, WSZ; Investigation: all authors; Methodology: NZ, DDC; Project administration: DDC; Supervision: DDC, HH; Writing—original draft: NZ, XYW; and Writing—review & editing: DDC. All authors have reviewed and approved the final manuscript. Availability of data and materials The data used and analysed in this study are available from the corresponding author on reasonable request. Declarations Statement on informed consent Since this study involves the retrospective use of data, and the patient information has been fully anonymized, and the study itself does not interfere with the patient's diagnosis and treatment process nor impose any additional burden on patients, the requirement for obtaining informed consent from patients for this study has been waived. Ethics approval and consent to participate The study protocol has been reviewed and approved by the Ethics Committee of Renmin Hospital of Wuhan University (Ethics Approval No.: WDRY2020F048). The need for informed consent was waived by the Ethics Committee of Renmin Hospital of Wuhan University due to the retrospective nature of the study. This study was conducted in accordance with the Helsinki declaration and its later amendments or comparable ethical standards, so as to ensure the full protection of patients' legitimate rights and interests such as the right to privacy and the right to information. Conflicts of Interest The authors declare no conflicts of interest. Clinical trial number Not applicable. Consent for publication Not applicable. References Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol. 2022;17(3):362–87. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016;11(1):39–51. Schiller JH, Harrington D, Belani CP, et al. 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Pretreatment lactate dehydrogenase may predict outcome of advanced non small-cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis. Cancer Med. 2019;8(4):1467–73. Weide B, Martens A, Hassel JC, et al. Baseline Biomarkers for Outcome of Melanoma Patients Treated with Pembrolizumab. Clin Cancer Res. 2016;22(22):5487–96. Kinoshita F, Yamashita T, Oku Y, et al. Prognostic Impact of Albumin-bilirubin (ALBI) Grade on Non-small Lung Cell Carcinoma: A Propensity-score Matched Analysis. Anticancer Res. 2021;41(3):1621–8. Iwai Y, Hamanishi J, Chamoto K, et al. Cancer immunotherapies targeting the PD-1 signaling pathway. J Biomed Sci. 2017;24(1):26. Kos FT, Hocazade C, Kos M, et al. Assessment of Prognostic Value of Neutrophil to Lymphocyte Ratio and Prognostic Nutritional Index as a Sytemic Inflammatory Marker in Non-small Cell Lung Cancer. Asian Pac J Cancer Prev. 2015;16(9):3997–4002. Miyazaki T, Sakai M, Sohda M, et al. Prognostic Significance of Inflammatory and Nutritional Parameters in Patients with Esophageal Cancer. Anticancer Res. 2016;36(12):6557–62. Zhou K, Cao J, Lin H, et al. Prognostic role of the platelet to lymphocyte ratio (PLR) in the clinical outcomes of patients with advanced lung cancer receiving immunotherapy: A systematic review and meta-analysis. Front Oncol. 2022;12:962173. Zhang N, Jiang J, Tang S, et al. Predictive value of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in non-small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis. Int Immunopharmacol. 2020;85:106677. Kelderman S, Heemskerk B, van Tinteren H, et al. Lactate dehydrogenase as a selection criterion for ipilimumab treatment in metastatic melanoma. Cancer Immunol Immunother. 2014;63(5):449–58. Diem S, Kasenda B, Spain L, et al. Serum lactate dehydrogenase as an early marker for outcome in patients treated with anti-PD-1 therapy in metastatic melanoma. Br J Cancer. 2016;114(3):256–61. Taniguchi Y, Tamiya A, Isa SI, et al. Predictive Factors for Poor Progression-free Survival in Patients with Non-small Cell Lung Cancer Treated with Nivolumab. Anticancer Res. 2017;37(10):5857–62. Oya Y, Yoshida T, Kuroda H, et al. Predictive clinical parameters for the response of nivolumab in pretreated advanced non-small-cell lung cancer. Oncotarget. 2017;8(61):103117–28. Mezquita L, Auclin E, Ferrara R, et al. Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351–7. Pinato DJ, Sharma R, Allara E, et al. The ALBI grade provides objective hepatic reserve estimation across each BCLC stage of hepatocellular carcinoma. J Hepatol. 2017;66(2):338–46. Matsukane R, Watanabe H, Hata K, et al. Prognostic significance of pre-treatment ALBI grade in advanced non-small cell lung cancer receiving immune checkpoint therapy. Sci Rep. 2021;11(1):15057. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 07 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor invited by journal 31 Jan, 2026 Editor assigned by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8723320","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587780976,"identity":"68fc97d6-498d-45af-bd49-eea51d098330","order_by":0,"name":"Nan Zhao","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Zhao","suffix":""},{"id":587780977,"identity":"32052dae-9411-482d-a924-75bc32f2baf0","order_by":1,"name":"Xinyu Wu","email":"","orcid":"","institution":"Renmin Hospital of Wuhan 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Baseline NLR, PLR, ALBI, and LDH for Predicting Overall Survival in Advanced Lung Cancer Patients Treated with ICIs\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8723320/v1/31dcbf9a5a435bf495d9023d.png"},{"id":102375700,"identity":"e7a9c581-9481-4b17-b93c-4ef5572c5c66","added_by":"auto","created_at":"2026-02-11 05:22:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164885,"visible":true,"origin":"","legend":"\u003cp\u003ePFS Survival Curves for Patients with Different Baseline Levels of NLR, PLR, ALBI, and LDH\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8723320/v1/40ef0df17eb846ad8d0b452e.png"},{"id":102375702,"identity":"13960f82-151f-4533-afc1-7c75421d5981","added_by":"auto","created_at":"2026-02-11 05:22:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":162226,"visible":true,"origin":"","legend":"\u003cp\u003eOS Survival Curves for Patients with Different Baseline Levels of NLR, PLR, ALBI, and LDH\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8723320/v1/d08a11346d358e170bba08bc.png"},{"id":102398233,"identity":"f0e8c417-5b84-4e36-939f-82a65b4d5426","added_by":"auto","created_at":"2026-02-11 10:21:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3507950,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8723320/v1/5ba39e06-df30-4635-93ad-8612cf37d311.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics and Hematological Indicators Predictors of Immunotherapy Response in Advanced Non-Small Cell Lung Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer remains a major threat to global public health, with its epidemiological features showing a persistent worsening trend. According to data from the International Agency for Research on Cancer (IARC), there are over 2.2\u0026nbsp;million new lung cancer cases annually worldwide, accounting for 12.4% of malignant tumors; and 1.8\u0026nbsp;million deaths, representing 18% of cancer-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Pathologically, non-small cell lung cancer (NSCLC) predominates (85%), with adenocarcinoma subtypes continuing to rise [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With advances in modern medical technology, early-stage lung cancer patients can achieve favorable prognosis through surgical resection combined with adjuvant therapy, with 5-year survival rates reaching 60% to 80%. However, clinical data indicate that approximately 70% of NSCLC patients are diagnosed at stage IIIB or IV [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditional chemotherapy regimens (e.g., platinum combined with pemetrexed) can partially alleviate symptoms, but the median overall survival (mOS) remains only 8\u0026ndash;12 months, accompanied by significant toxicities such as bone marrow suppression and gastrointestinal reactions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Targeted therapies (e.g., EGFR-TKIs, ALK inhibitors) have achieved breakthroughs in driver gene-positive patients, but their coverage is limited (only about 30% of NSCLC patients have actionable targets) and drug resistance issues restrict widespread application [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This dilemma has prompted researchers to turn their attention to immunotherapy.\u003c/p\u003e \u003cp\u003eThe advent of immune checkpoint inhibitors (ICIs) has transformed the landscape of traditional cancer treatment, introducing novel therapeutic approaches to clinical practice. Their mechanism of action focuses on relieving immunosuppressive signals in the tumor microenvironment (TME): PD-1/PD-L1 pathway inhibitors block the binding of PD-1 receptors on T cells to PD-L1 ligands on tumor cells, restoring effector T cell anti-tumor activity; while CTLA-4 inhibitors enhance activation signals during the T cell priming phase, expanding the immune response spectrum [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The KEYNOTE-024 study showed that in advanced NSCLC patients with high PD-L1 expression (tumor proportion score TPS\u0026thinsp;\u0026ge;\u0026thinsp;50%), pembrolizumab monotherapy significantly prolonged mOS compared to chemotherapy (30.0 vs. 14.2 months, HR\u0026thinsp;=\u0026thinsp;0.63), with a nearly 50% reduction in grade 3 or higher adverse events [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The IMpower150 trial demonstrated that in non-squamous NSCLC, the triple regimen of atezolizumab, bevacizumab, and chemotherapy significantly improved outcomes, with an objective response rate of 64%, benefiting even patients with brain metastases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Based on these breakthroughs, ICIs have been elevated from second-line to first-line standard therapy for advanced NSCLC.\u003c/p\u003e \u003cp\u003eNevertheless, the widespread application of immunotherapy still faces numerous challenges. Clinical data show that only about 50% of patients respond to ICIs, with long-term tumor remission achieved in only 10%-15%. Meanwhile, approximately 10%-15% of patients may experience severe immune-related adverse events (irAEs), which can lead to long-term sequelae or even life-threatening conditions. Therefore, selecting patients who can benefit from ICIs and preventing/managing potential irAEs have become key research priorities and challenges.\u003c/p\u003e \u003cp\u003eIn current clinical practice, predictive markers are mainly divided into two categories: tumor-intrinsic markers and host-related markers. PD-L1 TPS, as the most widely used marker, has been extensively applied in treatment decision-making for advanced NSCLC patients [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Generally, patients with higher PD-L1 TPS levels have higher response rates. However, the predictive ability of PD-L1 TPS is not absolute; some patients with high PD-L1 TPS do not benefit, while some with low or negative expression respond [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, tumor mutational burden (TMB) has garnered significant research interest as a potential biomarker [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Multiple studies indicate that high TMB may correlate with higher response rates [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Beyond biological markers, patient clinical features may also play important roles in efficacy prediction, such as gender [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], performance status (ECOG PS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], body mass index (BMI) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], specific sites of tumor metastasis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and concomitant medications (e.g., antibiotics and corticosteroids [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In recent years, the pathophysiology of irAEs and their correlation with individual patient characteristics have become research hotspots. Some studies suggest that irAEs occurrence may correlate with higher response rates, but the specific mechanisms remain unclear, and balancing reduced irAEs risk while ensuring efficacy is a pressing clinical issue.\u003c/p\u003e \u003cp\u003eIn recent years, systemic inflammation indicators based on peripheral blood have emerged as research hotspots for prognosis assessment due to their real-time, dynamic, and non-invasive advantages. Particularly in NSCLC patients, inflammatory markers in the blood are thought to be closely related to the formation of immunosuppressive microenvironments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies show that elevated neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) often predict shorter progression-free survival (PFS) and overall survival (OS) [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A study involving 187 patients found that NLR\u0026thinsp;\u0026lt;\u0026thinsp;5 was associated with improved PFS and OS, and PLR\u0026thinsp;\u0026lt;\u0026thinsp;200 with PFS, OS, ORR, and DCR [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggesting NLR and PLR as potential predictive markers for ICIs efficacy. Lactate dehydrogenase (LDH) and albumin-bilirubin index (ALBI) scores are also considered important prognostic factors. LDH, as a key marker of tumor cell metabolism, has been shown to correlate with cancer malignancy and patient prognosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Elevated LDH (\u0026gt;\u0026thinsp;250 U/L) indicates hypoxic microenvironment and active tumor metabolism, associated with primary resistance to ICIs (ORR reduced by 62%) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Kinoshita's team found that preoperative ALBI grade was significantly associated with postoperative recurrence in NSCLC, with ALBI grades 2/3 as potential independent prognostic factors for disease-free survival (DFS) and OS [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], suggesting it as a novel predictive tool for immunotherapy.\u003c/p\u003e \u003cp\u003eGiven the above research background, this study employs retrospective analysis to systematically evaluate clinical data from advanced NSCLC patients receiving ICIs, focusing on the clinical value of NLR, PLR, LDH, and ALBI in prognosis assessment. Subgroup analyses by pathological type (adenocarcinoma and squamous carcinoma) were conducted to provide evidence-based support for selecting immunotherapy beneficiaries and optimizing treatment decisions, while offering new insights for early irAEs warning.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 General Information\u003c/h2\u003e \u003cp\u003eThis retrospective study collected data from 198 NSCLC patients treated with ICIs at the Tumor Centers of Renmin Hospital of Wuhan University and Macheng People's Hospital between January 2021 and December 2023. The study was approved by the Clinical Research Ethics Committee of Renmin Hospital of Wuhan University (Ethics Approval No.: WDRY2020F048).\u003c/p\u003e \u003cp\u003eInclusion Criteria:\u003c/p\u003e \u003cp\u003e(1) Pathologically or histologically confirmed non-small cell lung cancer (NSCLC);\u003c/p\u003e \u003cp\u003e(2) Advanced lung malignancy (ineligible for radical surgery or radical radiotherapy);\u003c/p\u003e \u003cp\u003e(3) ECOG PS score: 0\u0026ndash;2;\u003c/p\u003e \u003cp\u003e(4) Received at least 2 cycles of immune checkpoint inhibitor therapy;\u003c/p\u003e \u003cp\u003e(5) Complete baseline data: enhanced CT/PET-CT within 30 days before treatment, pathological subtype and staging evidence, blood test indicators, etc.\u003c/p\u003e \u003cp\u003eExclusion Criteria:\u003c/p\u003e \u003cp\u003e(1) History of other malignancies within 5 years (excluding non-melanoma skin cancer/cervical carcinoma in situ);\u003c/p\u003e \u003cp\u003e(2) Presence of any of the following: active autoimmune disease (requiring immunosuppressants, e.g., systemic lupus erythematosus, myasthenia gravis); uncontrolled systemic infection (CRP\u0026thinsp;\u0026gt;\u0026thinsp;3 times normal upper limit with fever); interstitial lung disease (CT-confirmed pulmonary interstitial fibrosis\u0026thinsp;\u0026gt;\u0026thinsp;20%);\u003c/p\u003e \u003cp\u003e(3) Patients with fewer than two treatment cycles;\u003c/p\u003e \u003cp\u003e(4) Patients lacking complete clinical data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003eData collection encompassed outpatient and inpatient clinical information, including: (1) Demographic and baseline characteristics: age, gender, smoking history, comorbidities, etc.; (2) Tumor characteristics: histological subtype (adenocarcinoma/squamous carcinoma/SCLC/other), molecular typing (PD-L1 TPS detection platform and antibody, TMB value, driver gene mutations), tumor TNM staging, primary lesion maximum diameter (pre-treatment CT measurement), metastatic burden, etc.; (3) Treatment parameters: immunotherapy regimen (drug name and dose, line of therapy, combination regimen, cycles); (4) Laboratory indicators: complete blood count (absolute neutrophil count (ANC), absolute lymphocyte count (ALC), platelet count (PLT), absolute monocyte count (AMC), etc.), liver function (alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), total bilirubin (TBIL), and albumin-bilirubin index (ALBI)), inflammatory markers (C-reactive protein, lactate dehydrogenase (LDH), ferritin), renal function (creatinine, estimated glomerular filtration rate (eGFR)); (5) Imaging and efficacy assessment (6) Safety data: immune-related adverse reactions, treatment interruption/termination records; (7) Survival follow-up: Primary endpoints: PFS: from treatment initiation to radiological progression/death; OS: from treatment initiation to all-cause death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Treatment Regimens\u003c/h2\u003e \u003cp\u003eThis study selected patients receiving systemic ICIs therapy as subjects, dividing them into four subgroups based on regimens: ICIs monotherapy, ICIs combined with chemotherapy, ICIs combined with targeted therapy, and ICIs combined with chemotherapy and targeted therapy. Regimen selection was based on individualized clinical features and the attending physician's professional judgment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Efficacy Evaluation Methods\u003c/h2\u003e \u003cp\u003eAccording to the Response Evaluation Criteria in Solid Tumors (RECIST 1.1), each patient underwent chest enhanced CT every 8\u0026ndash;12 weeks for efficacy assessment. Data follow-up cutoff was December 1, 2024.\u003c/p\u003e \u003cp\u003eThis study employed the Response Evaluation Criteria in Solid Tumors (RECIST 1.1) for systematic evaluation. All patients underwent baseline chest enhanced CT (slice thickness\u0026thinsp;\u0026le;\u0026thinsp;2 mm, contrast agent iohexol 100 mL) to identify target lesions (up to 5, \u0026le;\u0026thinsp;2 per organ), with imaging re-evaluated every 8\u0026ndash;12 weeks during treatment. Follow-up endpoint was set as December 1, 2024. Cases lost to follow-up at the last visit were treated as censored data in statistical analysis. Efficacy was assessed according to international standards, categorized into four levels: (1) Complete Response (CR): Imaging shows complete disappearance of all target lesions, complete regression of non-target lesions, normalization of tumor markers, and no new lesions; (2) Partial Response (PR): Sum of target lesion diameters reduced by \u0026ge;\u0026thinsp;30% from baseline, with no progression in non-target lesions; (3) Stable Disease (SD): Changes in lesion diameters do not meet PR criteria but also do not meet progression criteria; (4) Progressive Disease (PD): Sum of target lesion diameters increased by \u0026ge;\u0026thinsp;20%, or clear progression in non-target lesions, or new lesions detected. All efficacy determinations were objectively based on imaging and laboratory results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Methods\u003c/h2\u003e \u003cp\u003eThis study used SPSS 27.0 and R 4.3.1 for data analysis, with GraphPad Prism 10 for visualization. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e\u0026plusmn;s), and categorical variables as frequencies (percentages). Inter-group differences were compared using chi-square tests or Fisher's exact test (when expected frequency\u0026thinsp;\u0026lt;\u0026thinsp;5). ROC curve analysis determined optimal cut-off values for peripheral blood markers and calculated corresponding area under the curve (AUC) and 95% confidence intervals. For survival analysis, Kaplan-Meier method was used to plot PFS and OS curves, with log-rank tests for inter-group comparisons. Cox proportional hazards regression model was employed for multivariate prognostic factor assessment. All statistical analyses in this study used two-sided tests, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThis study included a total of 198 patients with advanced NSCLC from the Oncology Centers of Renmin Hospital of Wuhan University and Macheng People's Hospital (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, male patients accounted for 78.28%, and female patients for 21.72%. Patients under 65 years old comprised 53.03%, and those with ECOG PS scores of 0\u0026ndash;1 accounted for 85.35%. In terms of clinical staging, stage IV patients accounted for 70.20%, with adenocarcinoma being the predominant pathological type (55.05%), followed by squamous cell carcinoma (41.92%). Regarding treatment, most patients received immunotherapy combined with chemotherapy (69.19%), with tislelizumab, camrelizumab, and sintilimab being commonly used immunotherapy drugs (accounting for 30.30%, 25.76%, and 24.24%, respectively), and 55.56% of patients received radiotherapy. Gene mutation-positive patients accounted for only 18.18%, and PD-L1-positive patients for 22.22%. In terms of hematological immune response markers, patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;3.72 accounted for 51.01%, PLR\u0026thinsp;\u0026lt;\u0026thinsp;207.43 for 52.02%, ALBI score \u0026lt;-2.68 for 59.60%, and LDH\u0026thinsp;\u0026lt;\u0026thinsp;226.50 for 41.41%. As of December 1, 2024, 176 patients had died during follow-up, with a median follow-up time of 28.1 months, median PFS of 7.7 months (95% CI\u0026thinsp;=\u0026thinsp;6.9\u0026thinsp;~\u0026thinsp;8.5 months), and median OS of 20.1 months (95% CI\u0026thinsp;=\u0026thinsp;18.2\u0026thinsp;~\u0026thinsp;21.9 months) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Baseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155(78.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(21.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93(46.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105(53.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109(55.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83(41.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(3.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169(85.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(14.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59(29.80)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(70.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(25.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111(56.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(18.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154(77.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44(22.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162(81.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36(18.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78(39.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73(36.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27(13.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(10.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(25.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(9.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60(30.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(24.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(10.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(17.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(69.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(3.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(10.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110(55.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88(44.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151(76.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(23.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(67.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65(32.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101(51.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97(48.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103(52.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95(47.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118(59.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80(40.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82(41.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(58.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ROC Curve Analysis\u003c/h2\u003e \u003cp\u003eThis study performed ROC curve analysis on the overall survival of patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to evaluate the discriminative ability of NLR, PLR, ALBI, and LDH in predicting mortality risk. The analysis results showed that the areas under the curve (AUC) for NLR, PLR, ALBI, and LDH were 0.804, 0.694, 0.684, and 0.726, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe optimal cut-off values calculated using the Youden index were: NLR 3.72, PLR 207.43, ALBI\u0026thinsp;\u0026minus;\u0026thinsp;2.68, LDH 226.5 U/L. Based on these cut-off values, patients were divided into low-level groups (L-group) and high-level groups (H-group), with further exploration of their correlations with clinical efficacy and survival prognosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of Factors Influencing Short-Term Efficacy\u003c/h2\u003e \u003cp\u003eBy the end of follow-up, the best treatment response assessments for the 198 included patients showed: PR in 51 cases (25.8%), SD in 111 cases (56.1%), PD in 36 cases (18.2%). Accordingly, the objective response rate (ORR) for the entire cohort was 25.8%, and the disease control rate (DCR) was 81.9%. For efficacy analysis, patients were divided into two subgroups: tumor response group (PR patients, n\u0026thinsp;=\u0026thinsp;51) and non-response group (SD\u0026thinsp;+\u0026thinsp;PD patients, n\u0026thinsp;=\u0026thinsp;147).\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Analysis of Short-Term Efficacy Factors in the Full Cohort\u003c/h2\u003e \u003cp\u003eThis study included 198 patients, of which 51 (25.76%) achieved PR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Univariate analysis showed that PD-L1 positive expression (40.91% vs. 21.43%, χ\u0026sup2;=6.791, P\u0026thinsp;=\u0026thinsp;0.009), absence of bone metastasis (30.08% vs. 16.92%, χ\u0026sup2;=3.949, P\u0026thinsp;=\u0026thinsp;0.047), PLR\u0026thinsp;\u0026lt;\u0026thinsp;207.43 (32.04% vs. 18.95%, χ\u0026sup2;=4.429, P\u0026thinsp;=\u0026thinsp;0.035), and ALBI \u0026lt;-2.68 (31.36% vs. 17.50%, χ\u0026sup2;=4.787, P\u0026thinsp;=\u0026thinsp;0.029) were significantly associated with higher PR rates. There were also significant differences in PR rates among different immunotherapy drugs (χ\u0026sup2;=12.862, P\u0026thinsp;=\u0026thinsp;0.012), with pembrolizumab having the highest PR rate (50.00%) and camrelizumab the lowest (13.73%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Clinical Characteristics and Short-Term Efficacy in the Full Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTumor Non-Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42(27.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113(72.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(20.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34(79.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28(26.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77(73.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(24.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70(75.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24(22.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85(77.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSquamous Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(31.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57(68.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(83.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47(27.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122(72.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(13.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25(86.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(33.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39(66.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.954(0.425\u0026thinsp;~\u0026thinsp;2.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31(22.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108(77.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePD-L1 Expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33(21.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121(78.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.361(0.161\u0026thinsp;~\u0026thinsp;0.808)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(40.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26(59.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38(23.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124(76.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13(36.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23(63.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(23.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60(76.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(34.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48(65.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.558(0.248\u0026thinsp;~\u0026thinsp;1.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(14.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23(85.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.549(0.396\u0026thinsp;~\u0026thinsp;6.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.802(0.193\u0026thinsp;~\u0026thinsp;3.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(38.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13(61.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(13.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44(86.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.157(0.042\u0026thinsp;~\u0026thinsp;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.311(0.109\u0026thinsp;~\u0026thinsp;0.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.681(0.212\u0026thinsp;~\u0026thinsp;2.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39(81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.258(0.07\u0026thinsp;~\u0026thinsp;0.952)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25(73.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36(26.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101(73.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(28.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15(71.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(20.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70(79.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41(27.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e110(72.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(21.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(78.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40(30.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93(69.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.955(0.791\u0026thinsp;~\u0026thinsp;4.831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(16.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54(83.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30(29.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71(70.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(21.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76(78.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33(32.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70(67.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.620(0.767\u0026thinsp;~\u0026thinsp;3.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(18.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77(81.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37(31.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81(68.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.524(1.148\u0026thinsp;~\u0026thinsp;5.552)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(17.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66(82.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24(29.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58(70.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27(23.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89(76.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis further confirmed that PD-L1 positive expression (OR\u0026thinsp;=\u0026thinsp;0.361, 95% CI: 0.161\u0026thinsp;~\u0026thinsp;0.808, P\u0026thinsp;=\u0026thinsp;0.013) and ALBI \u0026lt;-2.68 (OR\u0026thinsp;=\u0026thinsp;2.524, 95% CI: 1.148\u0026thinsp;~\u0026thinsp;5.552, P\u0026thinsp;=\u0026thinsp;0.021) were independent predictors of tumor response. In the subgroup analysis of immunotherapy drugs, compared to the \"other\" category, camrelizumab (OR\u0026thinsp;=\u0026thinsp;0.157, 95% CI: 0.042\u0026thinsp;~\u0026thinsp;0.590, P\u0026thinsp;=\u0026thinsp;0.006), pembrolizumab (OR\u0026thinsp;=\u0026thinsp;0.311, 95% CI: 0.109\u0026thinsp;~\u0026thinsp;0.885, P\u0026thinsp;=\u0026thinsp;0.029), and sintilimab (OR\u0026thinsp;=\u0026thinsp;0.258, 95% CI: 0.070\u0026thinsp;~\u0026thinsp;0.952, P\u0026thinsp;=\u0026thinsp;0.042) showed statistically significant differences in efficacy. Notably, bone metastasis status and PLR, which were significant in univariate analysis, may have been confounded by other variables in multivariate analysis and did not retain statistical significance (P\u0026thinsp;=\u0026thinsp;0.146 and P\u0026thinsp;=\u0026thinsp;0.206, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Analysis of Short-Term Efficacy Factors in Adenocarcinoma Patients\u003c/h2\u003e \u003cp\u003eThis study analyzed 109 adenocarcinoma patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and found that the line of therapy and type of immunotherapy drug were significantly associated with short-term efficacy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Patients receiving second-line therapy had the highest response rate (30.77%), while no patients on third-line therapy achieved response. The pembrolizumab group had a response rate of 50.00%, which was significantly superior to the sintilimab group (12.00%). Multivariate analysis indicated a trend toward poorer efficacy with sintilimab (OR\u0026thinsp;=\u0026thinsp;5.518, P\u0026thinsp;=\u0026thinsp;0.064). Additionally, PD-L1-positive patients had higher response rates compared to the negative group (32.14% vs. 18.52%), and patients without bone metastasis had higher response rates (27.69% vs. 13.64%), but these differences did not reach statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Clinical Characteristics and Short-Term Efficacy in Adenocarcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTumor Non-Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57(76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28(82.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(18.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66(81.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19(67.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(21.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71(78.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(22.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14(77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(20.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71(79.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(18.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66(81.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(32.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19(67.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45(81.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14(25.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40(74.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(22.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28(77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(30.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27(69.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.971(0.309\u0026thinsp;~\u0026thinsp;3.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(22.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14(77.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.061(0.252\u0026thinsp;~\u0026thinsp;4.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(44.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(55.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34(85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.439(0.7\u0026thinsp;~\u0026thinsp;16.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.802(0.127\u0026thinsp;~\u0026thinsp;5.069)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19(76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.608(0.308\u0026thinsp;~\u0026thinsp;8.383)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3(12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22(88.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.518(0.908\u0026thinsp;~\u0026thinsp;33.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(19.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50(80.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(21.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15(78.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(15.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37(84.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(26.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48(73.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(24.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56(75.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29(82.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18(27.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47(72.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6(13.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38(86.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(16.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46(83.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(27.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39(72.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13(20.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49(79.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(23.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36(76.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(23.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51(76.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(19.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34(80.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(27.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31(72.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54(81.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Analysis of Short-Term Efficacy Factors in Squamous Cell Carcinoma Patients\u003c/h2\u003e \u003cp\u003eThis study analyzed 83 squamous cell carcinoma patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and found that ECOG PS score (χ\u0026sup2;=5.784, P\u0026thinsp;=\u0026thinsp;0.016), PD-L1 expression (χ\u0026sup2;=4.122, P\u0026thinsp;=\u0026thinsp;0.042), NLR (χ\u0026sup2;=8.690, P\u0026thinsp;=\u0026thinsp;0.003), PLR (χ\u0026sup2;=10.345, P\u0026thinsp;=\u0026thinsp;0.001), and ALBI score (χ\u0026sup2;=6.350, P\u0026thinsp;=\u0026thinsp;0.012) were significantly associated with short-term efficacy. Specifically, patients with PS scores of 0\u0026ndash;1 had significantly higher response rates than those with PS score of 2 (36.11% vs. 0%), and PD-L1-positive patients had higher response rates than the negative group (53.33% vs. 26.47%). Hematological indicator analysis showed that patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;3.72 (45.45% vs. 15.38%), PLR\u0026thinsp;\u0026lt;\u0026thinsp;207.43 (48.72% vs. 15.91%), and ALBI\u0026lt;-2.68 (42.55% vs. 16.67%) had significantly better response rates. Multivariate analysis indicated that receiving radiotherapy was an independent protective factor (OR\u0026thinsp;=\u0026thinsp;0.298, 95% CI: 0.094\u0026thinsp;~\u0026thinsp;0.95, P\u0026thinsp;=\u0026thinsp;0.041), while other indicators did not retain statistical significance after multivariate adjustment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Clinical Characteristics and Short-Term Efficacy in Squamous Cell Carcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTumor Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTumor Non-Response Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(31.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e51(68.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(37.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e28(62.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(23.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e29(76.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(36.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e46(63.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(35.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24(64.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(28.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33(71.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(26.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e50(73.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.366(0.093\u0026thinsp;~\u0026thinsp;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(53.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7(46.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(29.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53(70.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e30(75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(38.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e19(61.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6(60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8(88.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4(50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(32.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e23(67.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(11.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8(88.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(32.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e47(67.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27(0.025\u0026thinsp;~\u0026thinsp;2.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(23.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33(76.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.298(0.094\u0026thinsp;~\u0026thinsp;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24(60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(30.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e50(69.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(36.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7(63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e44(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(23.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13(76.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24(54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.859(0.367\u0026thinsp;~\u0026thinsp;9.423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33(84.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(48.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20(51.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.667(0.559\u0026thinsp;~\u0026thinsp;12.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(15.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e37(84.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(42.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27(57.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.723(0.84\u0026thinsp;~\u0026thinsp;8.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e30(83.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e24(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(29.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e33(70.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Relationship Between Baseline Peripheral Blood Biomarkers and PFS\u003c/h2\u003e \u003cp\u003eThis study systematically evaluated the predictive value of baseline peripheral blood biomarkers on progression-free survival (PFS) in the entire cohort using Kaplan-Meier survival analysis. The results demonstrated that NLR, PLR, ALBI, and LDH all exhibited significant prognostic stratification capabilities (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, the median PFS in the low NLR group (L-NLR) was 8.7 months (95% CI: 7.1\u0026thinsp;~\u0026thinsp;9.1), significantly superior to 7.1 months in the high NLR group (H-NLR) (P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA); the median PFS in the low PLR group was 7.9 months (95% CI: 6.6\u0026thinsp;~\u0026thinsp;9.2), extending by 0.5 months compared to the high PLR group (P\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The median PFS in the low ALBI group was 8.1 months (95% CI: 7.1\u0026thinsp;~\u0026thinsp;9.1), extending by 1.1 months compared to the high ALBI group (P\u0026thinsp;=\u0026thinsp;0.028) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Notably, LDH demonstrated the strongest predictive efficacy, with the median PFS in the low LDH group reaching 9.5 months (95% CI: 6.7\u0026thinsp;~\u0026thinsp;12.3), reducing the risk by 46.3% compared to the high LDH group (7.1 months, 95% CI: 6.5\u0026thinsp;~\u0026thinsp;7.8) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn adenocarcinoma patients, the predictive patterns of peripheral blood biomarkers exhibited significant heterogeneity. LDH maintained independent prognostic value, with the median PFS in the low LDH group being 9.5 months (95% CI: 6.7\u0026thinsp;~\u0026thinsp;12.3), extending by 2.7 months compared to the high LDH group (P\u0026thinsp;=\u0026thinsp;0.032) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). However, the predictive efficacy of systemic inflammatory biomarkers was generally diminished: although the low NLR group showed a trend toward clinical benefit (8.1 vs. 6.5 months, P\u0026thinsp;=\u0026thinsp;0.051), it did not reach statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE); PLR and ALBI completely lost stratification capability (both P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003eThe predictive patterns of peripheral blood biomarkers in squamous cell carcinoma patients were highly consistent with the entire cohort and demonstrated stronger prognostic associations. The median PFS in the low NLR group reached 9.1 months (95% CI: 7.0\u0026thinsp;~\u0026thinsp;11.2), extending by 1.9 months compared to the high NLR group (P\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI); the prognostic advantage in the low PLR group was more pronounced (10.2 vs. 7.4 months, P\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Additionally, ALBI exhibited unique predictive value in squamous cell carcinoma, with the median PFS in the low score group being 8.7 months (95% CI: 6.5\u0026thinsp;~\u0026thinsp;10.9), significantly superior to the high score group (7.1 months, P\u0026thinsp;=\u0026thinsp;0.022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK). LDH remained the strongest predictor, with the median PFS in the low LDH group extending by 1.6 months compared to the high LDH group (9.1 vs. 7.5 months, P\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship Between Baseline Peripheral Blood Biomarkers and OS\u003c/h2\u003e \u003cp\u003eThis study systematically evaluated the predictive value of baseline peripheral blood biomarkers on overall survival (OS) in the entire cohort using Kaplan-Meier survival analysis. NLR demonstrated prognostic stratification capability, with the median OS in the low NLR group being 21.4 months (95% CI: 20.1\u0026thinsp;~\u0026thinsp;22.6), significantly superior to 17.5 months in the high NLR group (95% CI: 14.5\u0026thinsp;~\u0026thinsp;20.5) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). PLR also exhibited significant predictive value, with the median OS in the low PLR group reaching 20.4 months (95% CI: 19.1\u0026thinsp;~\u0026thinsp;21.8), extending by 2.7 months compared to the high PLR group (P\u0026thinsp;=\u0026thinsp;0.009) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). LDH showed excellent prognostic discrimination, with the median OS in the low LDH group being 21.4 months (95% CI: 19.2\u0026thinsp;~\u0026thinsp;23.5), significantly extended compared to the high LDH group (17.3 months, 95% CI: 14.5\u0026thinsp;~\u0026thinsp;20.2) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reducing the risk by 42.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In contrast, the predictive efficacy of ALBI was relatively weaker (P\u0026thinsp;=\u0026thinsp;0.074) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn adenocarcinoma patients, the predictive patterns of peripheral blood biomarkers presented unique characteristics. NLR maintained significant predictive value, with the median OS in the low NLR group being 21.4 months (95% CI: 19.8\u0026thinsp;~\u0026thinsp;22.9), extending by 4.9 months compared to the high NLR group (P\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). LDH also demonstrated independent prognostic significance, with the median OS in the low LDH group reaching 20.1 months (95% CI: 14.8\u0026thinsp;~\u0026thinsp;25.4), significantly superior to the high LDH group (17.5 months, 95% CI: 14.0\u0026thinsp;~\u0026thinsp;21.1) (P\u0026thinsp;=\u0026thinsp;0.021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). However, PLR (P\u0026thinsp;=\u0026thinsp;0.176) and ALBI (P\u0026thinsp;=\u0026thinsp;0.422) did not exhibit significant stratification capabilities (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003eThe predictive efficacy of peripheral blood biomarkers in squamous cell carcinoma patients was more pronounced. The median OS in the low NLR group reached 21.4 months (95% CI: 18.9\u0026thinsp;~\u0026thinsp;23.8), significantly extended compared to the high NLR group (P\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). The predictive value of PLR was more evident in squamous cell carcinoma, with the median OS in the low PLR group being 21.8 months (95% CI: 19.0\u0026thinsp;~\u0026thinsp;24.6), extending by 1.7 months compared to the high PLR group (P\u0026thinsp;=\u0026thinsp;0.024) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). ALBI also demonstrated predictive capability in squamous cell carcinoma that was not observed in adenocarcinoma, with the median OS in the low score group being 21.4 months (95% CI: 19.2\u0026thinsp;~\u0026thinsp;23.6) (P\u0026thinsp;=\u0026thinsp;0.047) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). Particularly noteworthy is that LDH exhibited the strongest prognostic discrimination in squamous cell carcinoma, with the median OS in the low LDH group reaching as high as 24.6 months (95% CI: 11.7\u0026thinsp;~\u0026thinsp;37.6), extending by 4.8 months compared to the high LDH group (P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Cox Regression Analysis of Factors Associated with PFS\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1 Analysis of Factors Influencing PFS in the Full Cohort\u003c/h2\u003e \u003cp\u003eIn this study, univariate and multivariate Cox proportional hazards models were used to analyze the impact of different variables on PFS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Univariate analysis results showed that PS score (HR\u0026thinsp;=\u0026thinsp;0.595, 95% CI: 0.398\u0026thinsp;~\u0026thinsp;0.889, P\u0026thinsp;=\u0026thinsp;0.011) was a significant predictor of PFS, with better PS scores (0\u0026thinsp;~\u0026thinsp;1) significantly associated with longer PFS. Additionally, the risk for SD patients in short-term efficacy was 2.232 times that of PR patients (95% CI: 1.547\u0026thinsp;~\u0026thinsp;3.221, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the risk for PD patients was 3.5 times that of PR patients (95% CI: 2.226\u0026thinsp;~\u0026thinsp;5.509, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Line of therapy was a partially significant predictor of PFS, with third-line therapy significantly increasing the risk of disease progression or death (HR\u0026thinsp;=\u0026thinsp;1.586, 95% CI: 1.003\u0026thinsp;~\u0026thinsp;2.507, P\u0026thinsp;=\u0026thinsp;0.048), while other lines (second-line, \u0026ge;fourth-line) showed no statistical difference compared to first-line therapy. Camrelizumab significantly increased the risk of disease progression (HR\u0026thinsp;=\u0026thinsp;1.891, 95% CI: 1.107\u0026thinsp;~\u0026thinsp;3.229, P\u0026thinsp;=\u0026thinsp;0.020), while other drugs (pembrolizumab, tislelizumab, sintilimab) showed no statistical difference compared to \"other\" immunotherapy drugs. Patients receiving radiotherapy had a significantly reduced risk of disease progression (HR\u0026thinsp;=\u0026thinsp;0.729, 95% CI: 0.545\u0026thinsp;~\u0026thinsp;0.975, P\u0026thinsp;=\u0026thinsp;0.033). All evaluated hematological function indicators were significantly associated with patient PFS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically: patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 had a significantly increased risk of disease progression (HR\u0026thinsp;=\u0026thinsp;1.647, 95% CI: 1.228\u0026thinsp;~\u0026thinsp;2.208, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); patients with PLR\u0026thinsp;\u0026ge;\u0026thinsp;207.43 had a 49.5% increased risk (HR\u0026thinsp;=\u0026thinsp;1.495, 95% CI: 1.113\u0026thinsp;~\u0026thinsp;2.006, P\u0026thinsp;=\u0026thinsp;0.007). Patients with ALBI index \u0026ge;-2.68 had significantly shorter PFS (HR\u0026thinsp;=\u0026thinsp;1.385, 95% CI: 1.033\u0026thinsp;~\u0026thinsp;1.856, P\u0026thinsp;=\u0026thinsp;0.029). Patients with LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 had the highest progression risk (HR\u0026thinsp;=\u0026thinsp;1.811, 95% CI: 1.336\u0026thinsp;~\u0026thinsp;2.456, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an 81.1% increase in risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing PFS in the Full Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.009 (0.712\u0026thinsp;~\u0026thinsp;1.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.878(0.658\u0026thinsp;~\u0026thinsp;1.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.659 (0.288\u0026thinsp;~\u0026thinsp;1.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.577 (0.250\u0026thinsp;~\u0026thinsp;1.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.595(0.398\u0026thinsp;~\u0026thinsp;0.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.781(0.503\u0026thinsp;~\u0026thinsp;1.212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.955(0.697\u0026thinsp;~\u0026thinsp;1.309)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.232(1.547\u0026thinsp;~\u0026thinsp;3.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.959(1.294\u0026thinsp;~\u0026thinsp;2.964)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.502(2.226\u0026thinsp;~\u0026thinsp;5.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.763(1.624\u0026thinsp;~\u0026thinsp;4.701)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.979(0.689\u0026thinsp;~\u0026thinsp;1.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767(0.521\u0026thinsp;~\u0026thinsp;1.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960 (0.691\u0026thinsp;~\u0026thinsp;1.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.017(0.711\u0026thinsp;~\u0026thinsp;1.453)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.586 (1.003\u0026thinsp;~\u0026thinsp;2.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.207(0.699\u0026thinsp;~\u0026thinsp;2.083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.762(0.458\u0026thinsp;~\u0026thinsp;1.268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.479(0.261\u0026thinsp;~\u0026thinsp;0.878)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.891(1.107\u0026thinsp;~\u0026thinsp;3.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.663(0.921\u0026thinsp;~\u0026thinsp;3.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.069(0.555\u0026thinsp;~\u0026thinsp;2.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.068(0.522\u0026thinsp;~\u0026thinsp;2.185)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.418(0.839\u0026thinsp;~\u0026thinsp;2.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.105(0.632\u0026thinsp;~\u0026thinsp;1.932)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.620(0.945\u0026thinsp;~\u0026thinsp;2.778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.292(0.734\u0026thinsp;~\u0026thinsp;2.273)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.050(0.712\u0026thinsp;~\u0026thinsp;1.548)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.297(0.857\u0026thinsp;~\u0026thinsp;1.964)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.303(0.950\u0026thinsp;~\u0026thinsp;5.579)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.133(0.794\u0026thinsp;~\u0026thinsp;5.729)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.027(0.587\u0026thinsp;~\u0026thinsp;1.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.740(0.907\u0026thinsp;~\u0026thinsp;3.337)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.729(0.545\u0026thinsp;~\u0026thinsp;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.708(0.508\u0026thinsp;~\u0026thinsp;0.986)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.023(0.730\u0026thinsp;~\u0026thinsp;1.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.222 (0.902\u0026thinsp;~\u0026thinsp;1.655)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.647(1.228\u0026thinsp;~\u0026thinsp;2.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.638(1.117\u0026thinsp;~\u0026thinsp;2.402)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.495(1.113\u0026thinsp;~\u0026thinsp;2.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.015(0.694\u0026thinsp;~\u0026thinsp;1.485)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.385(1.033\u0026thinsp;~\u0026thinsp;1.856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.222(0.875\u0026thinsp;~\u0026thinsp;1.706)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.811(1.336\u0026thinsp;~\u0026thinsp;2.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.783(1.275\u0026thinsp;~\u0026thinsp;2.494)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis showed that after adjusting for other confounding factors, treatment efficacy, presence of radiotherapy, NLR, and LDH levels were independent prognostic factors influencing PFS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Efficacy assessment indicated that compared to PR patients, SD (HR\u0026thinsp;=\u0026thinsp;1.959, 95% CI: 1.294\u0026thinsp;~\u0026thinsp;2.964, P\u0026thinsp;=\u0026thinsp;0.001) and PD (HR\u0026thinsp;=\u0026thinsp;2.763, 95% CI: 1.624\u0026thinsp;~\u0026thinsp;4.701, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) patients had increased risks of disease progression by 95.9% and 176.3%, respectively; patients receiving radiotherapy had a significantly reduced risk of disease progression (HR\u0026thinsp;=\u0026thinsp;0.708, 95% CI: 0.508\u0026thinsp;~\u0026thinsp;0.986, P\u0026thinsp;=\u0026thinsp;0.041); patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 had a 63.8% increased progression risk (HR\u0026thinsp;=\u0026thinsp;1.638, 95% CI: 1.117\u0026thinsp;~\u0026thinsp;2.402, P\u0026thinsp;=\u0026thinsp;0.012); patients with LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L had a 78.3% increased progression risk (HR\u0026thinsp;=\u0026thinsp;1.783, 95% CI: 1.275\u0026thinsp;~\u0026thinsp;2.494, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2 Analysis of Factors Influencing PFS in Adenocarcinoma Patients\u003c/h2\u003e \u003cp\u003eThis study found that disease control status, gene mutation status, and LDH levels were significantly associated with PFS (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared to patients with PR, those with SD exhibited a significantly increased risk of disease progression (HR\u0026thinsp;=\u0026thinsp;2.342, 95% CI: 1.395\u0026ndash;3.931, P\u0026thinsp;=\u0026thinsp;0.001), while patients with PD showed an even higher risk (HR\u0026thinsp;=\u0026thinsp;5.312, 95% CI: 2.795\u0026ndash;10.096, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The presence of gene mutations may confer a PFS benefit (HR\u0026thinsp;=\u0026thinsp;0.593, 95% CI: 0.370\u0026ndash;0.948, P\u0026thinsp;=\u0026thinsp;0.029), whereas patients with LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L had a significantly elevated progression risk (HR\u0026thinsp;=\u0026thinsp;1.555, 95% CI: 1.035\u0026ndash;2.334, P\u0026thinsp;=\u0026thinsp;0.033). Additionally, NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 demonstrated borderline significance (HR\u0026thinsp;=\u0026thinsp;1.47, 95% CI: 0.995\u0026ndash;2.172, P\u0026thinsp;=\u0026thinsp;0.053).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing PFS in Adenocarcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.041(0.676\u0026thinsp;~\u0026thinsp;1.603)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.032(0.7\u0026thinsp;~\u0026thinsp;1.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.725(0.433\u0026thinsp;~\u0026thinsp;1.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.099(0.666\u0026thinsp;~\u0026thinsp;1.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.342(1.395\u0026thinsp;~\u0026thinsp;3.931)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.195(1.294\u0026thinsp;~\u0026thinsp;3.723)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.312(2.795\u0026thinsp;~\u0026thinsp;10.096)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.636(2.393\u0026thinsp;~\u0026thinsp;8.981)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.795(0.5\u0026thinsp;~\u0026thinsp;1.265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.593(0.37\u0026thinsp;~\u0026thinsp;0.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.801(0.486\u0026thinsp;~\u0026thinsp;1.322)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881(0.551\u0026thinsp;~\u0026thinsp;1.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.629(0.877\u0026thinsp;~\u0026thinsp;3.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.664(0.366\u0026thinsp;~\u0026thinsp;1.205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.746(0.809\u0026thinsp;~\u0026thinsp;3.769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.056(0.415\u0026thinsp;~\u0026thinsp;2.682)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.264(0.562\u0026thinsp;~\u0026thinsp;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.745(0.778\u0026thinsp;~\u0026thinsp;3.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.048(0.645\u0026thinsp;~\u0026thinsp;1.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.535(0.858\u0026thinsp;~\u0026thinsp;7.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.058(0.569\u0026thinsp;~\u0026thinsp;1.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.738(0.497\u0026thinsp;~\u0026thinsp;1.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94(0.62\u0026thinsp;~\u0026thinsp;1.423)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.267(0.853\u0026thinsp;~\u0026thinsp;1.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47(0.995\u0026thinsp;~\u0026thinsp;2.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.184(0.8\u0026thinsp;~\u0026thinsp;1.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.251(0.841\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.555(1.035\u0026thinsp;~\u0026thinsp;2.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.233(0.797\u0026thinsp;~\u0026thinsp;1.908)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment using a multivariate Cox regression model, disease control status retained independent predictive value. The progression risk for SD patients was 2.195 times that of PR patients (95% CI: 1.294\u0026ndash;3.723, P\u0026thinsp;=\u0026thinsp;0.004), and for PD patients, it was as high as 4.636 times (95% CI: 2.393\u0026ndash;8.981, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, gene mutations (P\u0026thinsp;=\u0026thinsp;0.386) and LDH levels (P\u0026thinsp;=\u0026thinsp;0.347), which were significant in univariate analysis, did not maintain statistical significance in the multivariate model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.6.3 Analysis of Factors Influencing PFS in Squamous Cell Carcinoma Patients\u003c/h2\u003e \u003cp\u003eThis study indicates that performance status (PS score), serum lactate dehydrogenase (LDH) levels, and disease control status are independent prognostic factors influencing progression-free survival (PFS) in patients with squamous cell carcinoma (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Univariate analysis revealed that patients with a PS score of 0\u0026ndash;1 had a significantly lower risk of disease progression compared to those with a PS score of 2 (HR\u0026thinsp;=\u0026thinsp;0.336, 95% CI: 0.169\u0026ndash;0.67, P\u0026thinsp;=\u0026thinsp;0.002); LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L (HR\u0026thinsp;=\u0026thinsp;2.098, 95% CI: 1.293\u0026ndash;3.404, P\u0026thinsp;=\u0026thinsp;0.003) and stable disease status (SD, HR\u0026thinsp;=\u0026thinsp;2.276, 95% CI: 1.323\u0026ndash;3.918, P\u0026thinsp;=\u0026thinsp;0.003) were significantly associated with poorer PFS. Additionally, hematological indicators including NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 (HR\u0026thinsp;=\u0026thinsp;2.083, P\u0026thinsp;=\u0026thinsp;0.002), PLR\u0026thinsp;\u0026ge;\u0026thinsp;207.43 (HR\u0026thinsp;=\u0026thinsp;2.115, P\u0026thinsp;=\u0026thinsp;0.002), and ALBI score (\u0026ge;-2.68, HR\u0026thinsp;=\u0026thinsp;1.695, P\u0026thinsp;=\u0026thinsp;0.024) also demonstrated statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing PFS in Squamous Cell Carcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.001(0.497\u0026thinsp;~\u0026thinsp;2.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.645(0.409\u0026thinsp;~\u0026thinsp;1.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336(0.169\u0026thinsp;~\u0026thinsp;0.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464(0.221\u0026thinsp;~\u0026thinsp;0.971)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881(0.561\u0026thinsp;~\u0026thinsp;1.384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.276(1.323\u0026thinsp;~\u0026thinsp;3.918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.856(1.015\u0026thinsp;~\u0026thinsp;3.392)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.345(1.182\u0026thinsp;~\u0026thinsp;4.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.912(0.947\u0026thinsp;~\u0026thinsp;3.861)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743(0.417\u0026thinsp;~\u0026thinsp;1.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.387(0.661\u0026thinsp;~\u0026thinsp;2.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889(0.547\u0026thinsp;~\u0026thinsp;1.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.300(0.624\u0026thinsp;~\u0026thinsp;2.709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818(0.196\u0026thinsp;~\u0026thinsp;3.415)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.912(0.780\u0026thinsp;~\u0026thinsp;4.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.095(0.424\u0026thinsp;~\u0026thinsp;2.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.533(0.767\u0026thinsp;~\u0026thinsp;3.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.515(0.715\u0026thinsp;~\u0026thinsp;3.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.961(0.455\u0026thinsp;~\u0026thinsp;2.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.770(0.367\u0026thinsp;~\u0026thinsp;8.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.659(0.139\u0026thinsp;~\u0026thinsp;3.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.687(0.436\u0026thinsp;~\u0026thinsp;1.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.066(0.560\u0026thinsp;~\u0026thinsp;2.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.043(0.589\u0026thinsp;~\u0026thinsp;1.846)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.083(1.301\u0026thinsp;~\u0026thinsp;3.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14(0.592\u0026thinsp;~\u0026thinsp;2.193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.115(1.303\u0026thinsp;~\u0026thinsp;3.434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.293(0.647\u0026thinsp;~\u0026thinsp;2.585)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.695(1.071\u0026thinsp;~\u0026thinsp;2.684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36(0.825\u0026thinsp;~\u0026thinsp;2.242)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.098(1.293\u0026thinsp;~\u0026thinsp;3.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.909(1.121\u0026thinsp;~\u0026thinsp;3.252)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment using a multivariate Cox proportional hazards model, PS score (HR\u0026thinsp;=\u0026thinsp;0.464, 95% CI: 0.221\u0026ndash;0.971, P\u0026thinsp;=\u0026thinsp;0.042), LDH levels (HR\u0026thinsp;=\u0026thinsp;1.909, 95% CI: 1.121\u0026ndash;3.252, P\u0026thinsp;=\u0026thinsp;0.017), and SD status (HR\u0026thinsp;=\u0026thinsp;1.856, 95% CI: 1.015\u0026ndash;3.392, P\u0026thinsp;=\u0026thinsp;0.045) retained independent predictive value, whereas the significance of other hematological indicators was lost.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Cox Regression Analysis of Factors Associated with OS\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.7.1 Analysis of Factors Influencing OS in the Full Cohort\u003c/h2\u003e \u003cp\u003eIn this study, we employed univariate and multivariate Cox proportional hazards models to analyze the impact of various variables on overall survival (OS) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Univariate analysis revealed that histological type (with other types as the reference): patients with squamous cell carcinoma exhibited a significant 62.6% reduction in mortality risk (HR\u0026thinsp;=\u0026thinsp;0.374, 95% CI: 0.161\u0026ndash;0.867, P\u0026thinsp;=\u0026thinsp;0.022), while those with adenocarcinoma showed a borderline trend toward reduced risk (HR\u0026thinsp;=\u0026thinsp;0.468, 95% CI: 0.204\u0026ndash;1.073, P\u0026thinsp;=\u0026thinsp;0.073), without achieving statistical significance. Patients with a PS score of 0\u0026ndash;1 had a 47.7% lower mortality risk compared to those with a PS score of 2 (HR\u0026thinsp;=\u0026thinsp;0.523, 95% CI: 0.349\u0026ndash;0.783, P\u0026thinsp;=\u0026thinsp;0.002). Regarding short-term treatment efficacy, significant differences in OS were observed among patients with different responses (with PR as the reference group); both SD and PD patients demonstrated significantly elevated mortality risks (SD: HR\u0026thinsp;=\u0026thinsp;2.046, 95% CI: 1.392\u0026ndash;3.006, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; PD: HR\u0026thinsp;=\u0026thinsp;2.998, 95% CI: 1.870\u0026ndash;4.806, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with bone metastases exhibited poorer OS (HR\u0026thinsp;=\u0026thinsp;1.376, 95% CI: 1.004\u0026ndash;1.884, P\u0026thinsp;=\u0026thinsp;0.047). Inflammation-related indicators and liver function metabolic markers were significantly associated with patient OS: patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 had a 76.8% increased mortality risk (HR\u0026thinsp;=\u0026thinsp;1.768, 95% CI: 1.308\u0026ndash;2.388, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), those with PLR\u0026thinsp;\u0026ge;\u0026thinsp;207.43 had a 48.4% increased risk (HR\u0026thinsp;=\u0026thinsp;1.484, 95% CI: 1.100-2.000, P\u0026thinsp;=\u0026thinsp;0.010); meanwhile, patients with LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L showed a significant 89.9% increase in mortality risk (HR\u0026thinsp;=\u0026thinsp;1.899, 95% CI: 1.388\u0026ndash;2.599, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas those with ALBI \u0026ge;-2.68 displayed a trend toward increased risk (HR\u0026thinsp;=\u0026thinsp;1.314, 95% CI: 0.972\u0026ndash;1.775) that did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.076).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing OS in the Full Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.227(0.849\u0026thinsp;~\u0026thinsp;1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.777(0.578\u0026thinsp;~\u0026thinsp;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.468(0.204\u0026thinsp;~\u0026thinsp;1.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.343(0.137\u0026thinsp;~\u0026thinsp;0.858)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.374(0.161\u0026thinsp;~\u0026thinsp;0.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312(0.125\u0026thinsp;~\u0026thinsp;0.778)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.523(0.349\u0026thinsp;~\u0026thinsp;0.783)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607(0.394\u0026thinsp;~\u0026thinsp;0.935)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.989(0.716\u0026thinsp;~\u0026thinsp;1.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.046(1.392\u0026thinsp;~\u0026thinsp;3.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.715(1.113\u0026thinsp;~\u0026thinsp;2.643)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.998(1.870\u0026thinsp;~\u0026thinsp;4.806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.536(1.498\u0026thinsp;~\u0026thinsp;4.293)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935(0.650\u0026thinsp;~\u0026thinsp;1.346)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.692(0.459\u0026thinsp;~\u0026thinsp;1.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.037(0.661\u0026thinsp;~\u0026thinsp;1.628)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873(0.620\u0026thinsp;~\u0026thinsp;1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.081(0.679\u0026thinsp;~\u0026thinsp;1.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833(0.494\u0026thinsp;~\u0026thinsp;1.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.647(0.954\u0026thinsp;~\u0026thinsp;2.842)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23(0.667\u0026thinsp;~\u0026thinsp;2.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.982(0.493\u0026thinsp;~\u0026thinsp;1.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12(0.542\u0026thinsp;~\u0026thinsp;2.313)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.152(0.672\u0026thinsp;~\u0026thinsp;1.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.974(0.556\u0026thinsp;~\u0026thinsp;1.705)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.518(0.874\u0026thinsp;~\u0026thinsp;2.636)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.021(0.565\u0026thinsp;~\u0026thinsp;1.847)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.858(0.578\u0026thinsp;~\u0026thinsp;1.275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.983(0.819\u0026thinsp;~\u0026thinsp;4.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843(0.473\u0026thinsp;~\u0026thinsp;1.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800(0.593\u0026thinsp;~\u0026thinsp;1.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.102(0.779\u0026thinsp;~\u0026thinsp;1.561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.376(1.004\u0026thinsp;~\u0026thinsp;1.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.167(0.818\u0026thinsp;~\u0026thinsp;1.664)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.768(1.308\u0026thinsp;~\u0026thinsp;2.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.629(1.099\u0026thinsp;~\u0026thinsp;2.415)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.484(1.100\u0026thinsp;~\u0026thinsp;2.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.965(0.656\u0026thinsp;~\u0026thinsp;1.419)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.314(0.972\u0026thinsp;~\u0026thinsp;1.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.091(0.792\u0026thinsp;~\u0026thinsp;1.504)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.899(1.388\u0026thinsp;~\u0026thinsp;2.599)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.826(1.295\u0026thinsp;~\u0026thinsp;2.574)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariate Cox regression analysis indicated that, after adjusting for other confounding factors, lung cancer histological type, PS score, treatment efficacy, NLR, and LDH levels remained independent prognostic factors for OS (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to other types, patients with adenocarcinoma (HR\u0026thinsp;=\u0026thinsp;0.343, 95% CI: 0.137\u0026ndash;0.858, P\u0026thinsp;=\u0026thinsp;0.022) and squamous cell carcinoma (HR\u0026thinsp;=\u0026thinsp;0.312, 95% CI: 0.125\u0026ndash;0.778, P\u0026thinsp;=\u0026thinsp;0.012) had significantly reduced mortality risks by 65.7% and 68.8%, respectively; PS score (HR\u0026thinsp;=\u0026thinsp;0.607, 95% CI: 0.394\u0026ndash;0.935, P\u0026thinsp;=\u0026thinsp;0.024) was an important predictor of OS; regarding treatment efficacy, with PR as the reference, SD (HR\u0026thinsp;=\u0026thinsp;1.715, 95% CI: 1.113\u0026ndash;2.643, P\u0026thinsp;=\u0026thinsp;0.014) and PD (HR\u0026thinsp;=\u0026thinsp;2.536, 95% CI: 1.498\u0026ndash;4.293, P\u0026thinsp;=\u0026thinsp;0.001) patients had increased mortality risks by 71.5% and 153.6%, respectively; NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 (HR\u0026thinsp;=\u0026thinsp;1.629, 95% CI: 1.099\u0026ndash;2.415, P\u0026thinsp;=\u0026thinsp;0.015) and LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L (HR\u0026thinsp;=\u0026thinsp;1.826, 95% CI: 1.295\u0026ndash;2.574, P\u0026thinsp;=\u0026thinsp;0.001) were associated with 62.9% and 82.6% increases in mortality risk, respectively. However, PLR (P\u0026thinsp;=\u0026thinsp;0.856) and ALBI (P\u0026thinsp;=\u0026thinsp;0.594) did not demonstrate statistical significance in the multivariate analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.7.2 Analysis of Factors Influencing OS in Adenocarcinoma Patients\u003c/h2\u003e \u003cp\u003eIn the adenocarcinoma patient cohort, univariate Cox regression analysis revealed that multiple clinical features were significantly associated with overall survival (OS) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Disease control status demonstrated a strong association, with SD patients exhibiting an 86% increased mortality risk compared to PR patients (HR\u0026thinsp;=\u0026thinsp;1.860, 95% CI: 1.073\u0026ndash;3.073, P\u0026thinsp;=\u0026thinsp;0.026), and PD patients showing an even higher risk (HR\u0026thinsp;=\u0026thinsp;3.595, 95% CI: 1.907\u0026ndash;6.776, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding hematological indicators, both NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 (HR\u0026thinsp;=\u0026thinsp;1.651, 95% CI: 1.107\u0026ndash;2.461, P\u0026thinsp;=\u0026thinsp;0.014) and LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L (HR\u0026thinsp;=\u0026thinsp;1.632, 95% CI: 1.073\u0026ndash;2.482, P\u0026thinsp;=\u0026thinsp;0.022) exhibited statistical significance. Additionally, bone metastasis status displayed borderline significance (HR\u0026thinsp;=\u0026thinsp;1.447, 95% CI: 0.964\u0026ndash;2.171, P\u0026thinsp;=\u0026thinsp;0.075).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing OS in Adenocarcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.306(0.840\u0026thinsp;~\u0026thinsp;2.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954 (0.642\u0026thinsp;~\u0026thinsp;1.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.661(0.395\u0026thinsp;~\u0026thinsp;1.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.163(0.704\u0026thinsp;~\u0026thinsp;1.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.860(1.073\u0026thinsp;~\u0026thinsp;3.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.916(1.098\u0026thinsp;~\u0026thinsp;3.342)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.595(1.907\u0026thinsp;~\u0026thinsp;6.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.410(1.801\u0026thinsp;~\u0026thinsp;6.457)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.696(0.431\u0026thinsp;~\u0026thinsp;1.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.671(0.417\u0026thinsp;~\u0026thinsp;1.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.920(0.575\u0026thinsp;~\u0026thinsp;1.472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884(0.473\u0026thinsp;~\u0026thinsp;1.652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.705(0.382\u0026thinsp;~\u0026thinsp;1.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.395(0.644\u0026thinsp;~\u0026thinsp;3.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.025(0.394\u0026thinsp;~\u0026thinsp;2.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.048(0.464\u0026thinsp;~\u0026thinsp;2.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.762(0.780\u0026thinsp;~\u0026thinsp;3.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.823(0.505\u0026thinsp;~\u0026thinsp;1.341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.495(0.511\u0026thinsp;~\u0026thinsp;4.378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856 (0.454\u0026thinsp;~\u0026thinsp;1.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.828(0.554\u0026thinsp;~\u0026thinsp;1.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.096 (0.718\u0026thinsp;~\u0026thinsp;1.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.447(0.964\u0026thinsp;~\u0026thinsp;2.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.180(0.760\u0026thinsp;~\u0026thinsp;1.830)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.651(1.107\u0026thinsp;~\u0026thinsp;2.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.572(1.019\u0026thinsp;~\u0026thinsp;2.426)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.315(0.883\u0026thinsp;~\u0026thinsp;1.958)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.178(0.789\u0026thinsp;~\u0026thinsp;1.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.632(1.073\u0026thinsp;~\u0026thinsp;2.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.481(0.966\u0026thinsp;~\u0026thinsp;2.272)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing multivariate adjustment, disease control status retained independent predictive value. The mortality risk for SD patients was 1.916 times that of PR patients (95% CI: 1.098\u0026ndash;3.342, P\u0026thinsp;=\u0026thinsp;0.022), while for PD patients, it reached 3.410 times (95% CI: 1.801\u0026ndash;6.457, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 remained significant (HR\u0026thinsp;=\u0026thinsp;1.572, 95% CI: 1.019\u0026ndash;2.426, P\u0026thinsp;=\u0026thinsp;0.041), whereas LDH levels were reduced to borderline significance (HR\u0026thinsp;=\u0026thinsp;1.481, P\u0026thinsp;=\u0026thinsp;0.071). Notably, bone metastasis, which was significant in the univariate analysis, did not maintain significance in the multivariate model (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.7.3 Analysis of Factors Influencing OS in Squamous Cell Carcinoma Patients\u003c/h2\u003e \u003cp\u003eUnivariate analysis in squamous cell carcinoma patients revealed distinct prognostic features (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Age\u0026thinsp;\u0026lt;\u0026thinsp;65 years (HR\u0026thinsp;=\u0026thinsp;0.570, 95% CI: 0.355\u0026thinsp;~\u0026thinsp;0.917, P\u0026thinsp;=\u0026thinsp;0.020) and PS score of 0\u0026ndash;1 (HR\u0026thinsp;=\u0026thinsp;0.319, 95% CI: 0.163\u0026thinsp;~\u0026thinsp;0.626, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with better OS. In terms of disease control, SD (HR\u0026thinsp;=\u0026thinsp;2.421, 95% CI: 1.334\u0026thinsp;~\u0026thinsp;4.392, P\u0026thinsp;=\u0026thinsp;0.004) and PD (HR\u0026thinsp;=\u0026thinsp;2.409, 95% CI: 1.131\u0026thinsp;~\u0026thinsp;5.128, P\u0026thinsp;=\u0026thinsp;0.023) were significantly worse than PR. Hematological indicators all showed significant associations: patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72 had an 89.9% increased mortality risk (HR\u0026thinsp;=\u0026thinsp;1.899, 95% CI: 1.176\u0026thinsp;~\u0026thinsp;3.068, P\u0026thinsp;=\u0026thinsp;0.009), PLR\u0026thinsp;\u0026ge;\u0026thinsp;207.43 had a 72.9% increased risk (HR\u0026thinsp;=\u0026thinsp;1.729, 95% CI: 1.068\u0026thinsp;~\u0026thinsp;2.796, P\u0026thinsp;=\u0026thinsp;0.026), ALBI \u0026ge;-2.68 had a 62.9% increased risk (HR\u0026thinsp;=\u0026thinsp;1.629, 95% CI: 1.002\u0026thinsp;~\u0026thinsp;2.648, P\u0026thinsp;=\u0026thinsp;0.049), and LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L had a significantly increased risk of 230.1% (HR\u0026thinsp;=\u0026thinsp;2.301, 95% CI: 1.393\u0026thinsp;~\u0026thinsp;3.801, P\u0026thinsp;=\u0026thinsp;0.001). Notably, the immuno\u0026thinsp;+\u0026thinsp;targeted therapy regimen showed an extremely high mortality risk (HR\u0026thinsp;=\u0026thinsp;4.652, 95% CI: 0.925\u0026thinsp;~\u0026thinsp;23.399), but did not reach statistical significance (P\u0026thinsp;=\u0026thinsp;0.062).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate Cox Analysis of Factors Influencing OS in Squamous Cell Carcinoma Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate Analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.276(0.581\u0026thinsp;~\u0026thinsp;2.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(\u0026lt;65/\u0026ge;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.570(0.355\u0026thinsp;~\u0026thinsp;0.917)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.794(0.446\u0026thinsp;~\u0026thinsp;1.415)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS Score(0\u0026thinsp;~\u0026thinsp;1/2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.319(0.163\u0026thinsp;~\u0026thinsp;0.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.632(0.290\u0026thinsp;~\u0026thinsp;1.376)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Stage(Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.965(0.602\u0026thinsp;~\u0026thinsp;1.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEfficacy Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.421(1.334\u0026thinsp;~\u0026thinsp;4.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.103(1.048\u0026thinsp;~\u0026thinsp;4.218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.409 (1.131\u0026thinsp;~\u0026thinsp;5.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.650(1.14\u0026thinsp;~\u0026thinsp;6.159)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD-L1 Expression (Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.379(0.762\u0026thinsp;~\u0026thinsp;2.496)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene Mutation(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.597(0.240\u0026thinsp;~\u0026thinsp;1.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLine of Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.737(0.434\u0026thinsp;~\u0026thinsp;1.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.219(0.582\u0026thinsp;~\u0026thinsp;2.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.087(0.260\u0026thinsp;~\u0026thinsp;4.551)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy Drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.118(0.833\u0026thinsp;~\u0026thinsp;5.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.952(0.342\u0026thinsp;~\u0026thinsp;2.647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.234(0.599\u0026thinsp;~\u0026thinsp;2.545)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.279(0.588\u0026thinsp;~\u0026thinsp;2.784)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment Regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.019(0.465\u0026thinsp;~\u0026thinsp;2.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.671(0.708\u0026thinsp;~\u0026thinsp;3.946)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.652(0.925\u0026thinsp;~\u0026thinsp;23.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.632(2.0\u0026thinsp;~\u0026thinsp;79.783)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmuno\u0026thinsp;+\u0026thinsp;Chemo\u0026thinsp;+\u0026thinsp;Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.349(0.045\u0026thinsp;~\u0026thinsp;2.987)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.377(0.146\u0026thinsp;~\u0026thinsp;12.976)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.682(0.423\u0026thinsp;~\u0026thinsp;1.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.039(0.514\u0026thinsp;~\u0026thinsp;2.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone Metastasis(Present/Absent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.050(0.584\u0026thinsp;~\u0026thinsp;1.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR(\u0026ge;3.72/\u0026lt;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.899(1.176\u0026thinsp;~\u0026thinsp;3.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.335(0.657\u0026thinsp;~\u0026thinsp;2.714)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR(\u0026ge;207.43/\u0026lt;207.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.729(1.068\u0026thinsp;~\u0026thinsp;2.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.739(0.350\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI(\u0026ge;-2.68/\u0026lt;-2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.629(1.002\u0026thinsp;~\u0026thinsp;2.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.306(0.760\u0026thinsp;~\u0026thinsp;2.245)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(\u0026ge;226.50/\u0026lt;226.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.301(1.393\u0026thinsp;~\u0026thinsp;3.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.542(1.398\u0026thinsp;~\u0026thinsp;4.623)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe multivariate model showed that disease control status retained independent predictive value, with SD (HR\u0026thinsp;=\u0026thinsp;2.103, P\u0026thinsp;=\u0026thinsp;0.036) and PD (HR\u0026thinsp;=\u0026thinsp;2.650, P\u0026thinsp;=\u0026thinsp;0.023) both significant. LDH\u0026thinsp;\u0026ge;\u0026thinsp;226.50 U/L was an independent prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;2.542, 95% CI: 1.398\u0026thinsp;~\u0026thinsp;4.623, P\u0026thinsp;=\u0026thinsp;0.002). Surprisingly, the immuno\u0026thinsp;+\u0026thinsp;targeted therapy regimen's mortality risk further increased to 12.632 times (95% CI: 2\u0026thinsp;~\u0026thinsp;79.783, P\u0026thinsp;=\u0026thinsp;0.007). However, age, PS score, and inflammatory indicators that were significant in univariate analysis did not retain independence (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Analysis of Immune-Related Adverse Events\u003c/h2\u003e \u003cp\u003eThis study conducted a systematic analysis of irAEs in 198 patients with advanced NSCLC receiving immunotherapy (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). The results showed that 92 patients (46.46%) experienced irAEs of varying degrees, with hematological toxicities being the most common adverse reaction types, including anemia (41.41%) and neutropenia (40.91%), with grade III and above severe events accounting for 10.10% and 9.09%, respectively. Non-hematological toxicities were primarily gastrointestinal reactions (nausea/vomiting 32.32%, abdominal pain/diarrhea 25.25%) and dermatological toxicities (rash 23.74%, pruritus 25.25%), but severe events (grade III and above) were rare (all \u0026le;\u0026thinsp;2.53%). Notably, the overall incidence of immune-related pneumonia was 7.07%, with 42.9% (6/14) being grade III and above events. In terms of endocrine toxicities, the incidence of thyroid dysfunction was 21.21%, with 16.7% (7/42) being grade III and above events.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eirAEs in Advanced NSCLC Patients Receiving Immunotherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse Event\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade I-II (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade III and Above (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Incidence (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69(34.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92(46.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea/Vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (32.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (32.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal Pain/Diarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (25.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (25.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (23.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (23.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePruritus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (22.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (25.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (31.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (10.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (41.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutropenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (31.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (40.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombocytopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (14.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (18.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Function Abnormality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (17.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (4.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (22.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune-Related Pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (7.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune-Related Myocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid Dysfunction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (17.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (21.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther analysis of the association between hematological indicators and irAEs revealed that NLR and LDH were significantly correlated with irAEs risk (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The irAEs incidence in the low NLR group (\u0026lt;\u0026thinsp;3.72) was significantly higher than in the high NLR group (55.4% vs. 37.1%, P\u0026thinsp;=\u0026thinsp;0.010), and patients with low NLR had nearly twice the risk of developing irAEs (OR\u0026thinsp;=\u0026thinsp;1.976, 95% CI: 1.098\u0026thinsp;~\u0026thinsp;3.557, P\u0026thinsp;=\u0026thinsp;0.023). Similarly, the irAEs incidence in the low LDH group (\u0026lt;\u0026thinsp;226.50 U/L) was significantly higher than in the high LDH group (62.2% vs. 35.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the risk increased nearly threefold (OR\u0026thinsp;=\u0026thinsp;2.881, 95% CI: 1.590\u0026thinsp;~\u0026thinsp;5.223, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, PLR and ALBI showed no significant statistical association with irAEs (P\u0026thinsp;=\u0026thinsp;0.143 and P\u0026thinsp;=\u0026thinsp;0.528).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Hematological Indicators and irAEs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHematological Indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eirAEs Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-irAEs Group n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.976(1.098\u0026thinsp;~\u0026thinsp;3.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;207.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57(48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61(51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.881(1.590\u0026thinsp;~\u0026thinsp;5.223)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;226.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75(64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAlthough the precision of NSCLC treatment has significantly improved in recent years, it remains challenging. The emergence of PD-1 inhibitors has provided new hope for advanced NSCLC patients, but many clinical studies indicate that only a minority benefit [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, identifying effective predictive biomarkers to screen potential beneficiaries is urgent. This study explored the relationship between baseline peripheral blood biomarkers (NLR, PLR, ALBI, LDH) and short-term efficacy, PFS, and OS in advanced NSCLC patients, further analyzing their prognostic value. Our results show that NLR, PLR, ALBI, and LDH have significant roles in predicting PFS and OS, and combined analysis may enhance their clinical utility. Additionally, this study revealed significant pathological subtype (squamous vs. adenocarcinoma) influences on biomarker predictive efficacy, providing new perspectives for individualized stratification.\u003c/p\u003e \u003cp\u003eThis study, by analyzing baseline blood biomarkers in advanced NSCLC patients, found that NLR, PLR, ALBI, and LDH have significant meaning in predicting efficacy and survival. ROC curve analysis showed AUC values for NLR, PLR, ALBI, and LDH of 0.804, 0.694, 0.684, and 0.726 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with NLR and LDH having the best predictive efficacy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.70), suggesting that systemic inflammation (NLR/PLR) and tumor metabolic activity (LDH) may jointly drive immunotherapy response differences.\u003c/p\u003e \u003cp\u003eThis study further clarified key factors influencing immunotherapy short-term efficacy (ORR). Multivariate logistic regression showed PD-L1 positive expression (OR\u0026thinsp;=\u0026thinsp;0.361, P\u0026thinsp;=\u0026thinsp;0.013) and ALBI\u0026lt;-2.68 (OR\u0026thinsp;=\u0026thinsp;2.524, P\u0026thinsp;=\u0026thinsp;0.021) as independent predictors of objective response, suggesting synergistic roles of tumor immunogenicity and liver function metabolic status in determining early treatment response. Notably, although PD-L1-positive patients had significantly higher ORR (40.91% vs. 21.43%), their protective effect in multivariate models (OR\u0026thinsp;\u0026lt;\u0026thinsp;1) may reflect the dynamic complexity of PD-L1 expression\u0026mdash;some positive patients have weakened benefits due to high tumor burden or immunosuppressive microenvironments. The predictive value of PLR\u0026thinsp;\u0026lt;\u0026thinsp;207.43 (OR\u0026thinsp;=\u0026thinsp;1.892, P\u0026thinsp;=\u0026thinsp;0.035) and low ALBI score (OR\u0026thinsp;=\u0026thinsp;2.524, P\u0026thinsp;=\u0026thinsp;0.021) further supports the key roles of systemic inflammation and liver function in early immune activation.\u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis further revealed that lower NLR (\u0026lt;\u0026thinsp;3.72), PLR (\u0026lt;\u0026thinsp;207.43), ALBI (\u0026lt;-2.68), and LDH (\u0026lt;\u0026thinsp;226.5 U/L) levels were associated with longer PFS and OS. Among them, LDH's stratification ability was most prominent, with low-level group median PFS extended by 2.4 months (9.5 vs. 7.1 months), and OS risk reduced by 82.6% (HR\u0026thinsp;=\u0026thinsp;1.826), consistent with LDH-mediated lactate metabolism inhibiting T cell function and promoting immune escape. Further subgroup analysis showed that biomarker predictive value has significant pathological type dependency: in squamous carcinoma patients, low NLR (\u0026lt;\u0026thinsp;3.72) and low PLR (\u0026lt;\u0026thinsp;207.43) extended median PFS by 1.9 months (P\u0026thinsp;=\u0026thinsp;0.002) and 2.8 months (P\u0026thinsp;=\u0026thinsp;0.002), respectively, while in adenocarcinoma, only LDH retained independent prognostic value (P\u0026thinsp;=\u0026thinsp;0.032). This difference may stem from stronger inflammation-driven features in squamous carcinoma microenvironments (e.g., high IL-6/TNF-α expression [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]), while adenocarcinoma's metabolic heterogeneity (e.g., EGFR/KRAS mutation-related glycolysis reprogramming [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]) may weaken inflammatory marker predictive efficacy. Additionally, ALBI score's independent prognostic role in PFS and OS was validated, with lower ALBI scores (\u0026lt;-2.68) associated with longer survival, suggesting liver function not only affects drug metabolism and clearance but may also indirectly enhance immune response by regulating systemic inflammation balance (e.g., albumin maintaining vascular permeability, bilirubin antioxidative stress).\u003c/p\u003e \u003cp\u003eInflammatory status is closely related to the prognosis of various cancers [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and NLR and PLR evaluated in this study are good biomarkers reflecting inflammatory status. In a clinical study by Bagley et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], analysis of 175 advanced NSCLC patients treated with nivolumab showed baseline NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 as a poor prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;1.83, 95% CI:1.2\u0026thinsp;~\u0026thinsp;2.8, P\u0026thinsp;=\u0026thinsp;0.006) and an independent predictor for PFS (HR\u0026thinsp;=\u0026thinsp;1.42, 95% CI:1.02\u0026thinsp;~\u0026thinsp;2.0, P\u0026thinsp;=\u0026thinsp;0.04). Another retrospective study also indicated that baseline NLR\u0026thinsp;\u0026gt;\u0026thinsp;5 was closely related to poor OS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A meta-analysis by Cao [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] et al. included 14 retrospective studies involving 1225 NSCLC patients, exploring the predictive value of baseline and post-treatment NLR for nivolumab efficacy. Pooled results showed higher baseline NLR associated with poorer PFS (HR\u0026thinsp;=\u0026thinsp;1.44, 95% CI:1.18\u0026thinsp;~\u0026thinsp;1.77, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OS (HR\u0026thinsp;=\u0026thinsp;1.75, 95% CI:1.33\u0026thinsp;~\u0026thinsp;2.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subgroup analysis further indicated NLR\u0026thinsp;\u0026ge;\u0026thinsp;5 was more reliable for predicting PFS (HR\u0026thinsp;=\u0026thinsp;1.73, 95% CI:1.14\u0026thinsp;~\u0026thinsp;2.62, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OS (HR\u0026thinsp;=\u0026thinsp;1.76, 95% CI:1.47\u0026thinsp;~\u0026thinsp;2.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, post-treatment NLR elevation was associated with poorer PFS (HR\u0026thinsp;=\u0026thinsp;3.17, 95% CI:1.48\u0026thinsp;~\u0026thinsp;6.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OS (HR\u0026thinsp;=\u0026thinsp;2.26, 95% CI:1.05\u0026thinsp;~\u0026thinsp;4.86, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggest baseline and post-treatment NLR as potential prognostic markers for NSCLC patients receiving nivolumab. Zhou [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] et al.'s meta-analysis included 21 studies covering 2312 advanced lung cancer patients receiving immunotherapy. Pooled analysis showed elevated PLR associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;2.24, 95% CI:1.87\u0026thinsp;~\u0026thinsp;2.68, I\u0026sup2;=44%, P\u0026thinsp;=\u0026thinsp;0.01) and PFS (HR\u0026thinsp;=\u0026thinsp;1.66, 95% CI:1.36\u0026thinsp;~\u0026thinsp;2.04; I\u0026sup2;=64%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Zhang Na [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] et al. included 1845 NSCLC patients from 21 studies, with overall analysis showing high NLR significantly associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;2.50, 95% CI:1.79\u0026thinsp;~\u0026thinsp;3.51, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;1.77, 95% CI:1.51\u0026thinsp;~\u0026thinsp;2.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup results were consistent. Similarly, pooled PLR results showed elevated PLR associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;1.93, 95% CI:1.51\u0026thinsp;~\u0026thinsp;2.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;1.57, 95% CI:1.30\u0026thinsp;~\u0026thinsp;1.90, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In our study, NLR was an independent prognostic factor for OS and PFS, with higher NLR (NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.72) significantly associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;1.629, 95% CI:1.099\u0026thinsp;~\u0026thinsp;2.415, P\u0026thinsp;=\u0026thinsp;0.015) and PFS (HR\u0026thinsp;=\u0026thinsp;1.638, 95% CI:1.117\u0026thinsp;~\u0026thinsp;2.402, P\u0026thinsp;=\u0026thinsp;0.012). Similarly, high baseline PLR group showed poorer OS and PFS, but not significantly in multivariate analysis.\u003c/p\u003e \u003cp\u003eLDH is a metabolic enzyme secreted by proliferative tumor cells, with serum levels often used as a biomarker for tumor burden. Multiple studies show LDH significantly correlates with prognosis in melanoma patients receiving immune checkpoint inhibitors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Reports have explored LDH in predicting PFS [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and OS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] in ICIs-treated NSCLC patients. In Taniguchi et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]'s retrospective study of 201 advanced NSCLC patients receiving nivolumab, baseline LDH\u0026thinsp;\u0026gt;\u0026thinsp;240 U/L was significantly associated with poorer PFS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In L. Mezquita [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]'s study, patients with LDH exceeding the upper normal limit had HR\u0026thinsp;=\u0026thinsp;2.51 for OS (95% CI:1.32\u0026thinsp;~\u0026thinsp;4.76) compared to low LDH, indicating LDH as an important independent prognostic factor for advanced lung cancer patients receiving ICIs. This study also confirmed baseline LDH levels significantly associated with PFS and OS in ICIs-treated advanced NSCLC patients, with low LDH group PFS at 9.5 months (vs. 7.1 months, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), OS risk reduced by 82.6% (HR\u0026thinsp;=\u0026thinsp;1.826, P\u0026thinsp;=\u0026thinsp;0.001). Similar results were seen in adenocarcinoma and squamous subgroups: in adenocarcinoma, low LDH group PFS extended by 2.7 months (P\u0026thinsp;=\u0026thinsp;0.032), OS by 2.6 months (P\u0026thinsp;=\u0026thinsp;0.021). In squamous patients, low LDH group OS reached 24.6 months (vs. 19.8 months, P\u0026thinsp;=\u0026thinsp;0.001), with PFS also significantly extended (P\u0026thinsp;=\u0026thinsp;0.017). Meanwhile, LDH levels were significantly associated with irAEs, with low LDH group (\u0026lt;\u0026thinsp;226.50 U/L) having higher irAEs incidence (62.2% vs. 35.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and risk (OR\u0026thinsp;=\u0026thinsp;2.881, 95% CI:1.590\u0026thinsp;~\u0026thinsp;5.223, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAdditionally, the application of ALBI grading in assessing immunotherapy prognosis has gradually gained attention. Pinato and Kaneko et al. have explored ALBI grading's prognostic value in hepatocellular carcinoma patients receiving immunotherapy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. They found pre-treatment ALBI grading could predict OS extension and was superior to Child-Pugh grading in predicting mortality. Kinoshita et al. compared pre-operative ALBI grading with clinicopathological features and prognosis in resectable NSCLC patients, finding ALBI grades 2/3 associated with poor prognosis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Ryosuke Matsukane et al. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]'s study showed ALBI grading as a pre-treatment liver reserve assessment could significantly predict survival in ICIs-treated NSCLC patients, with pre-treatment ALBI grading as independent prognostic factor for PFS (HR 0.57, 95% CI 0.38\u0026thinsp;~\u0026thinsp;0.86, p\u0026thinsp;=\u0026thinsp;0.007) and OS (HR\u0026thinsp;=\u0026thinsp;0.45, 95% CI:0.29\u0026thinsp;~\u0026thinsp;0.72, P\u0026thinsp;=\u0026thinsp;0.001), indicating ALBI grading-evaluated pre-treatment liver function as an important biomarker for predicting ICIs efficacy in NSCLC. This article determined the optimal ALBI cut-off as -2.68 via calculation; in the full cohort, ALBI\u0026lt;-2.68 group PFS extended by 1.6 months (8.7 vs. 7.1 months, P\u0026thinsp;=\u0026thinsp;0.022), OS by 3.9 months (21.4 vs. 17.5 months, P\u0026thinsp;=\u0026thinsp;0.047), and multivariate analysis proved ALBI as independent predictor for PFS and OS. Similarly, in squamous patients, low ALBI score group median OS reached 21.4 months (vs. 17.3 months, P\u0026thinsp;=\u0026thinsp;0.047), with significant independent predictive value.\u003c/p\u003e \u003cp\u003eThis study also evaluated clinical factors like PS score and efficacy on patient prognosis via Cox regression. Results showed PS score and efficacy (especially SD and PD) significantly influenced PFS and OS. Poorer PS scores (PS\u0026thinsp;\u0026ge;\u0026thinsp;2) were closely associated with shorter PFS and OS, validating PS score as a classic prognostic indicator in lung cancer. Additionally, short-term efficacy (e.g., SD and PD) had important effects on prognosis, especially PD group patients with significantly shorter PFS and OS.\u003c/p\u003e \u003cp\u003eThis study provides valuable insights into exploring baseline blood biomarkers' prognostic value in immunotherapy for advanced NSCLC patients but has limitations. First, as a retrospective analysis from two centers (Renmin Hospital of Wuhan University and Macheng People's Hospital Tumor Centers), although multi-center design partially reduced single-institution bias, results may be influenced by inter-center data collection standard differences, regional variations, patient selection bias, and data completeness limitations. Second, although the sample size reached 198, it was limited in certain subgroup analyses (e.g., squamous patients receiving immuno-combined targeted therapy), potentially leading to insufficient statistical power, requiring larger-scale, multi-center prospective studies for validation. Additionally, while we analyzed multiple biomarkers (e.g., NLR, PLR, ALBI, LDH), this study did not deeply explore other factors potentially affecting immunotherapy prognosis, such as tumor gene mutations, immune microenvironment, and regimen differences, which may play varying roles in different patient groups. Finally, although we validated significant relationships between ALBI, NLR, PLR, etc., and PFS/OS, their clinical operability and practical application need further exploration and optimization, especially how to efficiently apply these markers in real clinical settings for efficacy prediction.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study found that peripheral blood markers (NLR, PLR, ALBI, LDH) have important predictive value for survival outcomes in advanced NSCLC, with significant pathological subtype heterogeneity: squamous prognosis mainly driven by systemic inflammation, while adenocarcinoma depends more on metabolic activity and molecular features, suggesting the need for subtype-specific biomarker strategies. Short-term disease control status is a key predictor of survival, radiotherapy improves prognosis, but immuno-combined targeted therapy may increase mortality risk in squamous patients. Meanwhile, baseline NLR and LDH are significantly associated with irAEs risk. These findings provide a theoretical basis for integrating inflammation, metabolism, and treatment response markers to build stratification models, hoping to guide clinical decisions through multi-dimensional biomarkers, optimizing treatment selection while balancing efficacy and safety, advancing precise practice of individualized immunotherapy in advanced NSCLC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALB, Albumin\u003c/p\u003e\n\u003cp\u003eALBI, Albumin-bilirubin\u003c/p\u003e\n\u003cp\u003eALC, Absolute Lymphocyte Count\u003c/p\u003e\n\u003cp\u003eAMC, Absolute Monocyte Count\u003c/p\u003e\n\u003cp\u003eANC, Absolute Neutrophil Count\u003c/p\u003e\n\u003cp\u003eAST, Aspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eAUC, Area Under The Curve\u003c/p\u003e\n\u003cp\u003eCR, Complete Response\u003c/p\u003e\n\u003cp\u003eDCR, Disease Control Rate\u003c/p\u003e\n\u003cp\u003eECOG PS, Eastern Cooperative Oncology Group Performance Score\u003c/p\u003e\n\u003cp\u003eICIs, Immune Checkpoint Inhibitors\u003c/p\u003e\n\u003cp\u003eirAEs, Immune-related Adverse Events\u003c/p\u003e\n\u003cp\u003eLDH, Lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eNLR, Neutrophil-to-lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eNSCLC, Non-Small Cell Lung Cancer\u003c/p\u003e\n\u003cp\u003eOS, Overall Survival\u003c/p\u003e\n\u003cp\u003eORR, Overall Response Rate\u003c/p\u003e\n\u003cp\u003ePFS, Progression Free Survival\u003c/p\u003e\n\u003cp\u003ePR, Partial Response\u003c/p\u003e\n\u003cp\u003ePD, Progressive Disease\u003c/p\u003e\n\u003cp\u003ePD-1, Programmed Death 1\u003c/p\u003e\n\u003cp\u003ePD-L1, Programmed Death-ligand 1\u003c/p\u003e\n\u003cp\u003ePLR, Platelet-to-lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eROC, Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eTME, Tumor microenvironment\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was supported by the Natural Science Foundation of Hubei Provincial (2023AFB766 to DDC) .The authors sincerely acknowledge the strong support for this study from the Department of Oncology at Macheng People's Hospital and Renmin Hospital of Wuhan University. We extend our gratitude to the Medical Department and relevant clinical units of Renmin Hospital of Wuhan University for facilitating data access. Furthermore, we thank the Clinical Research Ethics Committee of Renmin Hospital of Wuhan University for approving and overseeing the study protocol. Lastly, we wish to express our deepest appreciation to all the patients who participated in this study; their trust and contributions were fundamental to the completion of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: DDC; Data curation: NZ, XYW, WSZ, XC, HH; Formal analysis: NZ, XYW, DDC; Funding acquisition: DDC, WSZ; Investigation: all authors; Methodology: NZ, DDC; Project administration: DDC; Supervision: DDC, HH; Writing—original draft: NZ, XYW; and Writing—review \u0026amp; editing: DDC. All authors have reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eand materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used and analysed in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince this study involves the retrospective use of data, and the patient information has been fully anonymized, and the study itself does not interfere with the patient's diagnosis and treatment process nor impose any additional burden on patients, the\u0026nbsp;requirement for obtaining informed consent from patients for this study has been waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol has been reviewed and approved by the\u0026nbsp;Ethics Committee of Renmin Hospital of Wuhan University (Ethics Approval No.: WDRY2020F048). The need for informed consent was waived by the\u0026nbsp;Ethics Committee of Renmin Hospital of Wuhan University due to the retrospective nature of the study. This study was conducted in accordance with the Helsinki declaration and its later amendments or comparable ethical standards, so as to ensure the full protection of patients' legitimate rights and interests such as the right to privacy and the right to information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, et al. 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Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinato DJ, Sharma R, Allara E, et al. The ALBI grade provides objective hepatic reserve estimation across each BCLC stage of hepatocellular carcinoma. J Hepatol. 2017;66(2):338\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsukane R, Watanabe H, Hata K, et al. Prognostic significance of pre-treatment ALBI grade in advanced non-small cell lung cancer receiving immune checkpoint therapy. Sci Rep. 2021;11(1):15057.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, Immunotherapy, Biomarkers, Inflammatory markers, Predictive factors","lastPublishedDoi":"10.21203/rs.3.rs-8723320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8723320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eObjective:\u003c/em\u003eImmune checkpoint inhibitors (ICIs) have significantly improved the treatment outcomes for advanced non-small cell lung cancer (NSCLC), but patient benefits vary individually. Therefore, identifying biomarkers to predict the efficacy and prognosis of immunotherapy is crucial. Hematological markers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-bilirubin (ALBI) score, and lactate dehydrogenase (LDH) levels may correlate with tumor prognosis. This study aimed to evaluate the prognostic value of these hematological and clinical markers in advanced NSCLC patients treated with ICIs, providing a basis for individualized treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods: \u003c/em\u003eThis retrospective study included NSCLC patients treated with ICIs at the Tumor Centers of Renmin Hospital of Wuhan University and Macheng City People’s Hospital between January 2021 and December 2023. Clinical data such as gender, age, ECOG PS score, clinical stage, and treatment details were collected. Patients were stratified based on NLR, PLR, LDH, and ALBI scores. ROC curve analysis assessed the predictive capacity of these markers for mortality risk. Chi-square tests, logistic regression, Kaplan-Meier survival analysis, and log-rank tests were used to analyze short-term efficacy, immune-related adverse events (irAEs), progression-free survival (PFS), and overall survival (OS). Cox regression identified independent prognostic factors for PFS and OS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e A total of 198 advanced NSCLC patients (median follow-up: 28.1 months) were included. Median PFS (mPFS) and OS (mOS) were 7.7 months (95% CI: 6.9~8.5) and 20.1 months (95% CI: 18.2~21.9), respectively. ROC analysis demonstrated significant predictive value for baseline NLR, PLR, ALBI, and LDH in mortality risk (AUC: 0.804, 0.694, 0.684, 0.726, respectively). Efficacy analysis revealed PD-L1 positivity (OR = 0.361, 95% CI: 0.161~0.808, P = 0.013) and ALBI \u0026lt; -2.68 (OR = 2.524, 95% CI: 1.148–5.552, P = 0.021) as independent predictors of objective response rate (ORR: 25.8%), while camrelizumab significantly reduced response rates (OR = 0.157, P = 0.006). In the squamous cell carcinoma subgroup, radiotherapy was an independent protective factor for tumor response (OR = 0.298, 95% CI: 0.094–0.95, P = 0.041).\u003c/p\u003e\n\u003cp\u003eBaseline NLR, PLR, ALBI, and LDH significantly stratified PFS (P \u0026lt; 0.05): low NLR (8.7 vs. 7.1 months, P = 0.001), low PLR (7.9 vs. 7.4 months, P = 0.007), low ALBI (8.1 vs. 7.0 months, P = 0.028), and low LDH (9.5 vs. 7.1 months; 46.3% risk reduction, P \u0026lt; 0.001). In the adenocarcinoma subgroup, LDH remained an independent prognostic factor (9.5 vs. 6.8 months, P = 0.032). For squamous cell carcinoma, low NLR (9.1 vs. 7.1 months, P = 0.002), low PLR (10.2 vs. 7.4 months, P = 0.002), low ALBI (8.7 vs. 7.1 months, P = 0.022), and low LDH (9.1 vs. 7.5 months, P = 0.002) were significant. Baseline markers also predicted OS: low NLR (21.4 vs. 17.5 months, P \u0026lt; 0.001), low PLR (20.4 vs. 17.7 months, P = 0.009), and low LDH (21.4 months; 42.5% risk reduction, P \u0026lt; 0.001). Subtype analysis showed adenocarcinoma benefited from low NLR (21.4 vs. 16.5 months, P = 0.013) and low LDH (20.1 vs. 17.5 months, P = 0.021), while squamous carcinoma relied on low NLR (21.4 vs. 16.8 months, P = 0.008), low PLR (21.8 vs. 20.1 months, P = 0.024), low ALBI (21.4 vs. 18.2 months, P = 0.047), and low LDH (24.6 vs. 19.8 months, P = 0.001).\u003c/p\u003e\n\u003cp\u003eMultivariate Cox analysis identified treatment response (SD/PD), radiotherapy, NLR, and LDH as independent predictors of PFS: SD (HR = 1.959) and PD (HR = 2.763) increased progression risk by 95.9% and 176.3% (P \u0026lt; 0.01), respectively; radiotherapy reduced risk by 29.2% (HR = 0.708, P = 0.041); NLR ≥ 3.72 (HR = 1.638) and LDH ≥ 226.5 U/L (HR = 1.783) increased risk by 63.8% and 78.3% (P \u0026lt; 0.05). For OS, adenocarcinoma (HR = 0.343) and squamous carcinoma (HR = 0.312) reduced mortality risk by 65.7% and 68.8% (P \u0026lt; 0.05), while SD (HR = 1.715), PD (HR = 2.536), NLR ≥ 3.72 (HR = 1.629), and LDH ≥ 226.5 U/L (HR = 1.826) increased risk by 71.5%, 153.6%, 62.9%, and 82.6% (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eBaseline NLR and LDH correlated with irAEs risk: low NLR (55.4% vs. 37.1%, P = 0.010; OR = 1.976, 95% CI: 1.098~3.557, P = 0.023) and low LDH (62.2% vs. 35.3%, P \u0026lt; 0.001; OR = 2.881, 95% CI: 1.590~5.223, P \u0026lt; 0.001) increased irAEs incidence.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e Baseline hematological markers (NLR, PLR, ALBI, LDH) are valuable predictors of efficacy and prognosis in advanced NSCLC patients undergoing immunotherapy. Their prognostic roles vary by pathological subtype: squamous carcinoma relies more on inflammatory markers, while adenocarcinoma emphasizes metabolic markers and genetic mutations. This study provides a hematological biomarker-based stratification framework for individualized immunotherapy decisions in advanced NSCLC, offering critical guidance for optimizing treatment and prognosis management.\u003c/p\u003e","manuscriptTitle":"Characteristics and Hematological Indicators Predictors of Immunotherapy Response in Advanced Non-Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 05:22:50","doi":"10.21203/rs.3.rs-8723320/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T04:45:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-27T15:35:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331239642168041010524030836702157513917","date":"2026-02-26T10:07:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191529007503878321619460348408141535303","date":"2026-02-26T07:09:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176855377449811834715671616194970999214","date":"2026-02-26T05:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225459939458193019422113877610122029915","date":"2026-02-26T02:14:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92093448709142041605749742534716599434","date":"2026-02-25T19:18:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T08:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136338594783702743504607557941727684792","date":"2026-02-11T08:10:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16674753265808813197093561841318514760","date":"2026-02-10T21:53:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301527202498204111479264472763694759715","date":"2026-02-07T22:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T18:36:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-31T22:20:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-29T09:41:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T09:38:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-01-28T15:21:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e107fa8-a40e-4fb0-9645-0ca19008e955","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T15:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 05:22:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8723320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8723320","identity":"rs-8723320","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}