Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients

Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients | 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 Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients Wenyi Liu, Zhilin Sui, Chunguang Wang, Youjun Deng, Songhua Cai, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6532230/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The application of neoadjuvant immunotherapy combined with chemotherapy in intermediate and high-risk resectable non-small cell lung cancer (NSCLC) is increasing, with some patients achieving pathological complete response (pCR). However, there is no effective tool for clinical precision prediction of pCR. This study aims to integrate clinical data to develop and construct a nomogram model capable of predicting the risk of pCR in NSCLC patients following neoadjuvant chemotherapy combined with immunotherapy, thereby providing a reference for individualized treatment decisions. Methods A retrospective analysis was conducted on patients with resectable NSCLC who underwent neoadjuvant chemotherapy combined with immunotherapy between 2019 and 2022. Inclusion criteria were as follows: diagnosis of NSCLC (stages IIB–IIIB), completion of neoadjuvant chemotherapy and immunotherapy followed by surgical resection, and a definitive postoperative pathological assessment. Clinical parameters such as age, gender, smoking history, comorbidities, neoadjuvant treatment regimens, and treatment-related adverse events were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR, which were subsequently used to construct a nomogram model. Results A total of 179 patients were included in the study, comprising 92 cases (51.4%) in the pCR group and 87 cases (48.6%) in the non-pCR group. Multivariate analysis identified the following factors as significantly associated with pCR (P < 0.05): pathological type, family history of tumors, duration of smoking cessation, age, and number of neoadjuvant treatment cycles. A multivariate logistic regression model incorporating these factors demonstrated an area under the curve (AUC) of 0.709 (95% CI: 0.633–0.785), indicating good predictive performance. The calibration curve showed strong agreement between predicted probabilities and observed outcomes. Conclusions Based on real clinical data, this study developed and constructed a nomogram to predict pCR in NSCLC patients after neoadjuvant chemotherapy combined with immunotherapy. This tool not only performs well in terms of discrimination and calibration but also has potential clinical application value, providing new ideas and basis for the formulation of individualized treatment strategies. non-small cell lung cancer neoadjuvant chemotherapy combined with immunotherapy pathological complete response nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Significant progress has been made in the neoadjuvant treatment of lung cancer in recent years, particularly with the advent of immunotherapy, which offers new hope to patients[ 1 ]. Immunotherapy enhances the immune system's ability to combat tumor cells and has become a cornerstone of lung cancer treatment. The management of NSCLC has been significantly transformed by the introduction of immune checkpoint inhibitors (ICIs), which have shown remarkable efficacy in various clinical trials. Notably, phase III trials such as CheckMate 816 and KEYNOTE-671 have been pivotal in establishing the role of ICIs in the treatment of NSCLC. These trials have demonstrated that ICIs, when used in combination with chemotherapy, can significantly improve outcomes for patients with resectable NSCLC by increasing the pCR rates and decreasing relapse rates[ 2 – 5 ]. Research indicates that this combination therapy significantly improves overall survival (OS), disease-free survival (DFS), pCR rates, and major pathological response (MPR) rates. Despite its advantages, not all NSCLC patients benefit from neoadjuvant chemoimmunotherapy, underscoring the importance of accurately identifying those likely to respond before initiating treatment. Biomarkers play a critical role in patient selection, as their validation and application guide the identification of suitable candidates for neoadjuvant treatment[ 6 ]. ICIs in particular enhance T-cell effector functions and induce long-term immune memory, providing strong theoretical support for their use in neoadjuvant therapy[ 7 ]. Compared to chemotherapy alone, the addition of immunotherapy has demonstrated superior survival benefits, particularly in resectable stage II-IIIB NSCLC, where it outperforms adjuvant chemoimmunotherapy in terms of OS and DFS[ 8 ]. Additionally, this combination significantly improves pCR and MPR rates, which are pivotal for achieving favorable long-term outcomes[ 9 ]. While the potential of neoadjuvant immunotherapy is promising, further investigation is needed to validate its efficacy across diverse patient populations. Large-scale randomized controlled trials are essential to determine the optimal therapeutic regimens that balance efficacy with acceptable toxicity. Concurrently, developing and validating biomarkers remain crucial to guiding treatment decisions and enhancing therapeutic outcomes. Despite ongoing efforts to identify predictive factors and models for the success of neoadjuvant chemoimmunotherapy, a reliable and user-friendly tool for predicting pCR is still lacking in clinical practice. Accurate pre-treatment identification of patients likely to achieve pCR is vital for improving OS, reducing treatment costs, and avoiding overtreatment. Nomograms provide a practical and visual means of simplifying multifactorial regression models, enabling clinicians to make individualized prognostic predictions based on a patient’s multidimensional characteristics. Compared to traditional staging or single-factor indicators, nomograms often deliver superior predictive performance. Given the high incidence of pCR following neoadjuvant treatment in lung cancer and the limited availability of nomograms for pCR prediction, developing a model that integrates pre- and post-treatment parameters holds significant value in identifying candidates likely to achieve pCR. Based on the above background, this study aims to collect and analyze extensive clinical data from NSCLC patients treated with neoadjuvant chemoimmunotherapy at our hospital. By evaluating multiple variables before and after neoadjuvant treatment, we identify potential predictors of pCR. Using logistic regression analysis, we select relevant parameters to construct a nomogram for pCR prediction, which is subsequently validated internally to assess its performance. 2. MATERIALS AND METHODS 2.1. Patients A retrospective analysis was conducted using data from NSCLC patients who underwent neoadjuvant treatment followed by lung cancer surgery at Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center between 2019 and 2022. Patients were included in the study if they met all of the following criteria: Pathologically diagnosed with NSCLC (including squamous cell carcinoma, adenocarcinoma, or other subtypes) and having complete clinical and pathological data, in accordance with the 8th edition of the AJCC staging system; Pathological staging of II-IIIB stage NSCLC patients; Received platinum-based chemotherapy combined with immune checkpoint inhibitors; Postoperative pathological assessment clearly classified as either pCR or non-pCR. Exclusion criteria included: those who only received neoadjuvant chemotherapy, only received neoadjuvant immunotherapy, interrupted treatment regimens, and those with incomplete data. This study was approved by the Ethics Review Committee of the Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center. The study complies with the ethical principles outlined in the Declaration of Helsinki (2013 revision). The patient selection process for this study is summarized in Fig. 1 . 2.2. Treatment and Follow-up The neoadjuvant treatment regimens included neoadjuvant chemotherapy combined with immunotherapy and neoadjuvant chemotherapy alone. Chemotherapy consisted of paclitaxel combined with platinum-based drugs, while immunotherapy included Keytruda, Nivolumab, and other domestically produced immunotherapeutic agents in China. The multidisciplinary team (MDT) determined the specific neoadjuvant treatment regimen and the number of treatment cycles based on each patient’s condition and clinical trial participation. Following neoadjuvant treatment, all patients underwent radical surgery, which included lobectomy, sleeve lobectomy, or pneumonectomy. Postoperative tumor specimens were evaluated by experienced pathologists. Pathological complete response (pCR) was defined as the complete absence of residual cancer cells in all pathological sections of the resected lung tissue and lymph nodes. Patients with any residual cancer cells identified in the final pathology were classified as non-pCR. After surgery, patients underwent routine follow-up: every three months during the first two years, every six months during the subsequent three years, and annually thereafter. 2.3. Data Collection The selection of variables was partially based on previously published literature and supplemented by clinical expertise. The following parameters were evaluated as potential predictive factors for pCR: gender, age, presence of symptoms at initial diagnosis, weight loss at initial diagnosis, smoking history, smoking index, smoking cessation duration, alcohol consumption history, comorbidities, family history of cancer, tumor location, tumor type (central or peripheral), clinical T stage (cT), clinical N stage (cN), clinical TNM stage (cTNM), number of neoadjuvant treatment cycles, type of immunotherapy drug, type of chemotherapy drug, type of platinum-based drug, treatment-related adverse reactions, the interval between the end of neoadjuvant therapy and surgery, and pathological tumor subtype. Age was stratified into < 58 years and ≥ 58 years based on the X-tile method. Comorbidities were categorized as none, hypertension, diabetes, or other conditions. Immunotherapy drugs included Keytruda, Nivolumab, and other domestically produced immunotherapy agents. Chemotherapy drugs were categorized as paclitaxel or other agents, and platinum-based drugs included cisplatin, carboplatin, or other platinum compounds. Adverse events were classified into none, myelosuppression, hepatic and renal dysfunction, or immune-related adverse reactions, and were graded from 1 to 4. 2.4. Statistical Analysis Categorical variables were compared using the chi-square test. Statistical analyses were conducted using the R software package (version 4.4.2) and DCPM software (version 5.49, Jingding Medical Technology Co., Ltd.). A two-sided p-value of < 0.05 was considered statistically significant. Univariate analysis was performed to reduce dimensionality and retain variables significantly associated with pCR. Significant features were included in a multivariate logistic regression model to derive the predictive equation for pCR. The final regression coefficients were used to construct a nomogram to quantify the contribution of each variable to pCR prediction. Model evaluation: The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model's discriminative ability. Calibration curves were used to evaluate the consistency between predicted probabilities and actual outcomes. Bootstrap resampling was employed to validate the robustness and reproducibility of the model. 3. RESULTS 3.1. Patient characteristics From 2019 to 2022, a total of 375 NSCLC patients received neoadjuvant treatment followed by surgery at our institution. After excluding patients who did not meet the inclusion criteria or had incomplete data, 179 patients were included in the final analysis. Among them, 92 patients (51.4%) achieved pCR, while 87 patients (48.6%) were classified as non-pCR. The majority of the patients were male (171 cases, 95.5%), and over half were aged 58 years or older (100 cases, 55.9%). Most patients presented with symptoms at diagnosis, such as coughing or shortness of breath (132 cases, 73.7%), and did not experience weight loss (148 cases, 82.7%). Comorbidities were absent in 113 patients (63.1%). Central-type tumors were predominant (143 cases, 79.9%), and most patients had advanced nodal involvement (cN1-2, 168 cases, 93.9%). The tumor stage distribution was as follows: 38 cases (21.2%) were stage IIB, 83 cases (46.4%) were stage IIIA, and 58 cases (32.4%) were stage IIIB. Neoadjuvant treatment cycles varied, with 107 patients (59.8%) completing 2 cycles, 54 patients (30.2%) completing 3 cycles, and 18 patients (10.1%) completing 4 cycles. Treatment-related adverse events included myelosuppression (49 cases, 27.4%), hepatic and renal dysfunction (44 cases, 24.6%), and immune-related adverse reactions (24 cases, 13.4%). (Table 1 ) Table 1 Patient characteristics and univariate analysis for categorical variables associated with pCR. pCR N = 92 non-pCR N = 87 p-value Univariate analysis OR (95% CI) p-value Sex, n (%) 0.487 0.553(0.111–2.324) 0.427 Male 89 (96.74%) 82 (94.25%) Female 3 (3.26%) 5 (5.75%) Age, Mean ± SD 60.42 ± 6.23 58.89 ± 7.76 0.147 1.032(0.99–1.078) 0.145 Age stratification, n (%) 0.526 0.79(0.436–1.425) 0.433 ≥ 58 54 (58.70%) 46 (52.87%) <58 38 (41.30%) 41 (47.13%) Symptom, n (%) 0.367 1.441(0.74–2.835) 0.285 No 21 (22.83%) 26 (29.89%) Yes 71 (77.17%) 61 (70.11%) Weight loss, n (%) 0.823 1.182(0.544–2.604) 0.673 No 75 (81.52%) 73 (83.91%) Yes 17 (18.48%) 14 (16.09%) Smoking history, n (%) 0.496 1.762(0.564–6.039) 0.338 No 5 (5.43%) 8 (9.20%) Yes 87 (94.57%) 79 (90.80%) Smoking cessation duration 2 [0;37.5] 10 [0;165] 0.066 1(0.999-1) 0.072 Alcohol history, n (%) 0.698 1.451(0.484–4.583) 0.507 No 6 (6.52%) 8 (9.20%) Yes 86 (93.48%) 79 (90.80%) Comorbidities, n (%) 0.302 0.951(0.697–1.295) 0.747 No 57 (61.96%) 56 (64.37%) Hypertension 20 (21.74%) 16 (18.39%) Diabetes 10 (10.87%) 5 (5.75%) Others 5 (5.43%) 10 (11.49%) Family history, n (%) 0.017 10.48(1.945–194.6) 0.027 No 82 (89.13%) 86 (98.85%) Yes 10 (10.87%) 1 (1.15%) Tumor Site, n (%) 0.853 0.938(0.767–1.145) 0.528 Left upper lobe 24 (26.09%) 19 (21.84%) Left lower lobe 15 (16.30%) 16 (18.39%) Right upper lobe 28 (30.43%) 26 (29.89%) Right middle lobe 4 (4.35%) 2 (2.30%) Right lower lobe 21 (22.83%) 24 (27.59%) Tumor type, n (%) 0.999 0.932(0.447–1.945) 0.851 Central 74 (80.43%) 69 (79.31%) Peripheral 18 (19.57%) 18 (20.69%) cT, n (%) 0.667 1.216(0.869–1.711) 0.256 1 5 (5.43%) 5 (5.75%) 2 32 (34.78%) 37 (42.53%) 3 31 (33.70%) 28 (32.18%) 4 24 (26.09%) 17 (19.54%) cN, n (%) 0.354 0.921(0.566–1.491) 0.737 0 4 (4.35%) 6 (6.90%) 1 36 (39.13%) 26 (29.89%) 2 51 (55.43%) 55 (63.22%) 3 1 (1.09%) 0 (0.00%) cTNM, n (%) 0.977 0.988(0.658–1.483) 0.954 IIB 20 (21.74%) 18 (20.69%) IIIA 42 (45.65%) 41 (47.13%) IIIB 30 (32.61%) 28 (32.18%) Treatment cycle, n (%) 0.225 1.479(0.95–2.34) 0.087 2 50 (54.35%) 57 (65.52%) 3 30 (32.61%) 24 (27.59%) 4 12 (13.04%) 6 (6.90%) Immunization drugs, n (%) 0.283 1.324(0.896–1.977) 0.163 Keytruda 11 (11.96%) 18 (20.69%) Nivolumab 11 (11.96%) 9 (10.34%) Others 70 (76.09%) 60 (68.97%) Chemotherapeutic drugs, n (%) 0.158 0.502(0.209–1.15) 0.11 Paclitaxel 82 (89.13%) 70 (80.46%) Others 10 (10.87%) 17 (19.54%) Platinum drugs, n (%) 0.768 0.917(0.426–1.957) 0.823 Cisplatin 17 (18.48%) 14 (16.09%) Carboplatin 74 (80.43%) 73 (83.91%) Others 1 (1.09%) 0 (0.00%) Side effects stratification, n (%) 0.842 0.894(0.481–1.656) 0.722 No 33 (35.87%) 29 (33.33%) Yes 59 (64.13%) 58 (66.67%) Side effects, n (%) 0.255 0.933(0.704–1.234) 0.626 NO 33 (35.87%) 29 (33.33%) Myelosuppression 28 (30.43%) 21 (24.14%) Hepatic and renal function abnormalities 17 (18.48%) 27 (31.03%) Immune-related side effects 14 (15.22%) 10 (11.49%) Grading of side effects, n (%) 0.560 1.013(0.772–1.332) 0.924 0 33 (35.87%) 29 (33.33%) 1 26 (28.26%) 32 (36.78%) 2 24 (26.09%) 18 (20.69%) 3 6 (6.52%) 3 (3.45%) 4 3 (3.26%) 5 (5.75%) Time interval 33.5 [29;43.25] 34 [28;40.5] 0.661 1.007(0.989–1.027) 0.458 Time interval stratification, n (%) 0.624 1.263(0.635–2.539) 0.507 ≤ 42 68 (73.91%) 68 (78.16%) >42 24 (26.09%) 19 (21.84%) Pathology, n (%) 0.036 0.413(0.194–0.81) 0.014 Squamous carcinoma 82 (89.13%) 65 (74.71%) Adenocarcinoma 9 (9.78%) 18 (20.69%) Others 1 (1.09%) 4 (4.60%) 3.2. Univariate and Multivariate Analysis Univariate analysis identified multiple factors associated with pCR, including pathological type, chemotherapy drug type, immunotherapy drug type, treatment cycle count, family history of cancer, smoking cessation duration, smoking index, and age. (Table 1 ). Multivariate logistic regression analysis revealed five independent predictors of pCR (p < 0.05): (Table 2 ) 1.Pathological type: Squamous cell carcinoma was associated with higher pCR rates compared to adenocarcinoma. 2.Family history of cancer: A positive family history significantly increased the likelihood of achieving pCR. 3.Quit smoking days: Shorter smoking cessation durations were associated with higher pCR rates. 4.Age: Older patients demonstrated slightly higher pCR rates. 5.Treatment cycle count: A higher number of treatment cycles correlated with increased pCR rates. Table 2 Multivariate analysis for variables associated with pCR. Variable Multivariate analysis OR (95% CI) p-value Age 1.053 (1.005–1.106) 0.032 Smoking cessation duration 0.999 (0.999–0.999) 0.033 Family history 10.76 (1.903–203.3) 0.027 Treatment cycle 1.621 (1.007–2.661) 0.049 Pathology 0.344 (0.151–0.707) 0.006 3.3. Receiver operating characteristic Curve Analysis, Calibration Analysis, Decision Curve Cross-validation Analysis The multivariate model demonstrated good predictive performance, with an AUC of 0.709 (95% CI: 0.633–0.785). (Fig. 2 ) The calibration curve indicated strong agreement between predicted probabilities and observed outcomes, particularly in most probability ranges, although minor deviations were noted between probabilities of 0.5 and 0.8. Internal validation using 500 bootstrap resamplings confirmed the model's robustness. (Figs. 3 ). The decision curve analysis indicates that within a certain range of threshold probabilities, the model's predictions can bring about net benefits, especially in the low threshold probability area. However, in the high threshold probability area, the model's predictions may not be as good as taking no action at all. Through this analysis, we can determine the optimal threshold probability for the model in practical applications to maximize net benefits. (Figs. 4 ). 3.4. Nomogram Based on the coefficients of multivariate logistic regression, a nomogram was established to predict pCR. The nomogram shows that if a patient scores 256 points, combining five variables, the predicted probability of pCR can be as high as 90% (Fig. 5 ). The rationality analysis based on model probability (Fig. 6 ) also shows that the model's predictive ability is better than that of any single-factor variable. 4. DISCUSSION The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment landscape for lung cancer, particularly non-small cell lung cancer (NSCLC). These advancements have significantly improved treatment and management strategies for the disease. Meta-analyses have demonstrated the efficacy and safety of neoadjuvant immunotherapy in resectable NSCLC, presenting an encouraging outlook. Moreover, real-world studies have evaluated the clinical outcomes of ICIs following neoadjuvant treatment in resectable NSCLC patients, focusing on prognostic factors and survival prediction. Research has also highlighted the potential benefits of neoadjuvant immunotherapy in NSCLC patients with tumor gene mutations, although further investigations are warranted to validate its effectiveness in this subgroup. Compared to chemotherapy, neoadjuvant immunotherapy has shown superior efficacy and reduced toxicity in advanced NSCLC, supporting its integration into early-stage resectable NSCLC treatment. Additionally, neoadjuvant immunotherapy is emerging as a promising strategy for treating locally advanced resectable NSCLC, with studies exploring its safety, efficacy, and comparative outcomes against neoadjuvant chemoimmunotherapy or chemotherapy alone. Systematic reviews and meta-analyses underscore the benefits of adding immunotherapy to neoadjuvant chemotherapy for locally advanced NSCLC, emphasizing the importance of combination therapy. Furthermore, network meta-analyses comparing neoadjuvant immunotherapy with chemotherapy or perioperative immunotherapy have highlighted the transformative potential of immunotherapy in this context. Identifying which non-small cell lung cancer (NSCLC) patients will benefit from neoadjuvant chemoimmunotherapy is crucial for optimizing treatment outcomes and avoiding unnecessary side effects. Recent studies have highlighted various approaches to predict patient responses to this treatment modality. One promising approach is the use of blood biomarkers to predict complete pathological response in NSCLC patients undergoing neoadjuvant chemoimmunotherapy. In the NADIM clinical trial, researchers identified specific immune parameters in peripheral blood that were associated with complete pathological response, suggesting that these biomarkers could be used to stratify patients before treatment [ 10 ]. Researchers have also turned to advanced imaging techniques to predict treatment success. A deep learning model built on computed tomography (CT) imaging data has shown remarkable accuracy in predicting pathologic complete response to neoadjuvant chemoimmunotherapy in NSCLC. This innovation highlights how imaging data could become a key tool in identifying suitable candidates for this aggressive treatment strategy [ 11 ]. Another study explored the use of delta-radiomics features, which are based on the relative net change of radiomics features between baseline and preoperative CT scans. This approach showed superior predictive performance compared to pre-treatment radiomics models and could serve as a noninvasive biomarker for predicting major pathological response to neoadjuvant chemoimmunotherapy [ 12 ]. In a single-center study, scientists developed a predictive score for major pathological response using pre-treatment parameters such as prothrombin time, neutrophil percentage, and PD-L1 expression. This model offers a practical framework for personalized decision-making in operable NSCLC treated with neoadjuvant chemoimmunotherapy [ 13 ]. The tumor immune microenvironment also plays a significant role in predicting the pathologic response of neoadjuvant chemoimmunotherapy in NSCLC. A study found that specific patterns of tumor-infiltrating lymphocytes were associated with favorable pathological responses, underscoring the value of immune profiling in treatment planning [ 14 ]. Insights from a meta-regression analysis of randomized clinical trials reveal that the benefits of neoadjuvant chemotherapy in NSCLC are most pronounced in stage 3 disease when protocols include three chemotherapeutic agents. This finding reinforces the importance of aligning treatment strategies with disease stage and chemotherapy protocols [ 15 ]. These studies collectively emphasize the importance of predictive biomarkers and advanced imaging techniques in identifying NSCLC patients who are likely to benefit from neoadjuvant chemoimmunotherapy, thereby enhancing treatment efficacy and minimizing unnecessary exposure to potential side effects. Using real-world clinical data, this study successfully developed a nomogram model to predict pathological complete response (pCR) in NSCLC patients receiving neoadjuvant chemotherapy combined with immunotherapy. By retrospectively collecting data from patients treated between 2019 and 2022, the model was constructed through an in-depth analysis of clinical parameters, including age, gender, smoking history, comorbidities, neoadjuvant treatment regimens, and adverse reactions. Multivariate analysis identified five significant predictors of pCR: pathological type, family history of tumors, smoking cessation duration, age, and the number of neoadjuvant treatment cycles. These findings provide new insights into the factors influencing pathological responses in NSCLC patients undergoing combination therapy. Key Predictive Factors Pathological type emerged as a pivotal predictor of pCR. NSCLC subtypes, including squamous cell carcinoma (SCC) and adenocarcinoma, exhibit distinct molecular features, biological behaviors, and treatment sensitivities. SCC demonstrates higher sensitivity to immunotherapy, likely due to its unique molecular characteristics[ 16 , 17 ]. Studies indicate that ICIs yield significant therapeutic benefits in SCC patients, though their response to traditional chemotherapy may be less robust[ 18 ]. Recent advancements in immunotherapy and targeted therapies offer new hope for improving SCC outcomes[ 19 ]. Conversely, adenocarcinoma tends to respond more favorably to platinum-based chemotherapy, potentially due to molecular characteristics such as epidermal growth factor receptor (EGFR) mutations[ 20 , 21 ]. Targeted therapies for EGFR and ALK mutations have further enhanced the prognosis of adenocarcinoma patients[ 22 ]. Incorporating pathological type into the prediction model allows for more accurate assessment of a patient’s likelihood of achieving pCR. The role of family history as a predictive factor underscores the influence of genetic predisposition on NSCLC development and progression. A family history of NSCLC suggests genetic susceptibility, with studies identifying rare variants associated with increased disease risk. These variants, often related to tumor suppressor gene function or DNA repair pathways, may play a critical role, particularly in non-smokers or patients with non-squamous subtypes[ 23 , 24 ]. Smoking cessation duration also proved to be a significant factor. Long-term smoking is a well-established risk factor for NSCLC, but quitting smoking can improve lung function, reduce inflammation, and enhance treatment outcomes[ 25 ]. The longer a patient has abstained from smoking, the greater the improvement in lung function and overall survival, highlighting the positive impact of lifestyle changes on therapeutic efficacy[ 26 , 27 ]. However, our research findings show that the longer the smoking cessation duration, the lower the probability of pCR, which also suggests that pCR does not necessarily translate into long-term OS. This phenomenon deserves further in-depth study. Age was another significant predictor, likely reflecting physiological reserves, immune status, and treatment tolerance. Younger patients often exhibit stronger immune responses and faster recovery, contributing to better pCR rates and long-term survival [ 28 ]. The number of neoadjuvant treatment cycles also significantly influenced pCR. Extended treatment cycles provide more thorough therapy, increasing the likelihood of complete tumor remission. However, prolonged treatment may also heighten toxicity risks, necessitating a careful balance between efficacy and patient tolerance when designing individualized treatment plans[ 29 ]. Clinical Implications The nomogram developed in this study integrates multiple clinical parameters to provide a comprehensive and accurate tool for predicting pCR in NSCLC patients. Compared to traditional methods based on clinical staging or single biomarkers, the nomogram offers superior predictive performance. In practice, clinicians can use this tool to assess a patient’s likelihood of achieving pCR, enabling more precise and personalized treatment planning. Limitations and Future Directions This study has several limitations. First, as a single-center retrospective analysis with a relatively small sample size, the generalizability of the findings is limited. External validation using data from other institutions or prospective studies is required. Second, the study primarily focused on clinical parameters and excluded multi-omics data such as radiomics, genomics, and molecular pathology, which could enhance the model’s predictive power. Finally, although internal validation using the bootstrap method confirmed the model’s robustness, external validation remains necessary to establish its reliability. 5. CONCLUSIONS This study successfully constructed and validated a nomogram for predicting pCR in NSCLC patients treated with neoadjuvant chemotherapy combined with immunotherapy. By integrating easily accessible clinical data, the nomogram provides a practical and accurate tool for individualized treatment planning, with the potential to improve patient outcomes. Future studies should focus on external validation and the incorporation of multi-omics data to further refine the model and expand its clinical utility. Declarations AUTHOR CONTRIBUTION Wenyi Liu: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); software (equal); writing-original draft (lead). Zhilin Sui: Data curation (equal); formal analysis (equal); project administration (equal); writing-original draft (equal). Chunguang Wang: Data curation (equal); project administration (equal). Youjun Deng: Data curation (equal). Songhua Cai: Data curation (equal). Ran Jia: Data curation (equal). Zhentao Yu: Funding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal). Mingqiang Kang: Funding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal). Baihua Zhang: Funding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal). ACKNOWLEDGMENTS The abstract has been received as a Poster Presentation at the 33rd Annual Meeting of the Asian Society for Cardiovascular and Thoracic Surgery, Singapore, 14-17 May, 2025. CONFLICT OF INTEREST STATEMENT The authors have no conflicts of interest. DATA AVAILABILITY STATEMENT All raw data and code are available upon request. If anyone would like to request data from this study, they can contact the author, Wenyi Liu, email: [email protected] . FUNDING INFORMATION This study was supported by the Sanming Project of Medicine in Shenzhen (SZSM202211011) and Shenzhen Key Medical Discipline Construction Fund (SZXK075) and the National Natural Science Foundation of China (grant numbers: 82372680 and 82070499), Joint Funds for the Innovation of Science and Technology (2020Y9073) and the Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China. ETHICS STATEMENT This study was approved by the Institutional Review Board (IRB), and informed consent was obtained from all participants. The study complies with the ethical principles outlined in the Declaration of Helsinki (2013 revision). Written informed consent to participate in this study was required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements. 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Tumor immune microenvironment predicts the pathologic response of neoadjuvant chemoimmunotherapy in non-small-cell lung cancer. Cancer Sci. 2023;114:2569–83. Bozcuk H, Abali H, Coskun S, Lung Cancer Committee of Turkish Oncology Group. The correlates of benefit from neoadjuvant chemotherapy before surgery in non-small-cell lung cancer: a metaregression analysis. World J Surg Oncol. 2012;10:161. Chi A, He X, Hou L, Nguyen NP, Zhu G, Cameron RB, et al. Classification of non-small cell lung cancer’s tumor immune micro-environment and strategies to augment its response to immune checkpoint blockade. Cancers. 2021;13:2924. Drilon A, Rekhtman N, Ladanyi M, Paik P. Squamous-cell carcinomas of the lung: emerging biology, controversies, and the promise of targeted therapy. Lancet Oncol. 2012;13:e418–426. Kuribayashi K, Funaguchi N, Nakano T. Chemotherapy for advanced non-small cell lung cancer with a focus on squamous cell carcinoma. J Cancer Res Ther. 2016;12:528–34. Santos ES, Rodriguez E. Treatment considerations for patients with advanced squamous cell carcinoma of the lung. Clin Lung Cancer. 2022;23:457–66. Reungwetwattana T, Eadens MJ, Molina JR. Chemotherapy for non-small-cell lung carcinoma: from a blanket approach to individual therapy. Semin Respir Crit Care Med. 2011;32:78–93. Wang M, Herbst RS, Boshoff C. Toward personalized treatment approaches for non-small-cell lung cancer. Nat Med. 2021;27:1345–56. Naylor EC, Desani JK, Chung PK. Targeted therapy and immunotherapy for lung cancer. Surg Oncol Clin N Am. 2016;25:601–9. Dudbridge F, Brown SJ, Ward L, Wilson SG, Walsh JP. How many cases of disease in a pedigree imply familial disease? Ann Hum Genet. 2018;82:109–13. Miyabe S, Ito S, Sato I, Abe J, Tamai K, Mochizuki M, et al. Clinical and genomic features of non-small cell lung cancer occurring in families. Thorac Cancer. 2023;14:940–52. Perret JL, Walters EH. Cigarette smoking and lung function decline beyond quitting. Ann Transl Med. 2020;8:1531. Bokemeyer F, Lebherz L, Schulz H, Bokemeyer C, Gali K, Bleich C. Smoking patterns and the intention to quit in german patients with cancer: study protocol for a cross-sectional observational study. BMJ Open. 2023;13:e069570. de la Rosa-Carrillo D, de Granda-Orive JI, Diab Cáceres L, Gutiérrez Pereyra F, Raboso Moreno B, Martínez-García M-Á, et al. The impact of smoking on bronchiectasis and its comorbidities. Expert Rev Respir Med. 2024;18:255–68. Liao W, Li Y, Zou Y, Xu Q, Wang X, Li L. Younger patients with colorectal cancer may have better long-term survival after surgery: a retrospective study based on propensity score matching analysis. World J Surg Oncol. 2024;22:59. Wang H, Liang S, Yu Y, Han Y. Efficacy and safety of neoadjuvant immunotherapy protocols and cycles for non-small cell lung cancer: a systematic review and meta-analysis. Front Oncol. 2024;14:1276549. Additional Declarations No competing interests reported. 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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-6532230","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458334987,"identity":"7daad58c-e574-4c89-9c3b-d39fc9f8aaf2","order_by":0,"name":"Wenyi Liu","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Liu","suffix":""},{"id":458334988,"identity":"316f2cf6-8084-4398-9405-aeaf2c1178ab","order_by":1,"name":"Zhilin Sui","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhilin","middleName":"","lastName":"Sui","suffix":""},{"id":458334989,"identity":"1f418e37-6334-4912-bc31-f0522837bcc8","order_by":2,"name":"Chunguang Wang","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chunguang","middleName":"","lastName":"Wang","suffix":""},{"id":458334991,"identity":"3ab9311f-d90b-499a-b3eb-d5b207485ba3","order_by":3,"name":"Youjun Deng","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Youjun","middleName":"","lastName":"Deng","suffix":""},{"id":458334993,"identity":"d21b4640-9264-414b-9993-8d67c3dd5ef0","order_by":4,"name":"Songhua Cai","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Songhua","middleName":"","lastName":"Cai","suffix":""},{"id":458334995,"identity":"decef446-ef0c-4737-94e1-3ad3b5d8a66b","order_by":5,"name":"Ran Jia","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Jia","suffix":""},{"id":458334997,"identity":"76b20732-d48f-4e54-8556-ba3a57ca0785","order_by":6,"name":"Zhentao Yu","email":"","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Zhentao","middleName":"","lastName":"Yu","suffix":""},{"id":458334998,"identity":"3adad55e-30b3-4261-a6f8-9ad5831c82f3","order_by":7,"name":"Mingqiang Kang","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mingqiang","middleName":"","lastName":"Kang","suffix":""},{"id":458334999,"identity":"4e61f92e-47d6-443b-b44d-23b16f4049ff","order_by":8,"name":"Baihua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACAwbGNiAlwcPP3nyAJC0WcpI9xxKI1cLABqQqjA1m5BgQp8Wc/XDbY54aicQNPGc+3njDYCen20BAi2VPYrsxzzGJxO3svZst5zAkG5sdIOSwA4lt0jxsEok7e85uk+ZhOJC4jaCW8w+BWv4BHXYj5xmRWm4AbeFtkzA2uJHDRpwWyxkP2yTn9kmAAtnYco4BEX4x509/JvHmWx0oKh/eeFNhJ0dQCwqQ4CEyapC1kKpjFIyCUTAKRgQAAO/xQcR5880kAAAAAElFTkSuQmCC","orcid":"","institution":"National Cancer Center, National Clinical Research Center for Cancer/Cancer Hospital \u0026 Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Baihua","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-04-26 02:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6532230/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6532230/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83282388,"identity":"76afe486-99b1-4e85-8ecb-4b15d22a245d","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41324,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the patient screening process.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/f04d580eb7b6c584a97d8f66.png"},{"id":83283621,"identity":"5d3286e0-ac70-4318-8276-ba3fa006025f","added_by":"auto","created_at":"2025-05-22 10:52:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24439,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the nomograms.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/712cc879ae29b754fcaf2c6c.png"},{"id":83283307,"identity":"8cc2e098-126b-463a-831d-fdbc82c55d9e","added_by":"auto","created_at":"2025-05-22 10:44:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20179,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the nomograms.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/98aa43bc4c7b9539657f39dd.png"},{"id":83282385,"identity":"58156f5e-5fd2-47f4-90f9-9544491c17ff","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14718,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomograms. Apparent curve: represented by the red dashed line, reflects the net benefit of the model on the training data. Cross-validated curve: represented by the blue dashed line, the net benefit curve obtained by the cross-validation method, providing a more reliable estimate of the model's performance on new data. All: represented by the gray line, the net benefit assuming that all individuals take some action (e.g., all receive treatment or testing). All or nothing (None): represented by the horizontal line, the net benefit assuming that all individuals take no action.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/0c461db4aac0d4f21d2ff4f3.png"},{"id":83282392,"identity":"a44529fa-0fc1-4371-a81e-01ff1f8250bc","added_by":"auto","created_at":"2025-05-22 10:36:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":28723,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for prediction of pCR after neoadjuvant chemotherapy combined with immunotherapy in patients with NSCLC. For each patient, five variables are assigned points on a nomogram, represented by five lines moving upward. The sum of these points is then located on the “Total Points” axis. A line is drawn downward from this point to predict the probability of achieving pCR.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/baa805cc02624bfaffa65b79.png"},{"id":83283309,"identity":"ad06c882-b48e-4d73-84d4-ee3d514b95de","added_by":"auto","created_at":"2025-05-22 10:44:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51882,"visible":true,"origin":"","legend":"\u003cp\u003eRationality analysis based on model probability.\u003c/p\u003e","description":"","filename":"Onlinefloatimage65.png","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/091d5bdcdd80cbe1a2c57dc3.png"},{"id":83486725,"identity":"b404b141-9c9a-4f9e-9add-5af5e4b75d51","added_by":"auto","created_at":"2025-05-27 08:47:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1527679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6532230/v1/7ea4fbe9-be20-460f-b848-38c73894739e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSignificant progress has been made in the neoadjuvant treatment of lung cancer in recent years, particularly with the advent of immunotherapy, which offers new hope to patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Immunotherapy enhances the immune system's ability to combat tumor cells and has become a cornerstone of lung cancer treatment.\u003c/p\u003e \u003cp\u003eThe management of NSCLC has been significantly transformed by the introduction of immune checkpoint inhibitors (ICIs), which have shown remarkable efficacy in various clinical trials. Notably, phase III trials such as CheckMate 816 and KEYNOTE-671 have been pivotal in establishing the role of ICIs in the treatment of NSCLC. These trials have demonstrated that ICIs, when used in combination with chemotherapy, can significantly improve outcomes for patients with resectable NSCLC by increasing the pCR rates and decreasing relapse rates[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research indicates that this combination therapy significantly improves overall survival (OS), disease-free survival (DFS), pCR rates, and major pathological response (MPR) rates. Despite its advantages, not all NSCLC patients benefit from neoadjuvant chemoimmunotherapy, underscoring the importance of accurately identifying those likely to respond before initiating treatment.\u003c/p\u003e \u003cp\u003eBiomarkers play a critical role in patient selection, as their validation and application guide the identification of suitable candidates for neoadjuvant treatment[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. ICIs in particular enhance T-cell effector functions and induce long-term immune memory, providing strong theoretical support for their use in neoadjuvant therapy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Compared to chemotherapy alone, the addition of immunotherapy has demonstrated superior survival benefits, particularly in resectable stage II-IIIB NSCLC, where it outperforms adjuvant chemoimmunotherapy in terms of OS and DFS[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, this combination significantly improves pCR and MPR rates, which are pivotal for achieving favorable long-term outcomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile the potential of neoadjuvant immunotherapy is promising, further investigation is needed to validate its efficacy across diverse patient populations. Large-scale randomized controlled trials are essential to determine the optimal therapeutic regimens that balance efficacy with acceptable toxicity. Concurrently, developing and validating biomarkers remain crucial to guiding treatment decisions and enhancing therapeutic outcomes.\u003c/p\u003e \u003cp\u003eDespite ongoing efforts to identify predictive factors and models for the success of neoadjuvant chemoimmunotherapy, a reliable and user-friendly tool for predicting pCR is still lacking in clinical practice. Accurate pre-treatment identification of patients likely to achieve pCR is vital for improving OS, reducing treatment costs, and avoiding overtreatment.\u003c/p\u003e \u003cp\u003eNomograms provide a practical and visual means of simplifying multifactorial regression models, enabling clinicians to make individualized prognostic predictions based on a patient\u0026rsquo;s multidimensional characteristics. Compared to traditional staging or single-factor indicators, nomograms often deliver superior predictive performance. Given the high incidence of pCR following neoadjuvant treatment in lung cancer and the limited availability of nomograms for pCR prediction, developing a model that integrates pre- and post-treatment parameters holds significant value in identifying candidates likely to achieve pCR.\u003c/p\u003e \u003cp\u003eBased on the above background, this study aims to collect and analyze extensive clinical data from NSCLC patients treated with neoadjuvant chemoimmunotherapy at our hospital. By evaluating multiple variables before and after neoadjuvant treatment, we identify potential predictors of pCR. Using logistic regression analysis, we select relevant parameters to construct a nomogram for pCR prediction, which is subsequently validated internally to assess its performance.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patients\u003c/h2\u003e \u003cp\u003e A retrospective analysis was conducted using data from NSCLC patients who underwent neoadjuvant treatment followed by lung cancer surgery at Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center between 2019 and 2022. Patients were included in the study if they met all of the following criteria: Pathologically diagnosed with NSCLC (including squamous cell carcinoma, adenocarcinoma, or other subtypes) and having complete clinical and pathological data, in accordance with the 8th edition of the AJCC staging system; Pathological staging of II-IIIB stage NSCLC patients; Received platinum-based chemotherapy combined with immune checkpoint inhibitors; Postoperative pathological assessment clearly classified as either pCR or non-pCR.\u003c/p\u003e \u003cp\u003eExclusion criteria included: those who only received neoadjuvant chemotherapy, only received neoadjuvant immunotherapy, interrupted treatment regimens, and those with incomplete data.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Review Committee of the Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center. The study complies with the ethical principles outlined in the Declaration of Helsinki (2013 revision).\u003c/p\u003e \u003cp\u003eThe patient selection process for this study is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Treatment and Follow-up\u003c/h2\u003e \u003cp\u003eThe neoadjuvant treatment regimens included neoadjuvant chemotherapy combined with immunotherapy and neoadjuvant chemotherapy alone. Chemotherapy consisted of paclitaxel combined with platinum-based drugs, while immunotherapy included Keytruda, Nivolumab, and other domestically produced immunotherapeutic agents in China. The multidisciplinary team (MDT) determined the specific neoadjuvant treatment regimen and the number of treatment cycles based on each patient\u0026rsquo;s condition and clinical trial participation.\u003c/p\u003e \u003cp\u003eFollowing neoadjuvant treatment, all patients underwent radical surgery, which included lobectomy, sleeve lobectomy, or pneumonectomy. Postoperative tumor specimens were evaluated by experienced pathologists. Pathological complete response (pCR) was defined as the complete absence of residual cancer cells in all pathological sections of the resected lung tissue and lymph nodes. Patients with any residual cancer cells identified in the final pathology were classified as non-pCR.\u003c/p\u003e \u003cp\u003eAfter surgery, patients underwent routine follow-up: every three months during the first two years, every six months during the subsequent three years, and annually thereafter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Collection\u003c/h2\u003e \u003cp\u003eThe selection of variables was partially based on previously published literature and supplemented by clinical expertise. The following parameters were evaluated as potential predictive factors for pCR: gender, age, presence of symptoms at initial diagnosis, weight loss at initial diagnosis, smoking history, smoking index, smoking cessation duration, alcohol consumption history, comorbidities, family history of cancer, tumor location, tumor type (central or peripheral), clinical T stage (cT), clinical N stage (cN), clinical TNM stage (cTNM), number of neoadjuvant treatment cycles, type of immunotherapy drug, type of chemotherapy drug, type of platinum-based drug, treatment-related adverse reactions, the interval between the end of neoadjuvant therapy and surgery, and pathological tumor subtype.\u003c/p\u003e \u003cp\u003eAge was stratified into \u0026lt;\u0026thinsp;58 years and \u0026ge;\u0026thinsp;58 years based on the X-tile method. Comorbidities were categorized as none, hypertension, diabetes, or other conditions. Immunotherapy drugs included Keytruda, Nivolumab, and other domestically produced immunotherapy agents. Chemotherapy drugs were categorized as paclitaxel or other agents, and platinum-based drugs included cisplatin, carboplatin, or other platinum compounds. Adverse events were classified into none, myelosuppression, hepatic and renal dysfunction, or immune-related adverse reactions, and were graded from 1 to 4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were compared using the chi-square test. Statistical analyses were conducted using the R software package (version 4.4.2) and DCPM software (version 5.49, Jingding Medical Technology Co., Ltd.). A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eUnivariate analysis was performed to reduce dimensionality and retain variables significantly associated with pCR. Significant features were included in a multivariate logistic regression model to derive the predictive equation for pCR. The final regression coefficients were used to construct a nomogram to quantify the contribution of each variable to pCR prediction.\u003c/p\u003e \u003cp\u003eModel evaluation: The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model's discriminative ability. Calibration curves were used to evaluate the consistency between predicted probabilities and actual outcomes. Bootstrap resampling was employed to validate the robustness and reproducibility of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e \u003cp\u003eFrom 2019 to 2022, a total of 375 NSCLC patients received neoadjuvant treatment followed by surgery at our institution. After excluding patients who did not meet the inclusion criteria or had incomplete data, 179 patients were included in the final analysis. Among them, 92 patients (51.4%) achieved pCR, while 87 patients (48.6%) were classified as non-pCR.\u003c/p\u003e \u003cp\u003eThe majority of the patients were male (171 cases, 95.5%), and over half were aged 58 years or older (100 cases, 55.9%). Most patients presented with symptoms at diagnosis, such as coughing or shortness of breath (132 cases, 73.7%), and did not experience weight loss (148 cases, 82.7%). Comorbidities were absent in 113 patients (63.1%). Central-type tumors were predominant (143 cases, 79.9%), and most patients had advanced nodal involvement (cN1-2, 168 cases, 93.9%). The tumor stage distribution was as follows: 38 cases (21.2%) were stage IIB, 83 cases (46.4%) were stage IIIA, and 58 cases (32.4%) were stage IIIB.\u003c/p\u003e \u003cp\u003eNeoadjuvant treatment cycles varied, with 107 patients (59.8%) completing 2 cycles, 54 patients (30.2%) completing 3 cycles, and 18 patients (10.1%) completing 4 cycles. Treatment-related adverse events included myelosuppression (49 cases, 27.4%), hepatic and renal dysfunction (44 cases, 24.6%), and immune-related adverse reactions (24 cases, 13.4%). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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 characteristics and univariate analysis for categorical variables associated with pCR.\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=\"char\" char=\".\" 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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epCR\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;92\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003enon-pCR\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;87\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.553(0.111\u0026ndash;2.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (96.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (94.25%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.75%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.42\u0026thinsp;\u0026plusmn;\u0026thinsp;6.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.89\u0026thinsp;\u0026plusmn;\u0026thinsp;7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.032(0.99\u0026ndash;1.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge stratification, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79(0.436\u0026ndash;1.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (58.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (52.87%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (41.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (47.13%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.441(0.74\u0026ndash;2.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (22.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (29.89%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (77.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (70.11%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight loss, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.182(0.544\u0026ndash;2.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (81.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (83.91%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (18.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (16.09%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.762(0.564\u0026ndash;6.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (5.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.20%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (94.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (90.80%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking cessation duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 [0;37.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [0;165]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1(0.999-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol history, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.451(0.484\u0026ndash;4.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.20%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (93.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (90.80%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.951(0.697\u0026ndash;1.295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (61.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (64.37%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (18.39%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.75%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (5.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.49%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.48(1.945\u0026ndash;194.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (89.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (98.85%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.15%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Site, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938(0.767\u0026ndash;1.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft upper lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (21.84%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft lower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (16.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (18.39%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight upper lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (29.89%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight middle lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.30%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight lower lobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (22.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (27.59%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor type, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.932(0.447\u0026ndash;1.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (80.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (79.31%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (19.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.69%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.216(0.869\u0026ndash;1.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (5.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.75%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (34.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (42.53%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (33.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (32.18%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (19.54%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.921(0.566\u0026ndash;1.491)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (4.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.90%)\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (39.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (29.89%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51 (55.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (63.22%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.09%)\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\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\u003ecTNM, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.988(0.658\u0026ndash;1.483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (21.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.69%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (45.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (47.13%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (32.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (32.18%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment cycle, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.479(0.95\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (54.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (65.52%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (32.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (27.59%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (13.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.90%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunization drugs, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.324(0.896\u0026ndash;1.977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKeytruda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (11.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.69%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNivolumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (11.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.34%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 (76.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (68.97%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapeutic drugs, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.502(0.209\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (89.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (80.46%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (10.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (19.54%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatinum drugs, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917(0.426\u0026ndash;1.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCisplatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (18.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (16.09%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarboplatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (80.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (83.91%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.09%)\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\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\u003eSide effects stratification, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894(0.481\u0026ndash;1.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (35.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (33.33%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59 (64.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (66.67%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSide effects, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.933(0.704\u0026ndash;1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (35.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (33.33%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyelosuppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (30.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (24.14%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic and renal function abnormalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (18.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (31.03%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune-related side effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (15.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.49%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrading of side effects, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.013(0.772\u0026ndash;1.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (35.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (33.33%)\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (28.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (36.78%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.69%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.45%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (3.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.75%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.5 [29;43.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 [28;40.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.007(0.989\u0026ndash;1.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime interval stratification, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.263(0.635\u0026ndash;2.539)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (73.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (78.16%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (26.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (21.84%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology, n (%)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.413(0.194\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82 (89.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (74.71%)\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 \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (9.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.69%)\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4.60%)\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 \u003ctd align=\"left\" colname=\"c6\"\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Univariate and Multivariate Analysis\u003c/h2\u003e \u003cp\u003eUnivariate analysis identified multiple factors associated with pCR, including pathological type, chemotherapy drug type, immunotherapy drug type, treatment cycle count, family history of cancer, smoking cessation duration, smoking index, and age. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis revealed five independent predictors of pCR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.Pathological type: Squamous cell carcinoma was associated with higher pCR rates compared to adenocarcinoma.\u003c/h3\u003e\n\n\u003ch3\u003e2.Family history of cancer: A positive family history significantly increased the likelihood of achieving pCR.\u003c/h3\u003e\n\n\u003ch3\u003e3.Quit smoking days: Shorter smoking cessation durations were associated with higher pCR rates.\u003c/h3\u003e\n\n\u003ch3\u003e4.Age: Older patients demonstrated slightly higher pCR rates.\u003c/h3\u003e\n\n\u003ch3\u003e5.Treatment cycle count: A higher number of treatment cycles correlated with increased pCR rates.\u003c/h3\u003e\n\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\u003eMultivariate analysis for variables associated with pCR.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\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\u003e1.053 (1.005\u0026ndash;1.106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking cessation duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.999\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.76 (1.903\u0026ndash;203.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.621 (1.007\u0026ndash;2.661)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.344 (0.151\u0026ndash;0.707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Receiver operating characteristic Curve Analysis, Calibration Analysis, Decision Curve Cross-validation Analysis\u003c/h2\u003e \u003cp\u003eThe multivariate model demonstrated good predictive performance, with an AUC of 0.709 (95% CI: 0.633\u0026ndash;0.785). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe calibration curve indicated strong agreement between predicted probabilities and observed outcomes, particularly in most probability ranges, although minor deviations were noted between probabilities of 0.5 and 0.8. Internal validation using 500 bootstrap resamplings confirmed the model's robustness. (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe decision curve analysis indicates that within a certain range of threshold probabilities, the model's predictions can bring about net benefits, especially in the low threshold probability area. However, in the high threshold probability area, the model's predictions may not be as good as taking no action at all. Through this analysis, we can determine the optimal threshold probability for the model in practical applications to maximize net benefits. (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Nomogram\u003c/h2\u003e \u003cp\u003eBased on the coefficients of multivariate logistic regression, a nomogram was established to predict pCR. The nomogram shows that if a patient scores 256 points, combining five variables, the predicted probability of pCR can be as high as 90% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The rationality analysis based on model probability (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) also shows that the model's predictive ability is better than that of any single-factor variable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment landscape for lung cancer, particularly non-small cell lung cancer (NSCLC). These advancements have significantly improved treatment and management strategies for the disease. Meta-analyses have demonstrated the efficacy and safety of neoadjuvant immunotherapy in resectable NSCLC, presenting an encouraging outlook. Moreover, real-world studies have evaluated the clinical outcomes of ICIs following neoadjuvant treatment in resectable NSCLC patients, focusing on prognostic factors and survival prediction. Research has also highlighted the potential benefits of neoadjuvant immunotherapy in NSCLC patients with tumor gene mutations, although further investigations are warranted to validate its effectiveness in this subgroup. Compared to chemotherapy, neoadjuvant immunotherapy has shown superior efficacy and reduced toxicity in advanced NSCLC, supporting its integration into early-stage resectable NSCLC treatment. Additionally, neoadjuvant immunotherapy is emerging as a promising strategy for treating locally advanced resectable NSCLC, with studies exploring its safety, efficacy, and comparative outcomes against neoadjuvant chemoimmunotherapy or chemotherapy alone. Systematic reviews and meta-analyses underscore the benefits of adding immunotherapy to neoadjuvant chemotherapy for locally advanced NSCLC, emphasizing the importance of combination therapy. Furthermore, network meta-analyses comparing neoadjuvant immunotherapy with chemotherapy or perioperative immunotherapy have highlighted the transformative potential of immunotherapy in this context.\u003c/p\u003e \u003cp\u003eIdentifying which non-small cell lung cancer (NSCLC) patients will benefit from neoadjuvant chemoimmunotherapy is crucial for optimizing treatment outcomes and avoiding unnecessary side effects. Recent studies have highlighted various approaches to predict patient responses to this treatment modality.\u003c/p\u003e \u003cp\u003eOne promising approach is the use of blood biomarkers to predict complete pathological response in NSCLC patients undergoing neoadjuvant chemoimmunotherapy. In the NADIM clinical trial, researchers identified specific immune parameters in peripheral blood that were associated with complete pathological response, suggesting that these biomarkers could be used to stratify patients before treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearchers have also turned to advanced imaging techniques to predict treatment success. A deep learning model built on computed tomography (CT) imaging data has shown remarkable accuracy in predicting pathologic complete response to neoadjuvant chemoimmunotherapy in NSCLC. This innovation highlights how imaging data could become a key tool in identifying suitable candidates for this aggressive treatment strategy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother study explored the use of delta-radiomics features, which are based on the relative net change of radiomics features between baseline and preoperative CT scans. This approach showed superior predictive performance compared to pre-treatment radiomics models and could serve as a noninvasive biomarker for predicting major pathological response to neoadjuvant chemoimmunotherapy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a single-center study, scientists developed a predictive score for major pathological response using pre-treatment parameters such as prothrombin time, neutrophil percentage, and PD-L1 expression. This model offers a practical framework for personalized decision-making in operable NSCLC treated with neoadjuvant chemoimmunotherapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment also plays a significant role in predicting the pathologic response of neoadjuvant chemoimmunotherapy in NSCLC. A study found that specific patterns of tumor-infiltrating lymphocytes were associated with favorable pathological responses, underscoring the value of immune profiling in treatment planning [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInsights from a meta-regression analysis of randomized clinical trials reveal that the benefits of neoadjuvant chemotherapy in NSCLC are most pronounced in stage 3 disease when protocols include three chemotherapeutic agents. This finding reinforces the importance of aligning treatment strategies with disease stage and chemotherapy protocols [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese studies collectively emphasize the importance of predictive biomarkers and advanced imaging techniques in identifying NSCLC patients who are likely to benefit from neoadjuvant chemoimmunotherapy, thereby enhancing treatment efficacy and minimizing unnecessary exposure to potential side effects.\u003c/p\u003e \u003cp\u003eUsing real-world clinical data, this study successfully developed a nomogram model to predict pathological complete response (pCR) in NSCLC patients receiving neoadjuvant chemotherapy combined with immunotherapy. By retrospectively collecting data from patients treated between 2019 and 2022, the model was constructed through an in-depth analysis of clinical parameters, including age, gender, smoking history, comorbidities, neoadjuvant treatment regimens, and adverse reactions. Multivariate analysis identified five significant predictors of pCR: pathological type, family history of tumors, smoking cessation duration, age, and the number of neoadjuvant treatment cycles. These findings provide new insights into the factors influencing pathological responses in NSCLC patients undergoing combination therapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eKey Predictive Factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePathological type emerged as a pivotal predictor of pCR. NSCLC subtypes, including squamous cell carcinoma (SCC) and adenocarcinoma, exhibit distinct molecular features, biological behaviors, and treatment sensitivities. SCC demonstrates higher sensitivity to immunotherapy, likely due to its unique molecular characteristics[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Studies indicate that ICIs yield significant therapeutic benefits in SCC patients, though their response to traditional chemotherapy may be less robust[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent advancements in immunotherapy and targeted therapies offer new hope for improving SCC outcomes[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Conversely, adenocarcinoma tends to respond more favorably to platinum-based chemotherapy, potentially due to molecular characteristics such as epidermal growth factor receptor (EGFR) mutations[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Targeted therapies for EGFR and ALK mutations have further enhanced the prognosis of adenocarcinoma patients[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Incorporating pathological type into the prediction model allows for more accurate assessment of a patient\u0026rsquo;s likelihood of achieving pCR.\u003c/p\u003e \u003cp\u003eThe role of family history as a predictive factor underscores the influence of genetic predisposition on NSCLC development and progression. A family history of NSCLC suggests genetic susceptibility, with studies identifying rare variants associated with increased disease risk. These variants, often related to tumor suppressor gene function or DNA repair pathways, may play a critical role, particularly in non-smokers or patients with non-squamous subtypes[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSmoking cessation duration also proved to be a significant factor. Long-term smoking is a well-established risk factor for NSCLC, but quitting smoking can improve lung function, reduce inflammation, and enhance treatment outcomes[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The longer a patient has abstained from smoking, the greater the improvement in lung function and overall survival, highlighting the positive impact of lifestyle changes on therapeutic efficacy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, our research findings show that the longer the smoking cessation duration, the lower the probability of pCR, which also suggests that pCR does not necessarily translate into long-term OS. This phenomenon deserves further in-depth study.\u003c/p\u003e \u003cp\u003eAge was another significant predictor, likely reflecting physiological reserves, immune status, and treatment tolerance. Younger patients often exhibit stronger immune responses and faster recovery, contributing to better pCR rates and long-term survival [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe number of neoadjuvant treatment cycles also significantly influenced pCR. Extended treatment cycles provide more thorough therapy, increasing the likelihood of complete tumor remission. However, prolonged treatment may also heighten toxicity risks, necessitating a careful balance between efficacy and patient tolerance when designing individualized treatment plans[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical Implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe nomogram developed in this study integrates multiple clinical parameters to provide a comprehensive and accurate tool for predicting pCR in NSCLC patients. Compared to traditional methods based on clinical staging or single biomarkers, the nomogram offers superior predictive performance. In practice, clinicians can use this tool to assess a patient\u0026rsquo;s likelihood of achieving pCR, enabling more precise and personalized treatment planning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations and Future Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as a single-center retrospective analysis with a relatively small sample size, the generalizability of the findings is limited. External validation using data from other institutions or prospective studies is required. Second, the study primarily focused on clinical parameters and excluded multi-omics data such as radiomics, genomics, and molecular pathology, which could enhance the model\u0026rsquo;s predictive power. Finally, although internal validation using the bootstrap method confirmed the model\u0026rsquo;s robustness, external validation remains necessary to establish its reliability.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eThis study successfully constructed and validated a nomogram for predicting pCR in NSCLC patients treated with neoadjuvant chemotherapy combined with immunotherapy. By integrating easily accessible clinical data, the nomogram provides a practical and accurate tool for individualized treatment planning, with the potential to improve patient outcomes. Future studies should focus on external validation and the incorporation of multi-omics data to further refine the model and expand its clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenyi Liu:\u0026nbsp;\u003c/strong\u003eConceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); software (equal); writing-original draft (lead).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhilin Sui:\u003c/strong\u003e Data curation (equal); formal analysis (equal); project administration (equal); writing-original draft (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChunguang Wang:\u003c/strong\u003e Data curation (equal); project administration (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYoujun Deng:\u0026nbsp;\u003c/strong\u003eData curation (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSonghua Cai:\u0026nbsp;\u003c/strong\u003eData curation (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRan Jia:\u0026nbsp;\u003c/strong\u003eData curation (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZhentao Yu:\u003c/strong\u003e Funding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMingqiang Kang:\u003c/strong\u003e Funding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaihua Zhang:\u0026nbsp;\u003c/strong\u003eFunding acquisition (equal); data curation (equal); formal analysis (equal); investigation (equal); project administration (equal); writing-review and editing (equal).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe abstract has been received as a Poster Presentation at the 33rd Annual Meeting of the Asian Society for Cardiovascular and Thoracic Surgery, Singapore, 14-17 May, 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data and code are available upon request. If anyone would like to request data from this study, they can contact the author, Wenyi Liu, email:[email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Sanming Project of Medicine in Shenzhen (SZSM202211011) and Shenzhen Key Medical Discipline Construction Fund (SZXK075) and the National Natural Science Foundation of China (grant numbers: 82372680 and 82070499), Joint Funds for the Innovation of Science and Technology (2020Y9073) and the Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University), Fujian Province University, Fuzhou, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board (IRB), and informed consent was obtained from all participants. The study complies with the ethical principles outlined in the Declaration of Helsinki (2013 revision). Written informed consent to participate in this study was required from the participants or the participants\u0026rsquo; legal guardians/next of kin in accordance with the national legislation and the institutional requirements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eForde PM, Chaft JE, Smith KN, Anagnostou V, Cottrell TR, Hellmann MD, et al. Neoadjuvant PD-1 blockade in resectable lung cancer. N Engl J Med. 2018;378:1976\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiano I, Abuali I, Sharma A, Durant J, Dragnev KH. Role of Neoadjuvant Immune Checkpoint Inhibitors in Resectable Non-Small Cell Lung Cancer. Pharmaceuticals. 2023;16:233.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunne EG, Fick CN, Isbell JM, Chaft JE, Altorki N, Park BJ, et al. 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Front Oncol. 2024;14:1276549.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"non-small cell lung cancer, neoadjuvant chemotherapy combined with immunotherapy, pathological complete response, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6532230/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6532230/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe application of neoadjuvant immunotherapy combined with chemotherapy in intermediate and high-risk resectable non-small cell lung cancer (NSCLC) is increasing, with some patients achieving pathological complete response (pCR). However, there is no effective tool for clinical precision prediction of pCR. This study aims to integrate clinical data to develop and construct a nomogram model capable of predicting the risk of pCR in NSCLC patients following neoadjuvant chemotherapy combined with immunotherapy, thereby providing a reference for individualized treatment decisions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on patients with resectable NSCLC who underwent neoadjuvant chemotherapy combined with immunotherapy between 2019 and 2022. Inclusion criteria were as follows: diagnosis of NSCLC (stages IIB\u0026ndash;IIIB), completion of neoadjuvant chemotherapy and immunotherapy followed by surgical resection, and a definitive postoperative pathological assessment. Clinical parameters such as age, gender, smoking history, comorbidities, neoadjuvant treatment regimens, and treatment-related adverse events were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR, which were subsequently used to construct a nomogram model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 179 patients were included in the study, comprising 92 cases (51.4%) in the pCR group and 87 cases (48.6%) in the non-pCR group. Multivariate analysis identified the following factors as significantly associated with pCR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05): pathological type, family history of tumors, duration of smoking cessation, age, and number of neoadjuvant treatment cycles. A multivariate logistic regression model incorporating these factors demonstrated an area under the curve (AUC) of 0.709 (95% CI: 0.633\u0026ndash;0.785), indicating good predictive performance. The calibration curve showed strong agreement between predicted probabilities and observed outcomes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBased on real clinical data, this study developed and constructed a nomogram to predict pCR in NSCLC patients after neoadjuvant chemotherapy combined with immunotherapy. This tool not only performs well in terms of discrimination and calibration but also has potential clinical application value, providing new ideas and basis for the formulation of individualized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Nomogram for Predicting Pathological Complete Response to Neoadjuvant Chemotherapy Combined with Immunotherapy in NSCLC Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:35:57","doi":"10.21203/rs.3.rs-6532230/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f68d38d6-f1fc-4e64-b29a-564ae76e5413","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-27T08:39:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 10:35:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6532230","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6532230","identity":"rs-6532230","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}