Prognostic Significance of the LSUVmax–IPI Composite Score on Overall Survival in Metastatic Non–Small Cell Lung Cancer

Prognostic Significance of the LSUVmax–IPI Composite Score on Overall Survival in Metastatic Non–Small Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Significance of the LSUVmax–IPI Composite Score on Overall Survival in Metastatic Non–Small Cell Lung Cancer Evrican Zin Guzel, Ferhat Ekinci, Atike Pinar Erdogan, Mustafa Sahbazlar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8254045/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 Non–small cell lung cancer (NSCLC) is often diagnosed at metastatic stages, and overall prognosis remains poor. Reliable biomarkers reflecting both tumor metabolism and systemic inflammation are needed. This study investigates the prognostic significance of the LSUVmax–IPI composite index in metastatic NSCLC. Methods A total of 137 metastatic NSCLC patients who underwent pre-treatment 18F-FDG PET/CT were retrospectively analyzed. LSUVmax–IPI was calculated as SUVmax × IPI, and patients were categorized using a cut-off value of 4.4. Clinical, laboratory, metabolic, and survival variables were compared. Overall survival (OS) and progression-free survival (PFS) were analyzed using the Kaplan–Meier method. Results Patients with LSUVmax–IPI > 4.4 had significantly higher metabolic activity and inflammatory markers. Median OS was 28 months in the ≤ 4.4 group and 16 months in the > 4.4 group (p = 0.002). PFS showed a trend toward shorter duration in the > 4.4 group, though not statistically significant (p = 0.098). Conclusions LSUVmax–IPI is a practical prognostic index reflecting both tumor aggressiveness and systemic inflammation. Higher scores were strongly associated with poorer OS. Further validation in prospective studies is required. non–small cell lung cancer SUVmax inflammatory prognostic index PET/CT systemic inflammation prognosis Figures Figure 1 Figure 2 SIMPLE SUMMARY In metastatic non–small cell lung cancer (NSCLC), predicting disease course remains challenging, and survival rates are still limited despite advances in systemic therapies. Biomarkers that integrate tumor metabolic activity and the patient’s systemic inflammatory status may improve prognostic accuracy. This study evaluates the LSUVmax–IPI composite score, calculated by multiplying the primary tumor SUVmax by the Inflammatory Prognostic Index (IPI), in patients with metastatic NSCLC. Higher LSUVmax–IPI values were strongly associated with poorer survival, suggesting that this score may support risk stratification in clinical practice. Introduction Non–small cell lung cancer (NSCLC) accounts for the majority of lung cancer–related deaths worldwide, and most patients are diagnosed at metastatic stages [ 1 ]. Despite major therapeutic advances, survival outcomes remain limited, underscoring the need for novel prognostic markers that more accurately reflect tumor biology. 18F-FDG PET/CT is widely used for staging and assessing tumor metabolic activity. Although SUVmax provides information on glycolytic activity, its prognostic value varies across studies [ 2 , 3 ]. Systemic inflammation also plays a key role in cancer progression. Biomarkers such as the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and albumin reflect host inflammatory status. The Inflammatory Prognostic Index (IPI) integrates these markers and has demonstrated prognostic value in several malignancies [ 4 , 5 ]. Among studies combining metabolic and inflammatory parameters, Kolkıran et al. were the first to propose the SUVmax–IPI score, showing that this composite index independently predicted overall survival in nivolumab-treated metastatic NSCLC patients [ 6 ]. The aim of this study is to evaluate the prognostic value of SUVmax–IPI in a metastatic NSCLC population regardless of treatment modality or metastatic burden. Methods This study was approved by the Health Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine . We confirm that all procedures were conducted in accordance with relevant national and international guidelines, regulations, and the principles of the Declaration of Helsinki. Due to the retrospective design of the study, the requirement for informed consent was waived by the Health Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine. This retrospective, single-center study included 137 metastatic NSCLC patients diagnosed at Manisa Celal Bayar University between January 2015 and June 2024. All patients had histologically or cytologically confirmed NSCLC and underwent pre-treatment 18F-FDG PET/CT imaging. Histological classification was performed according to WHO criteria, and staging was determined using the AJCC 8th edition TNM system based on PET/CT, cranial MRI, and, when necessary, bronchoscopy or surgical pathology findings. Only stage IV patients with complete pre-treatment PET/CT, complete blood count, biochemical tests, and inflammatory markers were included. Exclusion criteria comprised the presence of a second primary malignancy, incomplete clinical or laboratory data, or initiation of treatment at an external center. Clinical, laboratory, radiological, and treatment-related variables were extracted from electronic medical records. Molecular profiling was performed using next-generation sequencing, evaluating EGFR, ALK, ROS1, and other clinically relevant mutations. PD-L1 expression was assessed using the tumor proportion score (TPS). FDG-PET/CT acquisition and analysis followed international standards, with imaging performed after fasting and ensuring adequate glycemic control, using a GE Discovery IQ system. Metabolic lesion segmentation was conducted semi-automatically using a 41% SUVmax isocontour, and SUVmax values were derived from the most metabolically active lesion. The Inflammatory Prognostic Index (IPI) was calculated as: IPI = (CRP × NLR) / albumin, and the composite SUVmax–IPI score was calculated as SUVmax × IPI. ROC analysis demonstrated significant prognostic performance for this score (AUC = 0.679; p = 0.004), and the optimal cut-off value was determined as 4.4. Categorical variables were summarized as frequencies and percentages, whereas continuous variables were reported as medians with interquartile ranges. PFS and OS were analyzed using the Kaplan–Meier method and compared using the log-rank test. Potential prognostic variables were first screened using univariate Cox regression analyses, and significant variables were included in multivariate Cox models. Results were reported as adjusted hazard ratios (aHRs) with 95% confidence intervals. Statistical analyses were performed using SPSS version 27, and a p-value < 0.05 was considered statistically significant. Results İPİ LSUVMAX*İPİ Area under the ROC curve (AUC) 0,640 0,679 95% Confidence interval 0,554 to 0,719 0,594 to 0,756 Significance level P (Area=0.5) 0,0293 0,0044 Youden index J 0,2524 0,3198 Associated criterion >0,79 >4,4 Sensitivity 67,54 81,98 Specificity 57,69 50,00 Abbreviations:ROC: receiver operating characteristic, : LSUVmax–IPI: Composite index calculated as SUVmax × Inflammatory Prognostic Index,IPI: Inflammatory Prognostic Index Patient Characteristics The analysis included 137 patients. When the LSUVmax–IPI ≤4.4 and >4.4 groups were compared, sex, age, smoking pack-years, BMI, and LVEF showed no significant differences. ECOG performance status, age categories, disease stage at diagnosis, and histopathological subtypes were also similar between the groups (all p > 0.05). Smoking status, alcohol consumption, BMI categories, and comorbidities such as hypertension, diabetes, coronary artery disease, chronic kidney disease, and COPD/asthma likewise did not differ significantly. Metastatic sites (liver, lung, bone, brain, distant lymph nodes) and rates of palliative radiotherapy, chemoradiotherapy, and adjuvant chemotherapy were comparable (p > 0.05). A borderline difference was observed in the number of treatment lines (≥4 lines: 71.0% vs. 53.5%; p = 0.099). Progression rates were identical in both groups (78.1%; p = 0.997). In contrast, surgical intervention was significantly more common in the ≤4.4 group (25.0% vs. 5.8%; p = 0.004), and mortality was significantly higher in the >4.4 group (86.7% vs. 62.5%; p = 0.004). Table 2. Clinical and Demographic Characteristics According to LSUVmax–IPI Groups (≤4.4 vs >4.4) LSUVMAX*İPİ ≤ 4,4 n (%) > 4,4 n (%) p Gender Male 25 (78,1) 92 (87,6) 0,250 Female 7 (21,9) 13 (12,4) Age at Diagnosis(years) 63.16 ± 8.89 65 (47–80) 62.19 ± 7.20 63 (40–75) 0.505 Smoking history (pack-years) 37.44 ± 33.80 30 (0–190) 34.09 ± 21.01 40 (0–100) 0.764 BMI (kg/m²) 26.03 ± 4.34 26.8 (17.3–37.3) 26.05 ± 4.30 25.4 (16.2–38.3) 0.949 Echo EF (%) 58.75 ± 2.84 60 (50–65) 58.74 ± 5.13 60 (22–65) 0.233 ECOG performance 0 1 (3,1) 5 (4,8) 1,000 0-1 31 (96,9) 100 (95,2) Age at Diagnosis > 65 year 17 (53,1) 44 (41,9) 0,312 < 65 year 15 (46,9) 61 (58,1) Stage at initial Diagnosis Stage 1–2 4 (12,5) 4 (3,8) 0,129 stage3 5 (15,6) 26 (24,8) stagee 4 23 (71,9) 75 (71,4) Histological Type Adenocarcinoma 20 (62,5) 55 (52,4) 0,515 Squamous cell 11 (34,4) 48 (45,7) Other 1 (3,1) 2 (1,9) Smoking status smoker 12 (37,5) 49 (46,7) 0,429 Ex smoker 16 (50,0) 39 (37,1) Non smoker 4 (12,5) 17 (16,2) Alcohol Use Non-user 29 (90,6) 102 (97,1) 0,226 Other (regular / social / former) 3 (9,4) 3 (2,9) BMI < 25 kg/m² 13 (40,6) 47 (44,8) 0,680 ≥ 25 kg/m² 19 (59,4) 58 (55,2) Hypertension present 7 (21,9) 31 (29,8) 0,500 Diabetes Mellitus present 9 (28,1) 17 (16,2) 0,196 CAD present 4 (12,5) 15 (14,3) 1,000 CKD present 3 (9,4) 6 (5,7) 0,436 COPD / Asthma present 5 (15,6) 26 (24,8) 0,341 Liver Metastasis present 8 (25,0) 17 (16,2) 0,298 Lung Metastasis present 28 (87,5) 85 (81,0) 0,595 Bone Metastasis present 17 (53,1) 61 (58,1) 0,685 Brain Metastasis present 12 (37,5) 25 (23,8) 0,171 Distant lymph node Metastasis present 9 (28,1) 41 (39,0) 0,300 Palliative radiotherapy received 19 (61,3) 56 (53,3) 0,539 Concurrent chemoradiotherapy received 15 (48,4) 42 (40,0) 0,416 Surgery received 8 (25,0) 6 (5,8) 0,004 Adjuvant chemotherapy received 8 (30,8) 25 (29,8) 1,000 ≥4 lines of systemic therapy ≥ 4 lines 22 (71,0) 54 (53,5) 0,099 Progression present 25 (78,1) 82 (78,1) 0,997 Death Yes 20 (62,5) 91 (86,7) 0,004 Data are presented as number (n) of patients and percentage (%). Abbreviations: LSUVmax–IPI: Composite index calculated as SUVmax × Inflammatory Prognostic Index, BMI: Body Mass Index, CAD: Coronary Artery Disease, CKD: Chronic Kidney Disease, COPD: Chronic Obstructive Pulmonary Disease, ECOG: Eastern Cooperative Oncology Group, EF: Ejection Fraction IPI: Inflammatory Prognostic Index, LN: Lymph Node, NSCLC: Non–Small Cell Lung Cancer, RT: Radiotherapy, KRT / CCRT: Concurrent Chemoradiotherapy, SUVmax: Maximum Standardized Uptake Value, ALK: Anaplastic Lymphoma Kinase Univariate and Multivariate Analysis of Progression-Free Survival and Overall Survival Cox regression analyses were performed to identify potential prognostic factors associated with PFS and OS. In the univariate analysis, the LSUVMAX–IPI score emerged as a significant predictor of progression (p = 0.003). In addition, ECOG performance status (0–1) was associated with a higher risk of progression (HR: 3.163; 95% CI: 1.567–6.384; p = 0.001). Non-smokers demonstrated a significantly increased progression risk compared with former smokers (HR: 1.869; 95% CI: 1.064–3.284; p = 0.030). Regarding disease stage, patients in stages 2–4 exhibited a lower risk of progression compared with those in stage 0 (p < 0.05). In the multivariate analysis, ECOG 0–1 remained an independent prognostic factor (HR: 6.277; p = 0.005), and the reduced progression risk in advanced-stage patients was confirmed. Backward stepwise analysis yielded similar results, with ECOG performance status and disease stage persisting as the strongest independent predictors of progression Table3.Cox regression analysis of progression-free survival. Univariate Multivariate p HR (95% Cl Min–Max) p HR (95% Cl Min–Max) Gender Female 0.456 1.212(0.731-.2.010) Age at Diagnosis 0.513 0.993(0.971-1.015) Smoking Status Ex-Smoker 0.070 smoker 0.109 1.395(0.928-2.097) Nonsmoker 0.030 1.869(1.064-3.284) Histological Type Ref: Adenocarcinoma 0.475 Squamous cell 0.833 0.960(0.655-1.406) Mixt 0.181 3.906(0.531-28.757) Other 0.436 1.754(0.427-7.211) BMI 0.144 0.968(0.926-1.011) BMI (Ref:>25 ) <25 0.409 1.170(0.805-1.701) ALK (Ref:negative) positive 0.148 1.714(0.825-3.558) PDL (Ref:negative) Positive 0.951 1.031(0.390-2.725) Stage at Initial Diagnosis Ref: 0 0.002 0,010 stage 2 0.009 0.052(0.06-0.485) 0.036 0.084(0.008-.0848) stage3 0.002 0.038(0.05-0.313) 0.005 0.046(0.005-0.392) stage4 0.016 0.080(0.010-0.628) 0.028 0.097(0.012-0.782) ECOG_categorize(Ref: Ecog 0) Ecog 0-1 0.001 3.163(1.567-6.384) 0.009 5.019(1.496-16.837) İPİ 0.587 0.997(0.984-1.009) PET LMax SUV Max 0.325 1.003(0.997-1.008) PET MET Max SUV Max 0.737 1.002(0.988-1.017) ABBREVIATIONS:HR: Hazard Ratio,CI: Confidence Interval,BMI: Body Mass Index,ALK: Anaplastic Lymphoma Kinase,PD-L1: Programmed Death-Ligand 1,ECOG: Eastern Cooperative Oncology Group,SUVmax: Maximum Standardized Uptake Value,PET: Positron Emission Tomography,IPI: Inflammatory Prognostic Index,NSCLC: Non–Small Cell Lung Cancer In the univariate analyses, an increase in the LSUVMAX–IPI value was significantly associated with a higher risk of mortality (HR: 1.001; 95% CI: 1.000–1.001; p = 0.003). The IPI score similarly demonstrated a significant association with increased mortality risk (HR: 1.014; p = 0.014). In addition, a higher number of treatment lines was identified as a protective factor against mortality (HR: 0.686; p = 0.014). In the multivariate analysis, which included variables with p 4.4 remained independently and significantly associated with mortality (HR: 2.383; 95% CI: 1.437–3.952; p = 0.001). Moreover, BMI <25 (p = 0.027) and the presence of bone metastasis (p = 0.022) were identified as independent predictors of mortality. Table 4. Cox regression analysis of exitus Univariate Multivariate p HR (95% Cl Min–Max) p HR (95% Cl Min–Max) Gender(ref: Female) male 0.170 1.437(0.856-2.410) 0.089 1.654(0.926-2.953) Age at Diagnosis 0.164 1.016 (0.994–1.039) Smoking Status Ex-Smoker 0.146 Smoker 0.105 1.392(0.933-2.076) Nonsmoker 0.093 1.577(0,926-2,684) Histological Type Ref: Adenocarcinoma 0.404 Squamous cell 0.285 1.225 (0.844-1.777) mikst 0.182 3.885(0.528-28558) ALK (Ref:negative) positive 0.500 1.277(0.627-2.603) PDL 1 (ref: negatif) pozitive 0.982 0.983(0.220-4.387) Stage at Initial Diagnosis Ref:0 0.893 Stage 2 0.903 Stage 3 0.897 Stage 4 0.896 Liver Metastasis 0.053 1.532(0.994-2.301) Lung Metastasis 0.535 1.167(0.717-1.900) Bone Metastasis 0.173 1.290(0.894-1.861) 0.022 1.623(1.071-2.460) Brain Metastasis 0.694 0.922(0.615-1.382) BMI 0.263 0.977(0.939-1.017) Distant Lymph Node Metastasis 0.941 0.986(0.677-1.436) PET MET Max SUV Max 0.168 1.010(0.996-1.024) Total Number of Treatment Lines Received (ref ≥4 lines) ˂0.001 0.686(0.561-0.839) ≤3 lines 0.854 1.036(0.714-1.501) LSUVMAX*İPİ ˃4.4 0.004 2.062(1.267-3.355) 0.001 2.383(1.437-3.952) İPİ ˃0.79 0.002 1.852(1.245-2.755) Abbreviations: HR: Hazard Ratio,CI: Confidence Interval,BMI: Body Mass Index,ALK: Anaplastic Lymphoma Kinase,PD-L1: Programmed Death-Ligand 1,PET: Positron Emission Tomography,SUVmax: Maximum Standardized Uptake Value,IPI: Inflammatory Prognostic Index, LSUVmax–IPI: Composite score calculated as SUVmax × IPI Survival Outcomes The median overall survival was 28 months in the LSUVMAX–IPI ≤4.4 group and 16 months in the >4.4 group (p = 0.002). One-year overall survival rates were 84.3% in the ≤4.4 group compared with 58.4% in the >4.4 group. These findings indicate that an LSUVMAX–IPI value ≤4.4 is a strong prognostic marker for improved survival outcomes. Overall Survival % (SE) 1-year 3 -year 5-year 10-year LSUVMAX*İPİ ≤4.4 84.3% (6.5) 43.6% (10.4) 29.9% (10.8) * >4.4 58.4% (4.9) 18,3% (4,1) 6.1% (2.9) 2.0% (1.9) Overall 65.1% (3.9) 25.7% (4.0) 11.8% (3.3) 3.0% (2.0) Table5. Median survival time according to LSUVmax–IPI Groups Table 6. Overall Survival % according to LSUVmax–IPI Groups Median Survival Time Estimate SE 95% CI Lower Bound Upper Bound LSUVMAX*İPİ ≤4.4 28 5.1 18 38 >4.4 16 1.3 13.4 18.6 Overall 17 1.5 14.2 19.8 ABBREVIATIONS:OS: Overall Survival,SE: Standard Error,CI: Confidence Interval,LSUVmax–IPI: Composite index calculated as SUVmax × Inflammatory Prognostic Index,NSCLC: Non–Small Cell Lung Cancer There was no statistically significant difference in PFS between the groups. The median PFS was 8 months in the ≤4.4 group, 6 months in the >4.4 group, and 7 months in the overall cohort (log-rank p = 0.098). In the time-dependent analysis, 6-month, 1-year, and 2-year PFS rates were 57.7%, 33.9%, and 19.1% in the ≤4.4 group, compared with 49.8%, 18.6%, and 5.8% in the >4.4 group. For the entire cohort, the corresponding PFS rates were 51.8%, 22.1%, and 6.6%, respectively. Fıgue2. Kaplan–Meier survival curves for progression-free survival (PFS) stratified by LSUVmax–IPI ≤4.4 vs. >4.4. Table7.median PFS duration according to LSUVmax–IPI Groups Median PFS Duration Estimate SE 95% CI Lower Bound Upper Bound LSUVMAX*İPİ ≤4.4 8 1.34 5.38 10.62 >4.4 6 0.79 4.45 7.55 Overall 7 0.72 5.58 8.42 Table8. PFS % according to LSUVmax–IPI Groups PFS % (SE) 6 month 1 year 2 year LSUVMAX*İPİ ≤4.4 57.7% (9.0) 33.9% (8.7) 19.1% (8.3) >4.4 49.8% (5.2) 18.6% (4.4) 5.8% (3.0) Overall 51.8% (4.4) 22.1% (3.8) 6.6% (2.8) ABBREVIATIONS:PFS: Progression-Free Survival,SE: Standard Error,CI: Confidence Interval,LSUVmax–IPI: Composite index calculated as SUVmax × Inflammatory Prognostic Index,NSCLC: Non–Small Cell Lung Cancer Comparison with Other Prognostic Scores When the LSUVMAX–IPI ≤4.4 and >4.4 groups were compared, significant differences were observed in metabolic and inflammatory parameters. PET LSUVmax was markedly higher in the >4.4 group (18.95 ± 24.19) compared with the ≤4.4 group (7.62 ± 4.30) (p 4.4 group (p = 0.008). The total number of treatment lines did not differ between groups (p = 0.493). In contrast, the IPI score was significantly higher in the >4.4 group (p < 0.001). Additional indices—including HALP, ALI, ALBAL, and ALSUL—also demonstrated significant differences between groups (all p < 0.001). However, no significant differences were found for the composite indices LSUVMAX–HALP and LSUVMAX–ALI (p = 0.695 and p = 0.737, respectively). Discussion Our findings demonstrated that higher LSUVmax–IPI scores were significantly associated with shorter overall survival, a result that is consistent with the study by Kolkıran et al., who first introduced the SUVmax–IPI score into the literature. The study by Kolkıran and colleagues was a multicenter investigation conducted in an exclusively nivolumab-treated metastatic NSCLC population that was negative for EGFR, ALK, and ROS1 mutations. Although this design provides valuable insight into immunotherapy-specific biological responses, the restricted diversity of treatment modalities partially limits the generalizability of their prognostic model. In contrast, our study evaluated the LSUVmax–IPI score in a broader and more clinically heterogeneous cohort of metastatic NSCLC patients, independent of treatment modality. By including patients who received chemotherapy, immunotherapy, or targeted therapies, our analysis allowed a more comprehensive assessment of the prognostic value of this composite index under real-world clinical conditions. Methodologically, notable differences also exist between the two studies. Kolkıran et al. reported a wide distribution of SUVmax–IPI scores and identified an optimal ROC-derived cut-off value of 241.9, a range that may pose challenges for clinical implementation. In our study, the score was calculated using the primary tumor SUVmax, resulting in a much lower and more clinically practical cut-off value of 4.4, thereby facilitating clearer risk stratification for clinicians. Moreover, the Kolkıran study assessed the score after the initiation of immunotherapy, allowing factors such as early progression, immunotherapy-related adverse events, and hyperprogression to exert substantial influence on the model. In contrast, our study calculated the LSUVmax–IPI score using pre-treatment PET/CT and inflammatory markers, making the score applicable before treatment initiation. When comparing statistical outcomes, both studies consistently demonstrate that composite metabolic-inflammatory indices hold strong prognostic value for overall survival. In the Kolkıran cohort, patients with SUVmax–IPI >241.9 had a median OS of 15 months, compared with 35 months in the low-score group, and the score remained an independent prognostic factor in multivariate analysis. Similarly, in our study, LSUVmax–IPI >4.4 was associated with significantly shorter median OS (16 vs. 28 months), and this threshold remained an independent predictor of mortality in multivariate modeling. Neither study observed a statistically significant difference in PFS between score groups, likely reflecting the biological heterogeneity of advanced disease and variability in immunotherapy response. Nonetheless, the trend toward shorter PFS in the high-score group was more pronounced in our cohort, suggesting that LSUVmax–IPI may still provide additional prognostic insight into progression dynamics across different treatment settings. Differences in patient characteristics further distinguish the two studies. The Kolkıran cohort had a predominance of male patients, PD-L1 expression played a major role in treatment response, and immunotherapy-related adverse events—particularly thyroiditis and pneumonitis—significantly influenced PFS and OS. In contrast, our study identified BMI, bone metastasis, prior surgical intervention, and multisystem treatment history as independent prognostic variables associated with LSUVmax–IPI. Additionally, patients with LSUVmax–IPI >4.4 exhibited higher metabolic activity, elevated inflammatory indices (HALP, ALI, ALBAL, ALSUL), and higher mortality rates, supporting the ability of the score to capture the multidimensional biology underlying tumor aggressiveness. Taken together, our findings indicate that composite metabolic-inflammatory indices retain strong prognostic value not only in immunotherapy-specific settings but also across broader clinical contexts, thereby expanding the applicability of the previously proposed SUVmax–IPI model. By validating the LSUVmax–IPI score in a wider metastatic NSCLC population and establishing a more practical cut-off value, our study contributes important evidence to the literature and supports the broader clinical utility of this integrated prognostic tool. The metabolic component SUVmax is an important parameter reflecting tumor glucose metabolism. Numerous studies have reported that elevated SUVmax values may be associated with poorer prognosis in NSCLC. In the meta-analysis by Berghmans et al., which included 13 studies and 1,474 patients, most studies identified high SUVmax as a poor prognostic factor, although some failed to show a significant association with survival. In our study as well, SUVmax alone was not significantly associated with OS or PFS, consistent with the inconsistencies reported in the literature. These findings suggest that metabolic activity may have limited prognostic power when assessed in isolation. However, when combined with the Inflammatory Prognostic Index (IPI)—a marker of systemic inflammation—the prognostic significance became evident, and the SUVmax–IPI score demonstrated meaningful clinical utility. Dirican et al. previously reported that IPI, composed of albumin, NLR, and CRP, predicted survival in both early- and advanced-stage NSCLC. However, their cohort included patients with varying disease stages, whereas our study focused solely on metastatic patients, thereby providing a more homogeneous clinical population. Furthermore, our study extended the inflammation-based index proposed by Dirican et al. by integrating tumor metabolic activity (SUVmax) with systemic inflammation (IPI), resulting in the development of a novel metabolic-inflammatory composite score (LSUVmax–IPI). This model more comprehensively captures tumor biology by incorporating both metabolic and immune-inflammatory dimensions. Previous research has also suggested that combining metabolic and inflammatory biomarkers may enhance prognostic accuracy in advanced NSCLC. For example, Castello et al. developed the IMPI score by integrating MTV and SII in 33 immunotherapy-treated patients; Bauckneht et al. demonstrated that an MTV + SII composite index predicted overall survival in 45 patients; and Karaoğlan et al. reported that the MIS score, derived from TLG and LIPI, strongly predicted PFS but had not yet matured for OS analyses. Common limitations of these studies include small sample sizes, exclusive inclusion of immunotherapy-treated patients, lack of control groups, and limited applicability for prognostication at the time of diagnosis. Zhao et al. combined SUVmax with the lymphocyte-to-monocyte ratio (LMR) to develop the SUV_LMR score in stage IIIB-IV NSCLC patients receiving chemotherapy alone, demonstrating strong predictive performance for both treatment response and survival. In contrast, our study evaluated the LSUVmax–IPI score in a more heterogeneous metastatic cohort, independent of treatment modality, and incorporated the IPI—a marker with a broader biological basis—as the inflammatory component. While the Zhao model integrates metabolic activity with an immune cell–based marker (LMR), LSUVmax–IPI incorporates more comprehensive inflammatory indicators—including CRP, NLR, and albumin—thereby providing a more holistic assessment of both the metabolic and immune-inflammatory axes of tumor biology. Although both studies demonstrated that composite metabolic-inflammatory scores have significant prognostic value for overall survival, the LSUVmax–IPI score offers the advantage of being applicable before treatment initiation and maintaining strong performance across diverse therapeutic backgrounds, thereby expanding its clinical utility. In this context, the LSUVmax–IPI (SUVmax × IPI) score proposed in our study offers several advantages over previous composite models. It can be calculated easily at the time of diagnosis, predicts prognosis independently of treatment type and mutation status, and is practical for routine clinical use. The findings of our study support LSUVmax–IPI as a strong and independent prognostic factor for overall survival in metastatic NSCLC. Conclusion Our findings demonstrate that the LSUVmax–IPI score, which simultaneously reflects tumor metabolic aggressiveness and the patient’s inflammatory status, is a valuable prognostic indicator for overall survival in metastatic NSCLC. Furthermore, our data support the growing body of evidence favoring combined metabolic–inflammatory composite scores for risk stratification in advanced NSCLC. Higher LSUVmax–IPI scores are associated with shorter survival and more aggressive tumor behavior. This composite index may aid individualized treatment planning and clinical decision-making. Prospective studies are warranted to validate these results and further clarify the prognostic role of LSUVmax–IPI in metastatic NSCLC. Abbreviations The following abbreviations are used in this manuscript: SUVmax LSUVmax maximum standardized uptake value Lung maximum standardized uptake value IPI inflammatory prognostic index FDG-PET 18 F-fluorodeoxyglucose positron emission tomography CRP C-reactive protein NLR neutrophil-to-lymphocyte ratio ROC ALI receiver operating characteristic advanced lung cancer ınflammation ındex(BMI × Albümin / NLR) HALP ALBAL ALSUL Hemoglobin, Albumin, Lymphocyte, Platelet Score(Hemoglobin × Albumin × Lymphocyte /Platelet) Albumin/Basophil Ratio Albumin/Sulfur Index IQR interquartile range CI confidence interval SE standard error aHR adjusted hazard ratio SPSS Statistical Package for the Social Sciences C-index concordance index DCA decision curve analysis CR complete response PR partial response SD stable disease PD progressive disease HR hazard ratio IPI Immune Prognostic Index dNLR derived neutrophil-to-lymphocyte ratio SII systemic immune-inflammation index PLR platelet-to-lymphocyte ratio Declarations Author Contributions: Conceptualization, E.ZG and F.E.; methodology,,A.P.E., M.S Funding: This research received no external funding. Institutional Review Board Statement: Ethical approval was granted by the Health Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine (Manisa, Turkey) with the reference number Informed Consent Statement: Patient consent was waived due to the retrospective design. Data Availability Statement: Data will be available from the corresponding author upon reasonable request. Conflicts of Interest: The authors declare no conflicts of interest. References Siegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A, Cancer statistics, Cancer JC. 2025 Jan-Feb;75(1):10–45. 10.3322/caac.21871 . Epub 2025 Jan 16. PMID: 39817679; PMCID: PMC11745215. Hyun SH, Ahn HK, Kim H, Ahn MJ, Park K, Ahn YC, Kim J, Shim YM, Choi JY. Volume-based assessment by (18)F-FDG PET/CT predicts survival in patients with stage III non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2014;41(1):50–8. 10.1007/s00259-013-2530-8 . Epub 2013 Aug 16. PMID: 23948859. Liao S, Penney BC, Wroblewski K, Zhang H, Simon CA, Kampalath R, Shih MC, Shimada N, Chen S, Salgia R, Appelbaum DE, Suzuki K, Chen CT, Pu Y. Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2012;39(1):27–38. 10.1007/s00259-011-1934-6 . Epub 2011 Sep 23. PMID: 21946983. Templeton AJ, McNamara MG, Šeruga B, Vera-Badillo FE, Aneja P, Ocaña A, Leibowitz-Amit R, Sonpavde G, Knox JJ, Tran B, Tannock IF, Amir E. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst. 2014;106(6):dju124. 10.1093/jnci/dju124 . PMID: 24875653. Stares M, Brown LR, Abhi D, Phillips I. Prognostic Biomarkers of Systemic Inflammation in Non-Small Cell Lung Cancer: A Narrative Review of Challenges and Opportunities. Cancers. 2024;16:1508. https://doi.org/10.3390/cancers16081508 . Kolkıran N, Erdoğan AP, Şahbazlar M, Taş S, Gököz Doğu G, Canaslan K, Ünek İT, Demirkıran Ö, Demir B, Teküstün GN, et al. SUVmax-IPI as a New Prognostic Index in Metastatic Non-Small Cell Lung Cancer Patients Receiving Nivolumab. Curr Oncol. 2025;32:566. https://doi.org/10.3390/curroncol32100566 . Berghmans T, Dusart M, Paesmans M, Hossein-Foucher C, Buvat I, Castaigne C, Scherpereel A, Mascaux C, Moreau M, Roelandts M, Alard S, Meert AP, Patz EF Jr, Lafitte JJ, Sculier JP, European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol. 2008;3(1):6–12. 10.1097/JTO.0b013e31815e6d6b . PMID: 18166834.). Dirican N, Dirican A, Anar C, Atalay S, Ozturk O, Bircan A, Akkaya A, Cakir M. A New Inflammatory Prognostic Index, Based on C-reactive Protein, the Neutrophil to Lymphocyte Ratio and Serum Albumin is Useful for Predicting Prognosis in Non-Small Cell Lung Cancer Cases. Asian Pac J Cancer Prev. 2016;17(12):5101–6. PMID: 28122441; PMCID: PMC5454643. Castello A, Toschi L, Rossi S, Mazziotti E, Lopci E. The immune-metabolic-prognostic index and clinical outcomes in patients with non-small cell lung carcinoma under checkpoint inhibitors. J Cancer Res Clin Oncol. 2020;146(5):1235–43. 10.1007/s00432-020-03150-9 . Epub 2020 Feb 11. PMID: 32048008; PMCID: PMC11804727. Bauckneht M, Genova C, Rossi G, Rijavec E, Dal Bello MG, Ferrarazzo G, Tagliamento M, Donegani MI, Biello F, Chiola S, Zullo L, Raffa S, Lanfranchi F, Cittadini G, Marini C, Lopci E, Sambuceti G, Grossi F, Morbelli S. The Role of the Immune Metabolic Prognostic Index in Patients with Non-Small Cell Lung Cancer (NSCLC) in Radiological Progression during Treatment with Nivolumab. Cancers (Basel). 2021;13(13):3117. 10.3390/cancers13133117 . PMID: 34206545; PMCID: PMC8268031. Karaoğlan BB, Dursun E, Mesci İ, Araz MS, Köksoy EB. Exploring the metabolic-immune score in advanced NSCLC treated with immunotherapy. Sci Rep. 2025;15(1):30781. 10.1038/s41598-025-16788-7 . PMID: 40841737; PMCID: PMC12371022. Zhao K, Wang C, Shi F, Liu H, Wu S, Ding H, Liang Z. Combined Prognostic Value of the SUVmax Derived from FDG-PET and the Lymphocyte-Monocyte Ratio in Patients with Stage IIIB–IV NSCLC Receiving Chemotherapy. BMC Cancer. 2021;21:66. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":22085,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for overall survival (OS) stratified by LSUVmax–IPI ≤4.4 vs. \u0026gt;4.4\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8254045/v1/adb1a6330c770178ffee5011.png"},{"id":99260498,"identity":"e413e72f-f9d4-46e5-b785-7b718cc3554b","added_by":"auto","created_at":"2025-12-31 01:18:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13763,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for progression-free survival (PFS) stratified by LSUVmax–IPI ≤4.4 vs. \u0026gt;4.4.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8254045/v1/3eaaa0a9845228eb790cc478.png"},{"id":103744783,"identity":"90115ced-562e-442d-bfe6-bac10c978fad","added_by":"auto","created_at":"2026-03-02 11:42:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1023370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8254045/v1/29fe2da7-1971-4da3-b473-e088503c693e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Significance of the LSUVmax–IPI Composite Score on Overall Survival in Metastatic Non–Small Cell Lung Cancer","fulltext":[{"header":"SIMPLE SUMMARY","content":"\u003cp\u003eIn metastatic non\u0026ndash;small cell lung cancer (NSCLC), predicting disease course remains challenging, and survival rates are still limited despite advances in systemic therapies. Biomarkers that integrate tumor metabolic activity and the patient\u0026rsquo;s systemic inflammatory status may improve prognostic accuracy. This study evaluates the LSUVmax\u0026ndash;IPI composite score, calculated by multiplying the primary tumor SUVmax by the Inflammatory Prognostic Index (IPI), in patients with metastatic NSCLC. Higher LSUVmax\u0026ndash;IPI values were strongly associated with poorer survival, suggesting that this score may support risk stratification in clinical practice.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eNon\u0026ndash;small cell lung cancer (NSCLC) accounts for the majority of lung cancer\u0026ndash;related deaths worldwide, and most patients are diagnosed at metastatic stages [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite major therapeutic advances, survival outcomes remain limited, underscoring the need for novel prognostic markers that more accurately reflect tumor biology.\u003c/p\u003e \u003cp\u003e18F-FDG PET/CT is widely used for staging and assessing tumor metabolic activity. Although SUVmax provides information on glycolytic activity, its prognostic value varies across studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Systemic inflammation also plays a key role in cancer progression. Biomarkers such as the neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and albumin reflect host inflammatory status. The Inflammatory Prognostic Index (IPI) integrates these markers and has demonstrated prognostic value in several malignancies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong studies combining metabolic and inflammatory parameters, Kolkıran et al. were the first to propose the SUVmax\u0026ndash;IPI score, showing that this composite index independently predicted overall survival in nivolumab-treated metastatic NSCLC patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of this study is to evaluate the prognostic value of SUVmax\u0026ndash;IPI in a metastatic NSCLC population regardless of treatment modality or metastatic burden.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was approved by the \u003cem\u003eHealth Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine\u003c/em\u003e. We confirm that all procedures were conducted in accordance with relevant national and international guidelines, regulations, and the principles of the Declaration of Helsinki. Due to the retrospective design of the study, the requirement for informed consent was waived by the Health Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine.\u003c/p\u003e \u003cp\u003eThis retrospective, single-center study included 137 metastatic NSCLC patients diagnosed at Manisa Celal Bayar University between January 2015 and June 2024. All patients had histologically or cytologically confirmed NSCLC and underwent pre-treatment 18F-FDG PET/CT imaging. Histological classification was performed according to WHO criteria, and staging was determined using the AJCC 8th edition TNM system based on PET/CT, cranial MRI, and, when necessary, bronchoscopy or surgical pathology findings.\u003c/p\u003e \u003cp\u003eOnly stage IV patients with complete pre-treatment PET/CT, complete blood count, biochemical tests, and inflammatory markers were included. Exclusion criteria comprised the presence of a second primary malignancy, incomplete clinical or laboratory data, or initiation of treatment at an external center. Clinical, laboratory, radiological, and treatment-related variables were extracted from electronic medical records. Molecular profiling was performed using next-generation sequencing, evaluating EGFR, ALK, ROS1, and other clinically relevant mutations. PD-L1 expression was assessed using the tumor proportion score (TPS).\u003c/p\u003e \u003cp\u003eFDG-PET/CT acquisition and analysis followed international standards, with imaging performed after fasting and ensuring adequate glycemic control, using a GE Discovery IQ system. Metabolic lesion segmentation was conducted semi-automatically using a 41% SUVmax isocontour, and SUVmax values were derived from the most metabolically active lesion. The Inflammatory Prognostic Index (IPI) was calculated as:\u003c/p\u003e \u003cp\u003eIPI = (CRP \u0026times; NLR) / albumin,\u003c/p\u003e \u003cp\u003eand the composite SUVmax\u0026ndash;IPI score was calculated as SUVmax \u0026times; IPI. ROC analysis demonstrated significant prognostic performance for this score (AUC\u0026thinsp;=\u0026thinsp;0.679; p\u0026thinsp;=\u0026thinsp;0.004), and the optimal cut-off value was determined as 4.4.\u003c/p\u003e \u003cp\u003eCategorical variables were summarized as frequencies and percentages, whereas continuous variables were reported as medians with interquartile ranges. PFS and OS were analyzed using the Kaplan\u0026ndash;Meier method and compared using the log-rank test. Potential prognostic variables were first screened using univariate Cox regression analyses, and significant variables were included in multivariate Cox models. Results were reported as adjusted hazard ratios (aHRs) with 95% confidence intervals. Statistical analyses were performed using SPSS version 27, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eİPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSUVMAX*İPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eArea under the ROC curve (AUC)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e0,640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e0,679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003e95% Confidence interval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e0,554 to 0,719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e0,594 to 0,756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eSignificance\u0026nbsp;level\u0026nbsp;P\u0026nbsp;(Area=0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e0,0293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e0,0044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eYouden\u0026nbsp;index\u0026nbsp;J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e0,2524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e0,3198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eAssociated\u0026nbsp;criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e\u0026gt;0,79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e\u0026gt;4,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e67,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e81,98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.328%;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.6279%;\"\u003e\n \u003cp\u003e57,69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.0441%;\"\u003e\n \u003cp\u003e50,00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:ROC:\u003c/strong\u003e receiver operating characteristic,\u003cstrong\u003e\u0026nbsp;:\u003c/strong\u003e \u003cstrong\u003eLSUVmax\u0026ndash;IPI:\u003c/strong\u003e Composite index calculated as SUVmax \u0026times; Inflammatory Prognostic Index,IPI: Inflammatory Prognostic Index\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe analysis included 137 patients. When the LSUVmax\u0026ndash;IPI \u0026le;4.4 and \u0026gt;4.4 groups were compared, sex, age, smoking pack-years, BMI, and LVEF showed no significant differences. ECOG performance status, age categories, disease stage at diagnosis, and histopathological subtypes were also similar between the groups (all p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eSmoking status, alcohol consumption, BMI categories, and comorbidities such as hypertension, diabetes, coronary artery disease, chronic kidney disease, and COPD/asthma likewise did not differ significantly.\u003c/p\u003e\n\u003cp\u003eMetastatic sites (liver, lung, bone, brain, distant lymph nodes) and rates of palliative radiotherapy, chemoradiotherapy, and adjuvant chemotherapy were comparable (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eA borderline difference was observed in the number of treatment lines (\u0026ge;4 lines: 71.0% vs. 53.5%; p = 0.099). Progression rates were identical in both groups (78.1%; p = 0.997). In contrast, surgical intervention was significantly more common in the \u0026le;4.4 group (25.0% vs. 5.8%; p = 0.004), and mortality was significantly higher in the \u0026gt;4.4 group (86.7% vs. 62.5%; p = 0.004).\u003c/p\u003e\n\u003cp\u003eTable 2. Clinical and Demographic Characteristics According to LSUVmax\u0026ndash;IPI Groups (\u0026le;4.4 vs \u0026gt;4.4)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"538\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eLSUVMAX*İPİ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026le; 4,4 n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026gt; 4,4 n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (78,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e92 (87,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (21,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13 (12,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eAge at Diagnosis(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e63.16 \u0026plusmn; 8.89\u003c/p\u003e\n \u003cp\u003e65 (47\u0026ndash;80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e62.19 \u0026plusmn; 7.20\u003c/p\u003e\n \u003cp\u003e63 (40\u0026ndash;75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSmoking history (pack-years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e37.44 \u0026plusmn; 33.80\u003c/p\u003e\n \u003cp\u003e30 (0\u0026ndash;190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e34.09 \u0026plusmn; 21.01\u003c/p\u003e\n \u003cp\u003e40 (0\u0026ndash;100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26.03 \u0026plusmn; 4.34\u003c/p\u003e\n \u003cp\u003e26.8 (17.3\u0026ndash;37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26.05 \u0026plusmn; 4.30\u003c/p\u003e\n \u003cp\u003e25.4 (16.2\u0026ndash;38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eEcho EF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e58.75 \u0026plusmn; 2.84\u003c/p\u003e\n \u003cp\u003e60 (50\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e58.74 \u0026plusmn; 5.13\u003c/p\u003e\n \u003cp\u003e60 (22\u0026ndash;65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eECOG performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (3,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5 (4,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e31 (96,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e100 (95,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAge at Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026gt; 65 year\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (53,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e44 (41,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt; 65 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15 (46,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e61 (58,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStage at initial\u003c/p\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eStage 1\u0026ndash;2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (12,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (3,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003estage3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5 (15,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26 (24,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003estagee 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e23 (71,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75 (71,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHistological Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAdenocarcinoma\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20 (62,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e55 (52,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSquamous cell\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e11 (34,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e48 (45,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1 (3,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e2 (1,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003esmoker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (37,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e49 (46,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eEx smoker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e16 (50,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e39 (37,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNon smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (12,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (16,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAlcohol Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eNon-user\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e29 (90,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e102 (97,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eOther (regular / social / former)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (9,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (2,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026lt; 25 kg/m\u0026sup2;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e13 (40,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e47 (44,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge; 25 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e19 (59,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e58 (55,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e7 (21,9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e31 (29,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9 (28,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (16,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4 (12,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15 (14,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3 (9,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6 (5,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eCOPD / Asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5 (15,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e26 (24,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eLiver Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (25,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (16,2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eLung Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e28 (87,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e85 (81,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBone Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e17 (53,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e61 (58,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eBrain Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e12 (37,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (23,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDistant lymph node Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9 (28,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e41 (39,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003ePalliative radiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ereceived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e19 (61,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e56 (53,3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eConcurrent chemoradiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ereceived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e15 (48,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42 (40,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ereceived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (25,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6 (5,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ereceived\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e8 (30,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (29,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003e\u0026ge;4 lines of systemic therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026ge; 4 lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e22 (71,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e54 (53,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eProgression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003epresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25 (78,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e82 (78,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 179px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e20 (62,5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e91 (86,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as number (n) of patients and percentage (%). \u003cstrong\u003eAbbreviations:\u003c/strong\u003e \u003cstrong\u003eLSUVmax\u0026ndash;IPI:\u003c/strong\u003e Composite index calculated as SUVmax \u0026times; Inflammatory Prognostic Index,\u003cstrong\u003eBMI:\u003c/strong\u003e Body Mass Index,\u003cstrong\u003eCAD:\u003c/strong\u003e Coronary Artery Disease,\u003cstrong\u003eCKD:\u003c/strong\u003e Chronic Kidney Disease,\u003cstrong\u003eCOPD:\u003c/strong\u003e Chronic Obstructive Pulmonary Disease, \u003cstrong\u003eECOG:\u003c/strong\u003e Eastern Cooperative Oncology Group,\u003cstrong\u003eEF:\u003c/strong\u003e Ejection Fraction\u003cbr\u003e\u003cstrong\u003eIPI:\u003c/strong\u003e Inflammatory Prognostic Index,\u003cstrong\u003eLN:\u003c/strong\u003e Lymph Node,\u003cstrong\u003eNSCLC:\u003c/strong\u003e Non\u0026ndash;Small Cell Lung Cancer,\u003cstrong\u003eRT:\u003c/strong\u003e Radiotherapy,\u003cstrong\u003eKRT / CCRT:\u003c/strong\u003e Concurrent Chemoradiotherapy,\u003cstrong\u003eSUVmax:\u003c/strong\u003e Maximum Standardized Uptake Value,\u003cstrong\u003eALK:\u003c/strong\u003e Anaplastic Lymphoma Kinase\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eUnivariate and Multivariate Analysis of Progression-Free Survival and Overall Survival\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eCox regression analyses were performed to identify potential prognostic factors associated with PFS and OS.\u003c/p\u003e\n\u003cp\u003eIn the univariate analysis, the LSUVMAX\u0026ndash;IPI score emerged as a significant predictor of progression (p = 0.003). In addition, ECOG performance status (0\u0026ndash;1) was associated with a higher risk of progression (HR: 3.163; 95% CI: 1.567\u0026ndash;6.384; p = 0.001). Non-smokers demonstrated a significantly increased progression risk compared with former smokers (HR: 1.869; 95% CI: 1.064\u0026ndash;3.284; p = 0.030). Regarding disease stage, patients in stages 2\u0026ndash;4 exhibited a lower risk of progression compared with those in stage 0 (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn the multivariate analysis, ECOG 0\u0026ndash;1 remained an independent prognostic factor (HR: 6.277; p = 0.005), and the reduced progression risk in advanced-stage patients was confirmed.\u003c/p\u003e\n\u003cp\u003eBackward stepwise analysis yielded similar results, with ECOG performance status and disease stage persisting as the strongest independent predictors of progression\u003c/p\u003e\n\u003cp\u003eTable3.Cox regression analysis of progression-free survival.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003eHR (95% Cl Min\u0026ndash;Max)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eHR (95% Cl Min\u0026ndash;Max)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.212(0.731-.2.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eAge at\u003c/p\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.993(0.971-1.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eSmoking Status Ex-Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.395(0.928-2.097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nonsmoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.869(1.064-3.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHistological Type\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ref:\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Adenocarcinoma\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eSquamous cell\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.960(0.655-1.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eMixt\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.906(0.531-28.757)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.754(0.427-7.211)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.968(0.926-1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eBMI (Ref:\u0026gt;25 ) \u0026nbsp;\u0026lt;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.170(0.805-1.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eALK (Ref:negative) positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.714(0.825-3.558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePDL (Ref:negative) Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.031(0.390-2.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStage at\u003c/p\u003e\n \u003cp\u003eInitial Diagnosis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eRef: 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0,010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003estage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.052(0.06-0.485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.084(0.008-.0848)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003estage3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.038(0.05-0.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.046(0.005-0.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003estage4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.080(0.010-0.628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e0.097(0.012-0.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eECOG_categorize(Ref: Ecog 0) Ecog 0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e3.163(1.567-6.384)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5.019(1.496-16.837)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eİPİ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.997(0.984-1.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePET LMax SUV Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.003(0.997-1.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePET MET Max SUV Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e1.002(0.988-1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eABBREVIATIONS:HR: Hazard Ratio,CI: Confidence Interval,BMI: Body Mass Index,ALK: Anaplastic Lymphoma Kinase,PD-L1: Programmed Death-Ligand 1,ECOG: Eastern Cooperative Oncology Group,SUVmax: Maximum Standardized Uptake Value,PET: Positron Emission Tomography,IPI: Inflammatory Prognostic Index,NSCLC: Non\u0026ndash;Small Cell Lung Cancer\u003c/p\u003e\n\u003cp\u003eIn the univariate analyses, an increase in the LSUVMAX\u0026ndash;IPI value was significantly associated with a higher risk of mortality (HR: 1.001; 95% CI: 1.000\u0026ndash;1.001; p = 0.003). The IPI score similarly demonstrated a significant association with increased mortality risk (HR: 1.014; p = 0.014). In addition, a higher number of treatment lines was identified as a protective factor against mortality (HR: 0.686; p = 0.014).\u003c/p\u003e\n\u003cp\u003eIn the multivariate analysis, which included variables with p \u0026lt; 0.100 from the univariate model and was performed using the Backward elimination method, an LSUVMAX\u0026ndash;IPI value \u0026gt;4.4 remained independently and significantly associated with mortality (HR: 2.383; 95% CI: 1.437\u0026ndash;3.952; p = 0.001). Moreover, BMI \u0026lt;25 (p = 0.027) and the presence of bone metastasis (p = 0.022) were identified as independent predictors of mortality.\u003c/p\u003e\n\u003cp\u003eTable 4. Cox regression analysis of exitus\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp; HR (95% Cl Min\u0026ndash;Max)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eHR (95% Cl Min\u0026ndash;Max)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGender(ref:\u003c/p\u003e\n \u003cp\u003eFemale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.437(0.856-2.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.654(0.926-2.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge at\u003c/p\u003e\n \u003cp\u003eDiagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.016 (0.994\u0026ndash;1.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSmoking Status\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eEx-Smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.392(0.933-2.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eNonsmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.577(0,926-2,684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHistological Type\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eRef:\u003c/p\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eSquamous cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.225 (0.844-1.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003emikst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;3.885(0.528-28558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eALK (Ref:negative)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1.277(0.627-2.603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePDL 1 (ref: negatif)\u003c/p\u003e\n \u003cp\u003epozitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.983(0.220-4.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStage at\u003c/p\u003e\n \u003cp\u003eInitial Diagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eRef:0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eStage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eStage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eStage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLiver Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.532(0.994-2.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLung Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.167(0.717-1.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBone Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;1.290(0.894-1.861)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e1.623(1.071-2.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBrain Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.922(0.615-1.382)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.977(0.939-1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eDistant Lymph Node Metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.986(0.677-1.436)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;PET MET Max SUV Max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.010(0.996-1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;Total Number of Treatment Lines \u0026nbsp; \u0026nbsp; Received\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e(ref \u0026ge;4 lines)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e˂0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e0.686(0.561-0.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026le;3 lines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.036(0.714-1.501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;LSUVMAX*İPİ ˃4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e2.062(1.267-3.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e2.383(1.437-3.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;İPİ ˃0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.852(1.245-2.755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e HR: Hazard Ratio,CI: Confidence Interval,BMI: Body Mass Index,ALK: Anaplastic Lymphoma Kinase,PD-L1: Programmed Death-Ligand 1,PET: Positron Emission Tomography,SUVmax: Maximum Standardized Uptake Value,IPI: Inflammatory Prognostic Index, LSUVmax\u0026ndash;IPI: Composite score calculated as SUVmax \u0026times; IPI\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSurvival Outcomes\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe median overall survival was 28 months in the LSUVMAX\u0026ndash;IPI \u0026le;4.4 group and 16 months in the \u0026gt;4.4 group (p = 0.002). One-year overall survival rates were 84.3% in the \u0026le;4.4 group compared with 58.4% in the \u0026gt;4.4 group. These findings indicate that an LSUVMAX\u0026ndash;IPI value \u0026le;4.4 is a strong prognostic marker for improved survival outcomes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"65%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 64px;\"\u003e\n \u003cp\u003eOverall Survival % (SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-year\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 -year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSUVMAX*İPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;4.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e84.3% (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e43.6% (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e29.9% (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;4.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e58.4% (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e18,3% (4,1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e6.1% (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.0% (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e65.1% (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e25.7% (4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e11.8% (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3.0% (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable5. Median survival time according to LSUVmax\u0026ndash;IPI Groups \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. Overall Survival % according to LSUVmax\u0026ndash;IPI Groups \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"53%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 52px;\"\u003e\n \u003cp\u003eMedian Survival Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 18px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 7px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eLower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eUpper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSUVMAX*İPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;4.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;4.4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eABBREVIATIONS:OS: Overall Survival,SE: Standard Error,CI: Confidence Interval,LSUVmax\u0026ndash;IPI: Composite index calculated as SUVmax \u0026times; Inflammatory Prognostic Index,NSCLC: Non\u0026ndash;Small Cell Lung Cancer\u003c/p\u003e\n\u003cp\u003eThere was no statistically significant difference in PFS between the groups. The median PFS was 8 months in the \u0026le;4.4 group, 6 months in the \u0026gt;4.4 group, and 7 months in the overall cohort (log-rank p = 0.098).\u003c/p\u003e\n\u003cp\u003eIn the time-dependent analysis, 6-month, 1-year, and 2-year PFS rates were 57.7%, 33.9%, and 19.1% in the \u0026le;4.4 group, compared with 49.8%, 18.6%, and 5.8% in the \u0026gt;4.4 group. For the entire cohort, the corresponding PFS rates were 51.8%, 22.1%, and 6.6%, respectively.\u003c/p\u003e\n\u003cp\u003eFıgue2. Kaplan\u0026ndash;Meier survival curves for progression-free survival (PFS) stratified by LSUVmax\u0026ndash;IPI \u0026le;4.4 vs. \u0026gt;4.4.\u003c/p\u003e\n\u003cp\u003eTable7.median PFS duration according to LSUVmax\u0026ndash;IPI Groups \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"50%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian PFS Duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eLower Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eUpper Bound\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSUVMAX*İPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026le;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e10.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026gt;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable8. PFS % according to LSUVmax\u0026ndash;IPI Groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"52%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePFS % (SE)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6 month\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e2 year\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 27px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSUVMAX*İPİ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026le;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e57.7% (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e33.9% (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e19.1% (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026gt;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e49.8% (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e18.6% (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5.8% (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e51.8% (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e22.1% (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6.6% (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eABBREVIATIONS:PFS: Progression-Free Survival,SE: Standard Error,CI: Confidence Interval,LSUVmax\u0026ndash;IPI: Composite index calculated as SUVmax \u0026times; Inflammatory Prognostic Index,NSCLC: Non\u0026ndash;Small Cell Lung Cancer\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eComparison with Other Prognostic Scores\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eWhen the LSUVMAX\u0026ndash;IPI \u0026le;4.4 and \u0026gt;4.4 groups were compared, significant differences were observed in metabolic and inflammatory parameters. PET LSUVmax was markedly higher in the \u0026gt;4.4 group (18.95 \u0026plusmn; 24.19) compared with the \u0026le;4.4 group (7.62 \u0026plusmn; 4.30) (p \u0026lt; 0.001). Similarly, metastatic SUVmax values were significantly elevated in the \u0026gt;4.4 group (p = 0.008).\u003c/p\u003e\n\u003cp\u003eThe total number of treatment lines did not differ between groups (p = 0.493). In contrast, the IPI score was significantly higher in the \u0026gt;4.4 group (p \u0026lt; 0.001). Additional indices\u0026mdash;including HALP, ALI, ALBAL, and ALSUL\u0026mdash;also demonstrated significant differences between groups (all p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eHowever, no significant differences were found for the composite indices LSUVMAX\u0026ndash;HALP and LSUVMAX\u0026ndash;ALI (p = 0.695 and p = 0.737, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings demonstrated that higher LSUVmax–IPI scores were significantly associated with shorter overall survival, a result that is consistent with the study by Kolkıran et al., who first introduced the SUVmax–IPI score into the literature. The study by Kolkıran and colleagues was a multicenter investigation conducted in an exclusively nivolumab-treated metastatic NSCLC population that was negative for EGFR, ALK, and ROS1 mutations. Although this design provides valuable insight into immunotherapy-specific biological responses, the restricted diversity of treatment modalities partially limits the generalizability of their prognostic model. In contrast, our study evaluated the LSUVmax–IPI score in a broader and more clinically heterogeneous cohort of metastatic NSCLC patients, independent of treatment modality. By including patients who received chemotherapy, immunotherapy, or targeted therapies, our analysis allowed a more comprehensive assessment of the prognostic value of this composite index under real-world clinical conditions.\u003c/p\u003e\n\u003cp\u003eMethodologically, notable differences also exist between the two studies. Kolkıran et al. reported a wide distribution of SUVmax–IPI scores and identified an optimal ROC-derived cut-off value of 241.9, a range that may pose challenges for clinical implementation. In our study, the score was calculated using the primary tumor SUVmax, resulting in a much lower and more clinically practical cut-off value of 4.4, thereby facilitating clearer risk stratification for clinicians. Moreover, the Kolkıran study assessed the score after the initiation of immunotherapy, allowing factors such as early progression, immunotherapy-related adverse events, and hyperprogression to exert substantial influence on the model. In contrast, our study calculated the LSUVmax–IPI score using pre-treatment PET/CT and inflammatory markers, making the score applicable before treatment initiation.\u003c/p\u003e\n\u003cp\u003eWhen comparing statistical outcomes, both studies consistently demonstrate that composite metabolic-inflammatory indices hold strong prognostic value for overall survival. In the Kolkıran cohort, patients with SUVmax–IPI \u0026gt;241.9 had a median OS of 15 months, compared with 35 months in the low-score group, and the score remained an independent prognostic factor in multivariate analysis. Similarly, in our study, LSUVmax–IPI \u0026gt;4.4 was associated with significantly shorter median OS (16 vs. 28 months), and this threshold remained an independent predictor of mortality in multivariate modeling. Neither study observed a statistically significant difference in PFS between score groups, likely reflecting the biological heterogeneity of advanced disease and variability in immunotherapy response. Nonetheless, the trend toward shorter PFS in the high-score group was more pronounced in our cohort, suggesting that LSUVmax–IPI may still provide additional prognostic insight into progression dynamics across different treatment settings.\u003c/p\u003e\n\u003cp\u003eDifferences in patient characteristics further distinguish the two studies. The Kolkıran cohort had a predominance of male patients, PD-L1 expression played a major role in treatment response, and immunotherapy-related adverse events—particularly thyroiditis and pneumonitis—significantly influenced PFS and OS. In contrast, our study identified BMI, bone metastasis, prior surgical intervention, and multisystem treatment history as independent prognostic variables associated with LSUVmax–IPI. Additionally, patients with LSUVmax–IPI \u0026gt;4.4 exhibited higher metabolic activity, elevated inflammatory indices (HALP, ALI, ALBAL, ALSUL), and higher mortality rates, supporting the ability of the score to capture the multidimensional biology underlying tumor aggressiveness.\u003c/p\u003e\n\u003cp\u003eTaken together, our findings indicate that composite metabolic-inflammatory indices retain strong prognostic value not only in immunotherapy-specific settings but also across broader clinical contexts, thereby expanding the applicability of the previously proposed SUVmax–IPI model. By validating the LSUVmax–IPI score in a wider metastatic NSCLC population and establishing a more practical cut-off value, our study contributes important evidence to the literature and supports the broader clinical utility of this integrated prognostic tool.\u003c/p\u003e\n\u003cp\u003eThe metabolic component SUVmax is an important parameter reflecting tumor glucose metabolism. Numerous studies have reported that elevated SUVmax values may be associated with poorer prognosis in NSCLC. In the meta-analysis by Berghmans et al., which included 13 studies and 1,474 patients, most studies identified high SUVmax as a poor prognostic factor, although some failed to show a significant association with survival. In our study as well, SUVmax alone was not significantly associated with OS or PFS, consistent with the inconsistencies reported in the literature. These findings suggest that metabolic activity may have limited prognostic power when assessed in isolation. However, when combined with the Inflammatory Prognostic Index (IPI)—a marker of systemic inflammation—the prognostic significance became evident, and the SUVmax–IPI score demonstrated meaningful clinical utility.\u003c/p\u003e\n\u003cp\u003eDirican et al. previously reported that IPI, composed of albumin, NLR, and CRP, predicted survival in both early- and advanced-stage NSCLC. However, their cohort included patients with varying disease stages, whereas our study focused solely on metastatic patients, thereby providing a more homogeneous clinical population. Furthermore, our study extended the inflammation-based index proposed by Dirican et al. by integrating tumor metabolic activity (SUVmax) with systemic inflammation (IPI), resulting in the development of a novel metabolic-inflammatory composite score (LSUVmax–IPI). This model more comprehensively captures tumor biology by incorporating both metabolic and immune-inflammatory dimensions.\u003c/p\u003e\n\u003cp\u003ePrevious research has also suggested that combining metabolic and inflammatory biomarkers may enhance prognostic accuracy in advanced NSCLC. For example, Castello et al. developed the IMPI score by integrating MTV and SII in 33 immunotherapy-treated patients; Bauckneht et al. demonstrated that an MTV + SII composite index predicted overall survival in 45 patients; and Karaoğlan et al. reported that the MIS score, derived from TLG and LIPI, strongly predicted PFS but had not yet matured for OS analyses. Common limitations of these studies include small sample sizes, exclusive inclusion of immunotherapy-treated patients, lack of control groups, and limited applicability for prognostication at the time of diagnosis.\u003c/p\u003e\n\u003cp\u003eZhao et al. combined SUVmax with the lymphocyte-to-monocyte ratio (LMR) to develop the SUV_LMR score in stage IIIB-IV NSCLC patients receiving chemotherapy alone, demonstrating strong predictive performance for both treatment response and survival. In contrast, our study evaluated the LSUVmax–IPI score in a more heterogeneous metastatic cohort, independent of treatment modality, and incorporated the IPI—a marker with a broader biological basis—as the inflammatory component. While the Zhao model integrates metabolic activity with an immune cell–based marker (LMR), LSUVmax–IPI incorporates more comprehensive inflammatory indicators—including CRP, NLR, and albumin—thereby providing a more holistic assessment of both the metabolic and immune-inflammatory axes of tumor biology. Although both studies demonstrated that composite metabolic-inflammatory scores have significant prognostic value for overall survival, the LSUVmax–IPI score offers the advantage of being applicable before treatment initiation and maintaining strong performance across diverse therapeutic backgrounds, thereby expanding its clinical utility.\u003c/p\u003e\n\u003cp\u003eIn this context, the LSUVmax–IPI (SUVmax × IPI) score proposed in our study offers several advantages over previous composite models. It can be calculated easily at the time of diagnosis, predicts prognosis independently of treatment type and mutation status, and is practical for routine clinical use. The findings of our study support LSUVmax–IPI as a strong and independent prognostic factor for overall survival in metastatic NSCLC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings demonstrate that the LSUVmax\u0026ndash;IPI score, which simultaneously reflects tumor metabolic aggressiveness and the patient\u0026rsquo;s inflammatory status, is a valuable prognostic indicator for overall survival in metastatic NSCLC. Furthermore, our data support the growing body of evidence favoring combined metabolic\u0026ndash;inflammatory composite scores for risk stratification in advanced NSCLC. Higher LSUVmax\u0026ndash;IPI scores are associated with shorter survival and more aggressive tumor behavior. This composite index may aid individualized treatment planning and clinical decision-making. Prospective studies are warranted to validate these results and further clarify the prognostic role of LSUVmax\u0026ndash;IPI in metastatic NSCLC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"390\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSUVmax\u003c/p\u003e\n \u003cp\u003eLSUVmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emaximum standardized uptake value\u003c/p\u003e\n \u003cp\u003eLung maximum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einflammatory prognostic index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFDG-PET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose positron emission tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"394\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003cp\u003eALI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ereceiver operating characteristic\u003c/p\u003e\n \u003cp\u003eadvanced lung cancer ınflammation ındex(BMI \u0026times; Alb\u0026uuml;min / NLR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHALP\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eALBAL \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eALSUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemoglobin, Albumin, Lymphocyte, Platelet Score(Hemoglobin \u0026times; Albumin \u0026times; Lymphocyte /Platelet)\u003c/p\u003e\n \u003cp\u003eAlbumin/Basophil Ratio\u003c/p\u003e\n \u003cp\u003eAlbumin/Sulfur Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003einterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003econfidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eaHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eadjusted hazard ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003econcordance index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecomplete response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003estable disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; PD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;progressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hazard ratio\u003c/p\u003e\n\u003cp\u003eIPI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Immune Prognostic Index\u003c/p\u003e\n\u003cp\u003edNLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;derived neutrophil-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eSII systemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003ePLR platelet-to-lymphocyte ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, E.ZG and F.E.; methodology,,A.P.E., M.S\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eEthical approval was granted by the Health Sciences Ethics Committee of Manisa Celal Bayar University Faculty of Medicine (Manisa, Turkey) with the reference number\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003ePatient consent was waived due to the retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eData will be available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Kratzer TB, Giaquinto AN, Sung H, Jemal A, Cancer statistics, Cancer JC. 2025 Jan-Feb;75(1):10\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21871\u003c/span\u003e\u003cspan address=\"10.3322/caac.21871\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Combined Prognostic Value of the SUVmax Derived from FDG-PET and the Lymphocyte-Monocyte Ratio in Patients with Stage IIIB\u0026ndash;IV NSCLC Receiving Chemotherapy. BMC Cancer. 2021;21:66.\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, SUVmax, inflammatory prognostic index, PET/CT, systemic inflammation, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8254045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8254045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon\u0026ndash;small cell lung cancer (NSCLC) is often diagnosed at metastatic stages, and overall prognosis remains poor. Reliable biomarkers reflecting both tumor metabolism and systemic inflammation are needed. This study investigates the prognostic significance of the LSUVmax\u0026ndash;IPI composite index in metastatic NSCLC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 137 metastatic NSCLC patients who underwent pre-treatment 18F-FDG PET/CT were retrospectively analyzed. LSUVmax\u0026ndash;IPI was calculated as SUVmax \u0026times; IPI, and patients were categorized using a cut-off value of 4.4. Clinical, laboratory, metabolic, and survival variables were compared. Overall survival (OS) and progression-free survival (PFS) were analyzed using the Kaplan\u0026ndash;Meier method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with LSUVmax\u0026ndash;IPI\u0026thinsp;\u0026gt;\u0026thinsp;4.4 had significantly higher metabolic activity and inflammatory markers. Median OS was 28 months in the \u0026le;\u0026thinsp;4.4 group and 16 months in the \u0026gt;\u0026thinsp;4.4 group (p\u0026thinsp;=\u0026thinsp;0.002). PFS showed a trend toward shorter duration in the \u0026gt;\u0026thinsp;4.4 group, though not statistically significant (p\u0026thinsp;=\u0026thinsp;0.098).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLSUVmax\u0026ndash;IPI is a practical prognostic index reflecting both tumor aggressiveness and systemic inflammation. Higher scores were strongly associated with poorer OS. Further validation in prospective studies is required.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of the LSUVmax–IPI Composite Score on Overall Survival in Metastatic Non–Small Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 01:18:30","doi":"10.21203/rs.3.rs-8254045/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":"5063fdbd-31dd-4ec2-9d94-4403340352ab","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T11:41:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 01:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8254045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8254045","identity":"rs-8254045","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}