Unmasking the Survival Disparity Between Large-Cell Neuroendocrine Carcinoma and Small-Cell Lung Cancer: A SEER Analysis

Unmasking the Survival Disparity Between Large-Cell Neuroendocrine Carcinoma and Small-Cell Lung Cancer: A SEER Analysis | 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 Unmasking the Survival Disparity Between Large-Cell Neuroendocrine Carcinoma and Small-Cell Lung Cancer: A SEER Analysis Ali Hemade, Pascale Salameh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6514503/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 : Large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung cancer (SCLC) are both high-grade neuroendocrine carcinomas of the lung. While SCLC has well-established treatment protocols, LCNEC remains poorly defined in clinical guidelines, leading to variability in management. Prior studies comparing their survival outcomes have yielded conflicting results, often limited by inadequate adjustment for confounders and lack of time-dependent modeling. Methods : Using the SEER database (2000–2021), we conducted a retrospective cohort study of 26,930 patients with histologically confirmed SCLC or LCNEC. Patients with stage IV disease or incomplete clinical data were excluded. Propensity score matching (PSM) was performed 1:1 based on age, sex, race, tumor stage (T/N), treatment modalities (chemotherapy, surgery, radiotherapy), and year of diagnosis. Survival was analyzed using Kaplan-Meier curves, Cox proportional hazards models, and time-dependent Cox regression incorporating histology*time interaction. Results : In the unmatched cohort, LCNEC was associated with significantly worse overall survival (OS) compared to SCLC (HR = 1.31; 95% CI, 1.23–1.39; p < 0.0001). After PSM (n = 1898 per group), survival curves remained separated in Kaplan-Meier analysis ( p < 0.0001). However, in the adjusted Cox model, LCNEC became associated with better OS (HR = 0.82; 95% CI, 0.73–0.93; p = 0.0024). Time-dependent Cox analysis revealed a significant cancer type x time interaction (HRinteraction = 0.74; p < 0.0001), indicating that the survival gap narrowed over time. Compared to patients who did not receive chemotherapy, chemotherapy was associated with improved OS (HR = 0.70); compared to no surgery, surgery was associated with improved OS (HR = 0.36); and compared to no radiotherapy, radiotherapy was associated with improved OS (HR = 0.62). Conclusions : The observed survival disadvantage of LCNEC in unadjusted analysis was largely driven by differences in stage and treatment. After rigorous adjustment and matching, LCNEC exhibited survival outcomes comparable to SCLC. These findings support managing LCNEC with SCLC-based treatment protocols and suggest that treatment disparities—not intrinsic tumor biology—are the primary drivers of prognosis. Oncology Large-cell neuroendocrine carcinoma small-cell lung cancer SEER propensity score matching time-dependent Cox regression survival analysis Figures Figure 1 Figure 2 Introduction Small-cell lung cancer (SCLC) and large-cell neuroendocrine carcinoma (LCNEC) represent two distinct yet overlapping high-grade neuroendocrine lung carcinomas (HGNECs). While SCLC is well-characterized with established treatment guidelines, LCNEC remains a rare and challenging entity with no universally accepted management approach [1]. The World Health Organization (WHO) reclassified LCNEC in 2015, distinguishing it from large-cell carcinoma and aligning it more closely with SCLC due to its aggressive behavior [2]. Despite histologic similarities, SCLC and LCNEC exhibit different genomic profiles, with LCNEC often harboring mutations akin to non-small-cell lung cancer (NSCLC) [3]. Unlike SCLC, which is treated with platinum-etoposide chemotherapy, LCNEC lacks a standardized therapeutic approach, leading to considerable variability in clinical practice [4]. Some studies suggest that LCNEC should be treated similarly to SCLC, whereas others indicate that an NSCLC-based approach might be more effective [5]. The lack of randomized trials has resulted in conflicting survival data, with some reports indicating better outcomes for LCNEC, while others suggest a poorer prognosis compared to SCLC [6]. Recent studies utilizing the Surveillance, Epidemiology, and End Results (SEER) database and the National Cancer Database (NCDB) have attempted to clarify the prognosis of LCNEC compared to SCLC [7]. However, many of these analyses lack adjustments for confounding variables such as treatment modalities, disease stage, and demographic factors, leading to potential biases in survival estimation [8]. Additionally, prior studies often focus on limited timeframes or do not incorporate time-dependent survival trends, leaving an important gap in understanding how prognosis evolves over time [5]. Given the inconsistencies in prognosis and treatment for LCNEC and SCLC, a well-controlled, propensity score-matched (PSM) analysis is needed to eliminate confounders and provide a more accurate survival comparison [9]. This study leverages the SEER database (2000–2025) to conduct the largest propensity-matched survival analysis to date, controlling for key prognostic factors such as age, sex, tumor stage, treatment modality, and lymph node ratio (LNR) [10]. Furthermore, our study incorporates a time-dependent Cox model to examine how the relative prognosis of LCNEC and SCLC shifts over time, an approach that has not been widely applied in previous research. This study aims to compare overall survival (OS) and cancer-specific survival (CSS) between LCNEC and SCLC using propensity score matching, determine whether survival disparities persist after adjusting for confounders, evaluate the impact of treatment modalities such as surgery, chemotherapy, and radiotherapy on prognosis, and analyze time-dependent survival trends to assess whether LCNEC mortality risk changes over time. We hypothesize that LCNEC appears to have worse survival than SCLC in unmatched analysis, but after PSM adjustment, survival differences will diminish, indicating that treatment disparities rather than intrinsic tumor biology drive prognosis. Methods Study Design This retrospective cohort study utilized a well-established oncology database, the Surveillance, Epidemiology, and End Results (SEER) database, to compare survival outcomes between Small-Cell Lung Cancer (SCLC) and Large-Cell Neuroendocrine Carcinoma (LCNEC) diagnosed between January 1st 2004 and December 31st 2021 (SEER Nov. 2023 submission). The primary objective was to compare the OS of LCNEC and SCLC before and after adjustment for confounding variables. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [11] guidelines to ensure methodological rigor and reproducibility. Study Patient Cohort Data were extracted from the SEER database, including information on patient demographics, tumor characteristics, treatment modalities, and survival outcomes. The dataset included 26,930 patients diagnosed with either SCLC or LCNEC. Eligibility criteria required a histologically confirmed diagnosis of SCLC (ICD-O-3: 8041/3) or LCNEC (ICD-O-3: 8013/3), available treatment data including chemotherapy, surgery, and radiotherapy, and documented tumor staging based on the T and N classification system. Patients were excluded if they had stage IV disease, mixed histology tumors, or incomplete clinical data regarding tumor stage, nodal involvement, or treatment history. Outcomes The primary outcomes were Overall Survival (OS), defined as the time from diagnosis to all-cause mortality, and Cancer-Specific Survival (CSS), defined as the time from diagnosis to lung cancer-related mortality. Statistical Analysis To mitigate baseline differences between SCLC and LCNEC patients, 1:1 nearest-neighbor propensity score matching (PSM) without replacement was performed. The propensity score was estimated using a logistic regression model, incorporating age, sex, race, tumor stage (T and N classification), chemotherapy, surgery, radiotherapy, and year of diagnosis as covariates. Matching quality was assessed using standardized mean differences (SMDs), with an SMD of less than 0.1 indicating adequate balance between groups. Following PSM, a total of 2,359 LCNEC patients were successfully matched to 2,359 SCLC patients. Baseline characteristics were summarized using means and standard deviations for continuous variables and frequencies with percentages for categorical variables. Differences between groups were compared using t-tests or Wilcoxon rank-sum tests for continuous variables and chi-square tests for categorical variables. Survival analysis was conducted using Kaplan-Meier methods, with log-rank tests employed to compare survival distributions between groups. Cox proportional hazard regression models were constructed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for OS and CSS. Univariable Cox models were first applied to each predictor, followed by multivariable Cox models adjusting for all clinically relevant covariates. We assessed the proportionalhazards assumption for each covariate using Schoenfeld residuals. The test revealed significant violations for histology (Cancer_binary; χ²=27.8, p < 0.0001) and several treatment and staging variables (all p < 0.001), indicating nonproportional effects over time. Consequently, we fitted an extended Cox model incorporating a timeinteraction term for histology to determine whether the relative hazard of LCNEC versus SCLC varied throughout the follow-up period. Results Descriptive statistics Table 1 a: Descriptive Statistics Before Propensity Score Matching Before PSM Variable LCNEC (n = 2,359) SCLC (n = 24,571) p-value Standardized Mean Difference (SMD) Age (Mean ± SD) 67.13 ± 9.89 67.88 ± 9.95 < 0.001 0.076 Year of Diagnosis (YOD) 2012.49 ± 5.16 2011.73 ± 5.29 < 0.001 0.146 Sex (%) < 0.001 0.166 Female 46.4% (1,095) 54.7% (13,442) Male 53.6% (1,264) 45.3% (11,129) Race (%) < 0.001 0.113 American Indian/Alaska Native 0.4% (9) 0.7% (174) Asian or Pacific Islander 3.8% (90) 3.6% (880) Black 12.0% (283) 8.9% (2,195) Unknown 0.0% (1) 0.1% (27) White 83.8% (1,976) 86.7% (21,295) Chemotherapy (%) < 0.001 0.622 No/Unknown 52.0% (1,226) 23.2% (5,703) Yes 48.0% (1,133) 76.8% (18,868) Surgery (%) < 0.001 1.556 No 34.2% (806) 93.2% (22,908) Yes 65.8% (1,553) 6.8% (1,663) Radiotherapy (%) < 0.001 0.659 No 69.3% (1,635) 38.1% (9,362) Yes 30.7% (724) 61.9% (15,209) Tumor Stage (T) (%) < 0.001 0.595 T0 0.6% (13) 1.0% (255) T1 11.7% (275) 6.9% (1,707) T2 12.9% (305) 11.6% (2,845) T3 12.6% (298) 12.8% (3,136) T4 14.6% (344) 36.7% (9,013) Lymph Node Stage (N) (%) < 0.001 0.858 N0 60.4% (1,426) 23.8% (5,843) N1 11.2% (264) 10.2% (2,495) N2 22.8% (538) 51.8% (12,736) N3 5.6% (131) 14.2% (3,497) Table 1 b: Descriptive Statistics After Propensity Score Matching Characteristic LCNEC (n = 1898) SCLC (n = 1898) Standardized Mean Difference (SMD) Age, mean (SD) 67.4 (10.0) 68.6 (9.8) 0.116 Sex, Male (%) 962 (50.7%) 847 (44.6%) 0.122 Race (%) 0.218 White 1625 (85.6%) 1644 (86.6%) Black 200 (10.5%) 139 (7.3%) Asian/Pacific Islander 65 (3.4%) 62 (3.3%) American Indian/Alaska Native 7 (0.4%) 44 (2.3%) Unknown 1 (0.1%) 9 (0.5%) Chemotherapy, Yes (%) 1107 (58.3%) 1240 (65.3%) 0.145 Surgery, Yes (%) 1092 (57.5%) 1089 (57.4%) 0.003 Radiotherapy, Yes (%) 707 (37.2%) 810 (42.7%) 0.111 T Stage (%) 0.417 T1 340 (17.9%) 258 (13.6%) T2 679 (35.8%) 679 (35.8%) T3 322 (17.0%) 322 (17.0%) T4 416 (21.9%) 472 (24.9%) N Stage (%) 0.122 N0 1002 (52.8%) 899 (47.4%) N1 242 (12.8%) 245 (12.9%) N2 526 (27.7%) 625 (32.9%) N3 128 (6.7%) 129 (6.8%) Year of Diagnosis, mean (SD) 2012.4 (5.2) 2012.3 (5.3) 0.022 Unmatched Analysis Prior to matching (Table 1 a), significant baseline differences were observed between the SCLC and LCNEC groups. LCNEC patients were more likely to undergo surgery (65.8% vs. 6.8%, p < 0.001), whereas SCLC patients were more frequently treated with chemotherapy (76.8% vs. 48.0%, p < 0.001) and radiotherapy (61.9% vs. 30.7%, p < 0.001). Tumor stage distributions also varied, with a higher prevalence of advanced nodal involvement (N2: 51.8% vs. 22.8%, p < 0.001) among SCLC patients. The mean age of LCNEC patients was slightly lower than that of SCLC patients (67.13 vs. 67.88, p < 0.001). Kaplan-Meier analysis (Fig. 1 ) demonstrated that SCLC patients had significantly better OS compared to LCNEC patients in the unmatched dataset (p < 0.0001). The survival probability of LCNEC was notably lower, particularly in the early follow-up period. Univariable Cox regression (Table 2 ) confirmed this trend, with LCNEC associated with significantly worse survival compared to SCLC (HR = 1.817, 95% CI: 1.727–1.911, p < 0.0001). Multivariable Cox regression (Table 3 ), adjusting for age, sex, race, chemotherapy, surgery, radiotherapy, tumor stage, nodal involvement, and year of diagnosis, reinforced this survival disparity. LCNEC remained associated with significantly worse survival compared to SCLC (HR = 1.306, 95% CI: 1.229–1.388, p < 0.0001). Treatment modalities played a crucial role in survival, with chemotherapy (HR = 0.742, p < 0.0001), surgery (HR = 0.618, p < 0.0001), and radiotherapy (HR = 0.666, p < 0.0001) associated with improved survival outcomes. Table 2 Univariable Cox Regression Results (Matched vs. Unmatched Data) Variable Matched HR (95% CI) Matched p-value Unmatched HR (95% CI) Unmatched p-value SCLC vs. LCNEC 1.98 (1.86–2.11) p < 0.0001 1.82 (1.73–1.91) p < 0.0001 Age 1.04 (1.03–1.04) p < 0.0001 1.03 (1.03–1.03) p < 0.0001 Sex (Male vs. Female) 0.81 (0.76–0.87) p < 0.0001 1.14 (1.11–1.17) p < 0.0001 Race Asian/Pacific Islander 0.82 (0.61–1.10) 0.180 1.04 (0.88–1.25) 0.6272 Black 0.59 (0.46–0.77) p < 0.0001 0.98 (0.83–1.16) 0.8015 Unknown 0.62 (0.27–1.42) 0.261 0.58 (0.31–1.07) 0.0801 White 0.86 (0.69–1.08) 0.195 1.03 (0.88–1.22) 0.7038 Chemotherapy (Yes vs. No) 1.59 (1.47–1.71) p < 0.0001 0.58 (0.56–0.60) p < 0.0001 Surgery (Yes vs. No) 0.37 (0.34–0.40) p < 0.0001 0.43 (0.41–0.45) p < 0.0001 Radiotherapy (Yes vs. No) 1.30 (1.22–1.39) p < 0.0001 0.60 (0.58–0.61) p < 0.0001 Tumor Stages (T) T1 1.01 (0.54–1.90) 0.9646 1.03 (0.89–1.19) 0.7253 T2 0.95 (0.50–1.77) 0.8608 1.30 (1.13–1.50) 0.0003 T3 1.12 (0.60–2.12) 0.7157 1.39 (1.21–1.60) p < 0.0001 T4 1.88 (1.01–3.50) 0.0466 1.71 (1.49–1.96) p < 0.0001 Lymph Node Stages (N) N1 1.64 (1.45–1.84) p < 0.0001 1.15 (1.10–1.21) p < 0.0001 N2 2.47 (2.29–2.68) p < 0.0001 1.57 (1.52–1.62) p < 0.0001 N3 2.93 (2.57–3.33) p < 0.0001 1.76 (1.69–1.84) p < 0.0001 Year of Diagnosis 0.96 (0.95–0.97) p < 0.0001 0.98 (0.97–0.98) p < 0.0001 Table 3 Multivariable Cox Regression Results (Matched vs. Unmatched Data) Variable Matched HR (95% CI) Matched p-value Unmatched HR (95% CI) Unmatched p-value SCLC vs. LCNEC 0.82 (0.73–0.93) 0.0024 1.31 (1.23–1.39) p < 0.0001 Age 1.03 (1.03–1.03) p < 0.0001 1.02 (1.02–1.03) p < 0.0001 Sex (Male vs. Female) 1.19 (1.09–1.29) p < 0.0001 1.17 (1.14–1.20) p < 0.0001 Race (Reference: White) Asian or Pacific Islander 0.86 (0.64–1.15) 0.303 0.90 (0.76–1.08) 0.261 Black 0.92 (0.70–1.21) 0.556 0.96 (0.81–1.13) 0.607 Unknown 0.62 (0.27–1.42) 0.258 0.50 (0.27–0.92) 0.0258 Chemotherapy (Yes vs. No) 0.70 (0.62–0.79) p < 0.0001 0.55 (0.53–0.57) p < 0.0001 Surgery (Yes vs. No) 0.36 (0.31–0.41) p < 0.0001 0.39 (0.37–0.41) p < 0.0001 Radiotherapy (Yes vs. No) 0.62 (0.57–0.67) p < 0.0001 0.53 (0.51–0.55) p < 0.0001 Tumor Stages (Reference: T1) T2 1.98 (1.05–3.74) 0.0353 1.39 (1.20–1.60) p < 0.0001 T3 2.89 (1.53–5.48) 0.0011 1.66 (1.44–1.91) p < 0.0001 T4 2.88 (1.53–5.42) 0.0010 1.80 (1.57–2.07) p < 0.0001 Lymph Node Stages (Reference: N0) N1 1.53 (1.33–1.76) p < 0.0001 1.27 (1.20–1.33) p < 0.0001 N2 1.81 (1.59–2.06) p < 0.0001 1.49 (1.44–1.54) p < 0.0001 N3 1.92 (1.63–2.28) p < 0.0001 1.72 (1.64–1.80) p < 0.0001 Year of Diagnosis 0.97 (0.96–0.98) p < 0.0001 0.98 (0.98–0.99) p < 0.0001 Matched Analysis After PSM (Table 1 b), the balance of covariates improved, though some residual differences remained. Kaplan-Meier analysis (Fig. 2 ) in the matched cohort showed that SCLC retained a significant survival advantage over LCNEC (p < 0.0001), with survival curves remaining separated throughout the follow-up period. Univariable Cox regression (Table 2 ) in the matched cohort confirmed that LCNEC was associated with significantly worse survival compared to SCLC (HR = 1.982, 95% CI: 1.478–2.657, p < 0.0001). Impact of Matching on Prognosis Multivariable Cox regression (Table 3 ) adjusting for demographic, clinical, and treatment-related factors revealed a critical shift in survival outcomes after matching. LCNEC was no longer a significant predictor of worse survival after matching (HR = 0.825, 95% CI: 0.728–0.934, p = 0.0024). In the conventional (timefixed) Cox model, LCNEC appeared to carry a reduced hazard relative to SCLC (HR = 0.82; 95% CI 0.73–0.93). However, the proportionalhazards assumption (Table 4 ) was violated (Schoenfeld p < 0.0001), rendering that single estimate unreliable. The timedependent Cox model (Table 5 ) therefore provides the valid inference: the main effect of histology at one month was HR₁₋ₘₒₙₜₕ=1.98 (95% CI 1.48–2.66), and the timeinteraction coefficient (Cancer_binary×log(time)) was 0.74 (95% CI 0.67–0.82; p < 0.0001). Table 4 Schoenfeld Residuals Test for Proportional Hazards Assumption in the PropensityScore–Adjusted Cox Model Variable χ² df P -value Subtype (LCNEC vs. SCLC) 27.76 1 < 0.0001 * Propensity score 2.60 1 0.1070 Age 57.67 1 < 0.0001 * Sex 0.68 1 0.4098 Race 15.27 4 0.0042 * Chemotherapy 1268.66 1 < 0.0001 * Surgery 56.28 1 < 0.0001 * Radiotherapy 1145.97 1 < 0.0001 * Year of Diagnosis (YOD) 3.45 1 0.0632 T 336.41 13 < 0.0001 * N 25.99 3 < 0.0001 * GLOBAL 2418.71 28 < 0.0001 * P < 0.05 indicates violation of the proportional hazards assumption for that variable; the GLOBAL test assesses overall model validity. Table 5 Time-Dependent Cox Model Variable HR (95% CI) p-value Cancer (SCLC vs. LCNEC) 1.98 (1.48–2.66) p < 0.0001 Cancer Time Interaction 0.74 (0.67–0.82) p < 0.0001 Age 1.00 (0.99–1.01) 0.9789 Age Time Interaction 1.00 (0.99–1.01) 0.2245 Race 1.09 (0.98–1.22) 0.1298 Race Time Interaction 0.97 (0.94–1.01) 0.1568 Chemotherapy (Yes vs. No) 0.57 (0.44–0.75) p < 0.0001 Chemotherapy Time Interaction 1.17 (1.07–1.28) p = 0.0008 Surgery (Yes vs. No) 0.75 (0.56–1.01) p = 0.0598 Surgery Time Interaction 1.01 (0.91–1.13) 0.8314 Radiotherapy (Yes vs. No) 0.69 (0.56–0.85) p = 0.0004 Radiotherapy Time Interaction 1.17 (1.08–1.26) p = 0.0001 Tumor Stage (T2 vs. T1) 1.55 (0.75–3.19) 0.2344 Tumor Stage (T3 vs. T1) 2.32 (1.09–5.01) p = 0.0293 Tumor Stage (T4 vs. T1) 2.08 (0.98–4.44) p = 0.0575 Tumor Stage Time Interaction 0.99 (0.98–1.00) p = 0.0129 Lymph Node Stage (N1 vs. N0) 1.34 (1.12–1.60) p = 0.0013 Lymph Node Stage (N2 vs. N0) 1.70 (1.31–2.20) p < 0.0001 Lymph Node Stage (N3 vs. N0) 1.95 (1.35–2.81) p = 0.0004 Lymph Node Stage Time Interaction 0.93 (0.89–0.97) p = 0.0013 Year of Diagnosis (YOD) 1.09 (1.07–1.10) p < 0.0001 YOD Time Interaction 0.92 (0.92–0.93) p < 0.0001 Discussion In this large population-based analysis spanning 2000–2025, we directly compared survival outcomes between LCNEC and SCLC, employing robust methods to control for confounders. Our findings offer important insights into the relative prognoses of these two high-grade neuroendocrine lung cancers. First, in the unmatched (unadjusted) cohort, LCNEC was associated with significantly worse survival than SCLC. By both overall survival and cancer-specific survival endpoints, patients diagnosed with LCNEC had shorter median survival and lower survival probabilities over time compared to those with SCLC. This initial result aligns with the notion that LCNEC is an extremely aggressive malignancy – in fact, previous reports have noted very poor outcomes for LCNEC in the absence of effective therapy. For example, Derks et al. observed a median OS of only around 4 months for stage IV LCNEC (versus ~ 7 months for stage IV SCLC) in a nationwide registry, underscoring the rapid lethality of LCNEC when not controlled​ [5]. Our finding of inferior unadjusted survival for LCNEC is consistent with such data and with the clinical impression that LCNEC, like SCLC, often runs an explosive clinical course. It is worth noting, however, that unadjusted comparisons can be misleading if the two groups differ systematically in factors like stage at diagnosis or treatment received. Therefore, the crude survival disadvantage observed for LCNEC required further scrutiny with adjusted analyses. After performing propensity score matching to balance key covariates between the LCNEC and SCLC groups, we re-examined the survival difference. Notably, even in the PSM-matched cohort, the Kaplan-Meier survival curves for OS and CSS remained separated, with LCNEC patients continuing to experience worse outcomes than matched SCLC patients. This indicates that certain disparities persisted despite matching on factors such as age, sex, race, stage, nodal status, treatment modalities, and diagnosis year. However, when we applied multivariable Cox regression to the matched cohort – thus adjusting for any residual differences and allowing a refined assessment of histology’s impact – the picture changed. In the adjusted Cox model (on the matched sample), LCNEC was no longer associated with significantly worse OS. In fact, the point estimate of the hazard ratio for LCNEC (relative to SCLC) fell below 1.0, suggesting that conditional on equivalent baseline characteristics , LCNEC patients did not have higher mortality risk than SCLC patients; their risk was slightly lower. In other words, once we controlled for stage, treatment, and other covariates, LCNEC did not independently predict worse overall survival. This is a critical finding that challenges the raw comparison. It implies that the apparent survival gap seen in unmatched analysis was largely attributable to imbalances in prognostic factors and treatment, rather than an intrinsic difference in tumor behavior. Our result here is in agreement with prior studies that performed multivariate adjustments or matching. For example, Derks et al. reported that multivariate-adjusted OS in LCNEC was equivalent to that in SCLC, after accounting for stage and therapy​ [12]. Likewise, Yang et al. analyzed stage IV cases with PSM and found no significant OS or CSS difference between LCNEC and SCLC in the matched groups​ [5]. Our study confirms on a larger scale that, after rigorous adjustment, LCNEC per se does not portend a worse survival than SCLC. The persistence of a gap in the unadjusted and matched-KM analysis, followed by its disappearance in the adjusted Cox, suggests that subtle confounders (likely related to treatment and stage) influenced the crude outcomes. One novel aspect of our analysis was the use of a time-dependent Cox model to investigate how the relative hazard of death for LCNEC versus SCLC changed over the follow-up period. Interestingly, this analysis revealed a significant interaction between cancer type and time, indicating that the hazard ratio for LCNEC (vs SCLC) was not constant but decreased over time. In practical terms, we found that the survival disadvantage of LCNEC was most pronounced early after diagnosis and then narrowed as time went on. During the initial months and first couple of years, LCNEC patients were at higher risk of death than SCLC patients (reflected in the worse short-term OS/CSS). But among those who survived longer-term, the difference in outcomes became much smaller, and later in the follow-up the hazard for LCNEC patients approached or even dipped below that of SCLC patients. This dynamic explains why our Kaplan-Meier curves showed separation early but began to converge later, and it reinforces the idea that the early period following diagnosis is critical for LCNEC outcomes. A plausible interpretation is that any excess mortality with LCNEC occurs primarily in the short term – perhaps due to suboptimal initial management – whereas SCLC carries a sustained risk that catches up over time (with its well-known pattern of early relapse even after initial response). To our knowledge, this is the first study to demonstrate a time-varying effect in the LCNEC vs SCLC survival comparison. This finding suggests that when evaluating prognosis, one should consider conditional survival : if an LCNEC patient can overcome the initial high-risk phase (possibly with effective therapy), their longer-term survival outlook may become comparable to that of SCLC survivors. Our results have several important implications when contextualized with prior literature, and they help reconcile some of the discrepancies among earlier studies. First, the unadjusted analyses in our study (and others) might have been confounded by differences in clinical management between LCNEC and SCLC. Historically, SCLC has an established treatment paradigm of early, intensive therapy – virtually all SCLC patients with limited-stage disease receive combined chemo-radiotherapy, and those with extensive disease receive prompt platinum-doublet chemotherapy, often with prophylactic cranial irradiation in responders [5]. In contrast, the optimal treatment for LCNEC has been less clear, and prior to its recognition as a high-grade neuroendocrine carcinoma, many LCNEC cases were managed similarly to other NSCLCs​ [13]. In the early 2000s, for example, a patient with LCNEC might have undergone surgery but then not received adjuvant chemotherapy, because clinicians considered it a type of large-cell carcinoma. Varlotto et al. noted that the clinical profiles of early-stage LCNEC patients (those undergoing resection) were more akin to other large-cell carcinomas than to SCLC, and their study argued LCNEC should be treated like NSCLC rather than SCLC​ [13]. Indeed, in that analysis, resected LCNEC patients had survival outcomes similar to non-neuroendocrine NSCLC and better than surgically managed SCLC, once again suggesting that when treated with surgery (with or without adjuvant therapy), LCNEC can do well​ [13]. However, the absence of chemotherapy in many resected-LCNEC cases likely contributed to higher recurrence rates. Several reports have documented that surgery alone is often inadequate for LCNEC, with very high relapse rates and poor long-term survival if adjuvant therapy is omitted​ [14]. Our findings support this: the early steep drop in the LCNEC survival curve is consistent with many LCNEC patients succumbing quickly, likely those who did not receive effective systemic therapy upfront. Crucially, when LCNEC is managed aggressively in the same way as SCLC , outcomes appear to improve markedly. A number of retrospective studies and a recent meta-analysis have directly compared SCLC-type chemotherapy (platinum-etoposide, the standard for SCLC) versus NSCLC-type regimens (platinum with gemcitabine, taxane, or pemetrexed) in LCNEC patients​ [15]. The consensus emerging from these studies is that SCLC-type chemotherapy yields superior results in LCNEC. For instance, a meta-analysis including ~ 446 LCNEC patients reported that first-line platinum-etoposide was associated with significantly longer OS (pooled HR ~ 0.73) and PFS (HR ~ 0.68) compared to NSCLC chemo regimens​ [15]. This finding held true not only in metastatic disease but also in the adjuvant setting: LCNEC patients who received post-operative SCLC-style chemo had better survival than those who received NSCLC adjuvant chemo​ [15]. Consistent with this, multiple small series have shown higher response rates in advanced LCNEC when treated with SCLC regimens, whereas NSCLC regimens often yield suboptimal results​ [2]. Our study did not directly test chemotherapy regimens, but the fact that LCNEC’s survival disadvantage disappeared after matching and adjustment strongly implies that treatment differences were a major driver of the initial disparity. It is likely that in the unmatched population, a substantial subset of LCNEC patients did not receive chemotherapy or received less effective NSCLC-type treatments, contributing to worse outcomes, whereas most SCLC patients (especially in modern years) received appropriate chemo-radiation per guidelines. After matching on treatment variables (and indirectly by adjusting for diagnosis year, which captures changes in practice), we essentially compared LCNEC and SCLC patients with similar therapy – and in that scenario, their survival was equivalent. This interpretation is further bolstered by the time-interaction finding: the narrowing survival gap over time suggests that once the treatment effect in the early period is accounted for (i.e. those who survived the early phase likely had effective initial therapy), LCNEC patients do as well as SCLC patients subsequently. In sum, our results indicate that the apparent prognostic disadvantage of LCNEC is not due to intrinsically more aggressive biology, but rather due to historically inadequate or delayed treatment in some LCNEC cases. This echoes observations from prior population-based studies: for example, one SEER analysis noted that LCNEC patients were much less likely to receive adjuvant chemotherapy than SCLC patients, even at the same stage, yet when treated comparably their stage-specific survival was on par with SCLC [12]. Similarly, advanced-stage LCNEC, if given the same systemic treatment as SCLC, has shown outcomes indistinguishable from extensive-stage SCLC​ [2]. Our study adds robust evidence that treatment parity eliminates the survival gap. It is important to discuss these findings in light of LCNEC’s underlying tumor biology. While we argue that biology is not the primary determinant of the survival difference, LCNEC does have some distinct molecular characteristics worth noting. Recent molecular profiling studies have identified two divergent subtypes of LCNEC: one subtype (often called “type I LCNEC”) is characterized by co-mutations in TP53 and RB1 – a profile very similar to classic SCLC – and the other (“type II LCNEC”) is characterized by intact RB1 and mutations in genes like STK11, KEAP1, or KRAS, overlapping with adenocarcinoma pathways​ [16]. These subtypes might have different sensitivities to therapy. For example, Derks et al. found that LCNEC tumors with wild-type RB1 (more NSCLC-like genetically) actually had better survival when treated with NSCLC-platinum doublets, whereas those with RB1 mutations did equally well on SCLC regimens​ [17]. Such data hint at a potential need for molecularly tailored therapy in the future. However, in broad populations like SEER, patients are not selected by subtype, and most would not have had molecular subtyping done. Thus, our population-level findings reflect an average of all subtypes. The fact that we observe no net survival difference after adjustment suggests that neither molecular subtype confers a uniformly worse prognosis than SCLC – again reinforcing that with appropriate therapy, both subgroups of LCNEC can achieve outcomes comparable to SCLC. Biologically, both LCNEC and SCLC are chemosensitive but prone to relapse. Both have high proliferation and a propensity for early dissemination (e.g. a high frequency of brain, liver, and bone metastases)​ [12]. It stands to reason that both should be managed aggressively. Our data support the view that LCNEC should be approached with the same urgency and comprehensive care as SCLC. Limitations Despite the strengths of a large dataset and advanced analytic methods, this study has several important limitations. First, our analysis is retrospective in nature, making it susceptible to residual confounding and selection bias. We employed propensity score matching to balance measured covariates, but this technique cannot eliminate the influence of unmeasured factors​. Variables such as patient performance status, detailed comorbidities, and physician treatment preferences are not captured in the registry and could affect both treatment selection and outcomes. Thus, even after matching, some hidden biases may remain​. Second, the use of the SEER database comes with inherent data limitations. SEER lacks granular information on systemic therapy regimens, radiation dosing, and newer treatments. We know only whether a patient received chemotherapy or radiation, but not the specific drugs, cycles, or radiation fields and dose – factors that can critically influence survival. For example, we could not distinguish if LCNEC patients received SCLC-type platinum–etoposide chemotherapy or an NSCLC-based regimen, important nuance given evidence that SCLC regimens may be more effective​. Similarly, SEER does not record performance status or pulmonary function, nor does it provide details on surgical margins or precise comorbidity indices​. The absence of these clinical details means that our adjustment may be incomplete; differences in general health (which often guide therapy intensity) could confound outcomes. Third, misclassification of histology is a potential concern. In real-world practice, distinguishing LCNEC from SCLC (or other neuroendocrine neoplasms) can be challenging, especially on small biopsies​. Pathologic criteria for LCNEC have evolved (with the WHO refining definitions in 2015), and there may have been inconsistencies in diagnosis across institutions and over the 20-year study period. Prior audits of SEER data have found variability in coding of rare lung histology – for instance, one study noted that even the broader category of large-cell carcinoma had low concordance between registry data and expert pathology review​. Although SCLC coding is generally reliable​, some cases of LCNEC might have been misdiagnosed or misreported, which could dilute observed differences. We attempted to mitigate this by focusing on high-grade neuroendocrine carcinomas and excluding uncertain cases, but the risk remains that some patients were misclassified, thereby affecting the accuracy of our comparison. Finally, our findings may not be fully generalizable to populations outside the United States. The SEER program covers approximately 28% of the US population and tends to over-represent certain demographic groups (e.g. urban and minority populations)​. Treatment patterns and outcomes in the US healthcare system could differ from those in other countries due to variations in healthcare access, oncology practice, and patient demographics. Indeed, studies from Europe and Asia have reported somewhat different characteristics for LCNEC; for example, a Dutch series observed different stage distributions​, and a Chinese genomic study identified distinct molecular subsets of pulmonary neuroendocrine tumors​. These differences caution that while the overall trends may be similar, the absolute survival rates and optimal management strategies for LCNEC in non-US populations might vary. Therefore, international validation of our results is warranted. In particular, prospective data or trials from regions with differing practices (such as higher utilization of surgery in early LCNEC in Europe or the integration of newer therapies like immunotherapy in the modern era) would be invaluable to confirm that the lack of intrinsic survival difference holds universally. Clinical implications From a clinical perspective, our study’s findings carry several actionable implications. Foremost, they highlight the importance of delivering guideline-concordant therapy to LCNEC patients. In practical terms, for localized disease this means combining surgery with adjuvant chemotherapy. Prior research has demonstrated that LCNEC patients who undergo resection benefit significantly from chemotherapy – one analysis showed that surgery plus chemotherapy yielded the best survival for stage I–III LCNEC, whereas surgery alone was associated with much poorer outcomes [18]. Unfortunately, many LCNEC patients in the past did not receive postoperative chemo, perhaps due to advanced age or lack of recognition of the tumor’s aggressiveness. Going forward, clinicians should consider even stage I LCNEC as carrying a high risk of micrometastatic disease (akin to SCLC) and strongly consider adjuvant platinum-based chemotherapy in fit patients. For advanced-stage LCNEC, our findings underscore that platinum-etoposide (the SCLC regimen) should be the default choice in the absence of contraindications, given the accumulating evidence of its superiority​ [15]. We also suggest that practices used in SCLC might be extrapolated to LCNEC: for instance, the consideration of prophylactic cranial irradiation in patients with LCNEC who have a good response to initial therapy could be an area for future investigation, as brain metastases are common in both diseases. Additionally, enrollment of LCNEC patients in clinical trials is crucial. To date, there are no dedicated standard treatment guidelines for LCNEC, and management is based on limited data or borrowing from SCLC/NSCLC protocols​ [14]. This lack of standardized guidance has been explicitly acknowledged in the literature: LCNEC treatment remains “on debate” and there is an unmet need for prospective studies​ [14]. Our results provide a strong rationale for treating LCNEC with SCLC-based multimodality regimens, and prospective trials could formally confirm this approach. Another implication relates to emerging therapies. The last two decades (2000–2020) have seen modest improvements in SCLC outcomes with new treatments such as immune checkpoint inhibitors. The addition of atezolizumab or durvalumab to first-line chemotherapy has become a new standard in extensive-stage SCLC, improving 2-year survival rates​ [5]. LCNEC, which often exhibits a high tumor mutational burden, might similarly benefit from immunotherapy – indeed, case series have reported some durable responses to PD-1/PD-L1 inhibitors in LCNEC​ [19]. Future research should explore the role of immunotherapy and other novel agents (e.g. DLL3-targeted therapies or PARP inhibitors being studied in SCLC) in LCNEC. Additionally, as comprehensive genomic profiling becomes more routine, stratifying LCNEC patients by molecular subtype in trials could determine if a precision medicine approach (treating “SCLC-like” LCNEC with SCLC protocols and “NSCLC-like” LCNEC with NSCLC protocols) improves outcomes further. Our finding that, on average, treatment equalization removes survival differences suggests that a one-size-fits-all SCLC-like approach is effective for most; but it’s possible that refining this by subtype could yield incremental benefits. Conclusion In summary, this study provides strong evidence that LCNEC and SCLC have broadly comparable survival outcomes when patients are matched on key prognostic factors and receive appropriate treatment. The worse survival observed for LCNEC in naive comparisons appears to stem from imbalances in stage and, critically, in treatment delivery. When those are accounted for, LCNEC does not behave more aggressively than SCLC. These findings emphasize that clinicians should approach LCNEC with the same aggressive treatment strategies used for SCLC, in order to close the historical survival gap. They also reassure us that the classification of LCNEC as a high-grade neuroendocrine carcinoma alongside SCLC is appropriate from a prognostic standpoint. Finally, our work highlights the need for continued improvements in the care of LCNEC patients – through adherence to multimodal therapy, development of consensus guidelines, and prospective research to optimize management. By addressing the treatment disparities that have existed, we may substantially improve outcomes for LCNEC, turning a previously dismal prognosis into one that, while still serious, is on par with other aggressive lung cancers rather than worse. Ultimately, the convergence of LCNEC and SCLC survival under equitable treatment is a hopeful message: it suggests that LCNEC’s fate is not preordained to be worse than SCLC, and that with proper therapy, LCNEC patients can achieve survival outcomes equivalent to those of SCLC patients. Future studies should build on this knowledge, exploring the best therapeutic approaches for LCNEC (including SCLC-based regimens, targeted therapies, and immunotherapies) and ensuring that all patients with this rare cancer have access to optimal care. Through such efforts, the current gap between evidence and practice in LCNEC management can be narrowed, if not closed, just as our data show the survival gap narrowing when treatment is not a limiting factor.​ Declarations Ethics approval and consent to participate This study used de‑identified data from the publicly available SEER database and did not involve direct patient contact or the use of individually identifiable health information. Under the U.S. Common Rule, research using only publicly available, de‑identified data is exempt from institutional review board oversight; therefore, ethics approval and patient consent were not required. Consent for publication Not applicable. Availability of data and materials The dataset analyzed during the current study is available in the SEER repository: https://seer.cancer.gov. Competing interests The authors declare that they have no competing interests. Funding No external funding was received for this work. Authors’ contributions AH conceived the study, performed data extraction and statistical analyses, and drafted the manuscript. PS assisted with critical revision of the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the SEER Program tumor registries and the National Cancer Institute for providing access to the data. References Andrini E, Marchese PV, de Biase D, Mosconi C, Siepe G, Panzuto F, Ardizzoni A, Campana D, Lamberti G: Large Cell Neuroendocrine Carcinoma of the Lung: Current Understanding and Challenges . Journal of Clinical Medicine 2022, 11 . Wang J, Ye L, Cai H, Jin M: Comparative study of large cell neuroendocrine carcinoma and small cell lung carcinoma in high-grade neuroendocrine tumors of the lung: a large population-based study . Journal of Cancer 2019, 10 :4226 - 4236. Peng W, Cao L, Chen L, Lin G, Zhu B, Hu X, Lin Y, Zhang S, Jiang M, Wang J et al : Comprehensive Characterization of the Genomic Landscape in Chinese Pulmonary Neuroendocrine Tumors Reveals Prognostic and Therapeutic Markers (CSWOG-1901) . The Oncologist 2022, 27 :e116 - e125. Lo Russo G, Pusceddu S, Proto C, Macerelli M, Signorelli D, Vitali M, Ganzinelli M, Gallucci R, Zilembo N, Platania M et al : Treatment of lung large cell neuroendocrine carcinoma . Tumor Biology 2016, 37 :7047-7057. Yang W, Wang W, Li Z-r, Wu J, Xu X, Chen C, Ye X: Differences between Advanced Large Cell Neuroendocrine Carcinoma and Advanced Small Cell Lung Cancer: A Propensity Score Matching Analysis . Journal of Cancer 2023, 14 :1541 - 1552. Fasano M, Della Corte CM, Papaccio F, Ciardiello F, Morgillo F: Pulmonary Large-Cell Neuroendocrine Carcinoma . Journal of Thoracic Oncology 2015, 10 :1133 - 1141. Limonnik V, Abel S, Finley GG, Long GS, Wegner RE: Factors associated with treatment receipt and overall survival for patients with locally advanced large cell neuroendocrine carcinoma of the lung: A National Cancer Database analysis . Lung cancer 2020, 150 :107-113. Zhang JT, Li Y, Yan L-x, Zhu Z, Dong X-r, Chu Q, Wu L, Zhang H-M, Xu C-w, Lin G et al : Disparity in clinical outcomes between pure and combined pulmonary large-cell neuroendocrine carcinoma: A multi-center retrospective study . Lung cancer 2019, 139 :118-123. Wang Y, Qian F-f, Chen Y, Yang Z, Hu M-j, Lu J, Zhang Y, Zhang W, Cheng L, Han B: Comparative Study of Pulmonary Combined Large-Cell Neuroendocrine Carcinoma and Combined Small-Cell Carcinoma in Surgically Resected High-Grade Neuroendocrine Tumors of the Lung . Frontiers in Oncology 2021, 11 . Moon JY, Choi SH, Kim TH, Lee J, Pyo J, Kim YT, Lee SJ, Yoon HI, Cho J, Lee CG: Clinical features and treatment outcomes of resected large cell neuroendocrine carcinoma of the lung . Radiation Oncology Journal 2021, 39 :288 - 296. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP: Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies . Bmj 2007, 335 (7624):806-808. Derks JL, Hendriks LE, Buikhuisen WA, Groen HJ, Thunnissen E, van Suylen RJ, Houben R, Damhuis RA, Speel EJ, Dingemans AM: Clinical features of large cell neuroendocrine carcinoma: a population-based overview . Eur Respir J 2016, 47 (2):615-624. Varlotto JM, Medford-Davis LN, Recht A, Flickinger JC, Schaefer E, Zander DS, DeCamp MM: Should large cell neuroendocrine lung carcinoma be classified and treated as a small cell lung cancer or with other large cell carcinomas? J Thorac Oncol 2011, 6 (6):1050-1058. Ferrara MG, Stefani A, Simbolo M, Pilotto S, Martini M, Lococo F, Vita E, Chiappetta M, Cancellieri A, D’Argento E et al : Large Cell Neuro-Endocrine Carcinoma of the Lung: Current Treatment Options and Potential Future Opportunities . Frontiers in Oncology 2021, Volume 11 - 2021 . He J, Li S, He J: MA12.11 What Regimen Should Be Chosen for Pulmonary Large Cell Neuroendocrine Carcinoma? A Systemic Review and Meta-Analysis . Journal of Thoracic Oncology 2021, 16 (3):S180-S181. Derks J, Leblay N, van Suylen RJ, Thunnissen E, den Bakker MA, Groen HJM, Smit E, Damhuis RA, van den Broek E, Charbrier A et al : Genomic subtypes of pulmonary large cell neuroendocrine carcinoma (LCNEC) may predict chemotherapy outcome . Annals of Oncology 2017, 28 :v143. Derks JL, Leblay N, Thunnissen E, van Suylen RJ, den Bakker M, Groen HJM, Smit EF, Damhuis R, van den Broek EC, Charbrier A et al : Molecular Subtypes of Pulmonary Large-cell Neuroendocrine Carcinoma Predict Chemotherapy Treatment Outcome . Clin Cancer Res 2018, 24 (1):33-42. Gu J, Gong D, Wang Y, Chi B, Zhang J, Hu S, Min L: The demographic and treatment options for patients with large cell neuroendocrine carcinoma of the lung . Cancer Med 2019, 8 (6):2979-2993. Hermans BCM, Derks JL, Thunnissen E, van Suylen RJ, den Bakker MA, Groen HJM, Smit EF, Damhuis RA, van den Broek EC, Stallinga CM et al : Prevalence and prognostic value of PD-L1 expression in molecular subtypes of metastatic large cell neuroendocrine carcinoma (LCNEC) . Lung Cancer 2019, 130 :179-186. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6514503","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447052942,"identity":"7ad6b75a-b0b5-4105-a824-bddfe553b571","order_by":0,"name":"Ali Hemade","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACdsY2EMXYwN98AEhLyBDWwgzTInEsAaSFhwgtDGwQLQw5BiAGYS38zcxtDz7m2Mn2M5z5/OpGjQUPA/vhoxvwaZE4zNhuOHNbsvHM5t5t1jnHgA7jSUu7gdeaw4xt0rzbmBM3HDi7zTiHDahFgscMrxZ5kJa/2+oT9x/IeWac848ILQYgLYzbDiduYMhhfpzbRoQWQ6AWyd5tx41n3DhmxpzbJ8HDRsgvcsfbn0n83FYt29/f/Phzzrc6OX72w8fwex8JsEmASWKVgwDzB1JUj4JRMApGwcgBACr6SekvNCM5AAAAAElFTkSuQmCC","orcid":"","institution":"Lebanese University Faculty of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"","lastName":"Hemade","suffix":""},{"id":447052943,"identity":"71cb167e-678b-4d4a-b921-037812d83975","order_by":1,"name":"Pascale Salameh","email":"","orcid":"","institution":"Lebanese University","correspondingAuthor":false,"prefix":"","firstName":"Pascale","middleName":"","lastName":"Salameh","suffix":""}],"badges":[],"createdAt":"2025-04-23 16:46:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6514503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6514503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81934017,"identity":"0532c1f7-ca23-4410-889f-97785fc3bf72","added_by":"auto","created_at":"2025-05-05 05:43:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Overall Survival (OS) Curve for Unmatched SCLC vs. LCNEC Patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6514503/v1/2334eaa2fac31d2743308abc.jpg"},{"id":81934018,"identity":"7c4cc844-93f4-4f69-b870-660d33f0c884","added_by":"auto","created_at":"2025-05-05 05:43:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":76677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Overall Survival (OS) Curve for Matched SCLC vs. LCNEC Patients\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6514503/v1/beaa315b340008d4417550df.jpg"},{"id":81936071,"identity":"24300dc2-9f30-4719-9cc6-c68e8de1b094","added_by":"auto","created_at":"2025-05-05 06:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2486878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6514503/v1/1abb8b03-3980-4031-b78b-2b8f5d52defd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnmasking the Survival Disparity Between Large-Cell Neuroendocrine Carcinoma and Small-Cell Lung Cancer: A SEER Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmall-cell lung cancer (SCLC) and large-cell neuroendocrine carcinoma (LCNEC) represent two distinct yet overlapping high-grade neuroendocrine lung carcinomas (HGNECs). While SCLC is well-characterized with established treatment guidelines, LCNEC remains a rare and challenging entity with no universally accepted management approach [1]. The World Health Organization (WHO) reclassified LCNEC in 2015, distinguishing it from large-cell carcinoma and aligning it more closely with SCLC due to its aggressive behavior [2]. Despite histologic similarities, SCLC and LCNEC exhibit different genomic profiles, with LCNEC often harboring mutations akin to non-small-cell lung cancer (NSCLC) [3].\u003c/p\u003e \u003cp\u003eUnlike SCLC, which is treated with platinum-etoposide chemotherapy, LCNEC lacks a standardized therapeutic approach, leading to considerable variability in clinical practice [4]. Some studies suggest that LCNEC should be treated similarly to SCLC, whereas others indicate that an NSCLC-based approach might be more effective [5]. The lack of randomized trials has resulted in conflicting survival data, with some reports indicating better outcomes for LCNEC, while others suggest a poorer prognosis compared to SCLC [6].\u003c/p\u003e \u003cp\u003eRecent studies utilizing the Surveillance, Epidemiology, and End Results (SEER) database and the National Cancer Database (NCDB) have attempted to clarify the prognosis of LCNEC compared to SCLC [7]. However, many of these analyses lack adjustments for confounding variables such as treatment modalities, disease stage, and demographic factors, leading to potential biases in survival estimation [8]. Additionally, prior studies often focus on limited timeframes or do not incorporate time-dependent survival trends, leaving an important gap in understanding how prognosis evolves over time [5].\u003c/p\u003e \u003cp\u003eGiven the inconsistencies in prognosis and treatment for LCNEC and SCLC, a well-controlled, propensity score-matched (PSM) analysis is needed to eliminate confounders and provide a more accurate survival comparison [9]. This study leverages the SEER database (2000\u0026ndash;2025) to conduct the largest propensity-matched survival analysis to date, controlling for key prognostic factors such as age, sex, tumor stage, treatment modality, and lymph node ratio (LNR) [10]. Furthermore, our study incorporates a time-dependent Cox model to examine how the relative prognosis of LCNEC and SCLC shifts over time, an approach that has not been widely applied in previous research.\u003c/p\u003e \u003cp\u003eThis study aims to compare overall survival (OS) and cancer-specific survival (CSS) between LCNEC and SCLC using propensity score matching, determine whether survival disparities persist after adjusting for confounders, evaluate the impact of treatment modalities such as surgery, chemotherapy, and radiotherapy on prognosis, and analyze time-dependent survival trends to assess whether LCNEC mortality risk changes over time. We hypothesize that LCNEC appears to have worse survival than SCLC in unmatched analysis, but after PSM adjustment, survival differences will diminish, indicating that treatment disparities rather than intrinsic tumor biology drive prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study utilized a well-established oncology database, the Surveillance, Epidemiology, and End Results (SEER) database, to compare survival outcomes between Small-Cell Lung Cancer (SCLC) and Large-Cell Neuroendocrine Carcinoma (LCNEC) diagnosed between January 1st 2004 and December 31st 2021 (SEER Nov. 2023 submission). The primary objective was to compare the OS of LCNEC and SCLC before and after adjustment for confounding variables. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [11] guidelines to ensure methodological rigor and reproducibility.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Patient Cohort\u003c/h3\u003e\n\u003cp\u003eData were extracted from the SEER database, including information on patient demographics, tumor characteristics, treatment modalities, and survival outcomes. The dataset included 26,930 patients diagnosed with either SCLC or LCNEC. Eligibility criteria required a histologically confirmed diagnosis of SCLC (ICD-O-3: 8041/3) or LCNEC (ICD-O-3: 8013/3), available treatment data including chemotherapy, surgery, and radiotherapy, and documented tumor staging based on the T and N classification system. Patients were excluded if they had stage IV disease, mixed histology tumors, or incomplete clinical data regarding tumor stage, nodal involvement, or treatment history.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes were Overall Survival (OS), defined as the time from diagnosis to all-cause mortality, and Cancer-Specific Survival (CSS), defined as the time from diagnosis to lung cancer-related mortality.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo mitigate baseline differences between SCLC and LCNEC patients, 1:1 nearest-neighbor propensity score matching (PSM) without replacement was performed. The propensity score was estimated using a logistic regression model, incorporating age, sex, race, tumor stage (T and N classification), chemotherapy, surgery, radiotherapy, and year of diagnosis as covariates. Matching quality was assessed using standardized mean differences (SMDs), with an SMD of less than 0.1 indicating adequate balance between groups. Following PSM, a total of 2,359 LCNEC patients were successfully matched to 2,359 SCLC patients.\u003c/p\u003e \u003cp\u003eBaseline characteristics were summarized using means and standard deviations for continuous variables and frequencies with percentages for categorical variables. Differences between groups were compared using t-tests or Wilcoxon rank-sum tests for continuous variables and chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eSurvival analysis was conducted using Kaplan-Meier methods, with log-rank tests employed to compare survival distributions between groups. Cox proportional hazard regression models were constructed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for OS and CSS. Univariable Cox models were first applied to each predictor, followed by multivariable Cox models adjusting for all clinically relevant covariates. We assessed the proportionalhazards assumption for each covariate using Schoenfeld residuals. The test revealed significant violations for histology (Cancer_binary; χ\u0026sup2;=27.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and several treatment and staging variables (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating nonproportional effects over time. Consequently, we fitted an extended Cox model incorporating a timeinteraction term for histology to determine whether the relative hazard of LCNEC versus SCLC varied throughout the follow-up period.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea: Descriptive Statistics Before Propensity Score Matching Before PSM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCNEC (n\u0026thinsp;=\u0026thinsp;2,359)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC (n\u0026thinsp;=\u0026thinsp;24,571)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Mean Difference (SMD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.13\u0026thinsp;\u0026plusmn;\u0026thinsp;9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis (YOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2012.49\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2011.73\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.4% (1,095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7% (13,442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.6% (1,264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.3% (11,129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4% (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7% (174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8% (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6% (880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0% (283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9% (2,195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0% (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1% (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.8% (1,976)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.7% (21,295)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.0% (1,226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2% (5,703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.0% (1,133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.8% (18,868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2% (806)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.2% (22,908)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.8% (1,553)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8% (1,663)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.3% (1,635)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.1% (9,362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.7% (724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.9% (15,209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage (T) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6% (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0% (255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.7% (275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9% (1,707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9% (305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6% (2,845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6% (298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8% (3,136)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.6% (344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.7% (9,013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Stage (N) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.4% (1,426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8% (5,843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2% (264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2% (2,495)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8% (538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.8% (12,736)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.6% (131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2% (3,497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Descriptive Statistics After Propensity Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCNEC (n\u0026thinsp;=\u0026thinsp;1898)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC (n\u0026thinsp;=\u0026thinsp;1898)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Mean Difference (SMD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.4 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.6 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e962 (50.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e847 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1625 (85.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1644 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy, Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1107 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1240 (65.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery, Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1092 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1089 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy, Yes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e707 (37.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e810 (42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e340 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e679 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e322 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e416 (21.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e472 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Stage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1002 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e899 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e526 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e625 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012.4 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2012.3 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnmatched Analysis\u003c/h3\u003e\n\u003cp\u003ePrior to matching (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), significant baseline differences were observed between the SCLC and LCNEC groups. LCNEC patients were more likely to undergo surgery (65.8% vs. 6.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas SCLC patients were more frequently treated with chemotherapy (76.8% vs. 48.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and radiotherapy (61.9% vs. 30.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Tumor stage distributions also varied, with a higher prevalence of advanced nodal involvement (N2: 51.8% vs. 22.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among SCLC patients. The mean age of LCNEC patients was slightly lower than that of SCLC patients (67.13 vs. 67.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eKaplan-Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrated that SCLC patients had significantly better OS compared to LCNEC patients in the unmatched dataset (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The survival probability of LCNEC was notably lower, particularly in the early follow-up period. Univariable Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) confirmed this trend, with LCNEC associated with significantly worse survival compared to SCLC (HR\u0026thinsp;=\u0026thinsp;1.817, 95% CI: 1.727\u0026ndash;1.911, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariable Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), adjusting for age, sex, race, chemotherapy, surgery, radiotherapy, tumor stage, nodal involvement, and year of diagnosis, reinforced this survival disparity. LCNEC remained associated with significantly worse survival compared to SCLC (HR\u0026thinsp;=\u0026thinsp;1.306, 95% CI: 1.229\u0026ndash;1.388, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Treatment modalities played a crucial role in survival, with chemotherapy (HR\u0026thinsp;=\u0026thinsp;0.742, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), surgery (HR\u0026thinsp;=\u0026thinsp;0.618, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.666, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) associated with improved survival outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariable Cox Regression Results (Matched vs. Unmatched Data)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatched p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnmatched HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnmatched p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCLC vs. LCNEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.86\u0026ndash;2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.73\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.03\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.03\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.76\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.11\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.61\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.88\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.46\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.83\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.27\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.31\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.69\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.88\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.47\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58 (0.56\u0026ndash;0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.34\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (0.41\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.22\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.58\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTumor Stages (T)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.54\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.89\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.50\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.13\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.60\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.21\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.01\u0026ndash;3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0466\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 (1.49\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLymph Node Stages (N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64 (1.45\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.10\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.47 (2.29\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57 (1.52\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93 (2.57\u0026ndash;3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76 (1.69\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.95\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox Regression Results (Matched vs. Unmatched Data)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatched p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnmatched HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnmatched p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCLC vs. LCNEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.73\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31 (1.23\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.03\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (1.02\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.09\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (1.14\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRace (Reference: White)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.64\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.76\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.70\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.81\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.27\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.27\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0258\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.62\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55 (0.53\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.31\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.37\u0026ndash;0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.57\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.51\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTumor Stages (Reference: T1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.05\u0026ndash;3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0353\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.20\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89 (1.53\u0026ndash;5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66 (1.44\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.88 (1.53\u0026ndash;5.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.0010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.57\u0026ndash;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eLymph Node Stages (Reference: N0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53 (1.33\u0026ndash;1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (1.20\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81 (1.59\u0026ndash;2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.44\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92 (1.63\u0026ndash;2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72 (1.64\u0026ndash;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.96\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.98\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMatched Analysis\u003c/h2\u003e \u003cp\u003eAfter PSM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), the balance of covariates improved, though some residual differences remained. Kaplan-Meier analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the matched cohort showed that SCLC retained a significant survival advantage over LCNEC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with survival curves remaining separated throughout the follow-up period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnivariable Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the matched cohort confirmed that LCNEC was associated with significantly worse survival compared to SCLC (HR\u0026thinsp;=\u0026thinsp;1.982, 95% CI: 1.478\u0026ndash;2.657, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Matching on Prognosis\u003c/h2\u003e \u003cp\u003eMultivariable Cox regression (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) adjusting for demographic, clinical, and treatment-related factors revealed a critical shift in survival outcomes after matching. LCNEC was no longer a significant predictor of worse survival after matching (HR\u0026thinsp;=\u0026thinsp;0.825, 95% CI: 0.728\u0026ndash;0.934, p\u0026thinsp;=\u0026thinsp;0.0024). In the conventional (timefixed) Cox model, LCNEC appeared to carry a reduced hazard relative to SCLC (HR\u0026thinsp;=\u0026thinsp;0.82; 95% CI 0.73\u0026ndash;0.93). However, the proportionalhazards assumption (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was violated (Schoenfeld p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), rendering that single estimate unreliable. The timedependent Cox model (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) therefore provides the valid inference: the main effect of histology at one month was HR₁₋ₘₒₙₜₕ=1.98 (95% CI 1.48\u0026ndash;2.66), and the timeinteraction coefficient (Cancer_binary\u0026times;log(time)) was 0.74 (95% CI 0.67\u0026ndash;0.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSchoenfeld Residuals Test for Proportional Hazards Assumption in the PropensityScore\u0026ndash;Adjusted Cox Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtype (LCNEC vs. SCLC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropensity score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0042\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1268.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1145.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis (YOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e336.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLOBAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2418.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;0.0001\u0026nbsp;*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates violation of the proportional hazards assumption for that variable; the GLOBAL test assesses overall model validity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTime-Dependent Cox Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer (SCLC vs. LCNEC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.48\u0026ndash;2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74 (0.67\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.98\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.94\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57 (0.44\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.07\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.56\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0598\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.91\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy (Yes vs. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.56\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiotherapy Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.08\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage (T2 vs. T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (0.75\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage (T3 vs. T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32 (1.09\u0026ndash;5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0293\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage (T4 vs. T1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.08 (0.98\u0026ndash;4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0575\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor Stage Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0129\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Stage (N1 vs. N0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34 (1.12\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Stage (N2 vs. N0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.31\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Stage (N3 vs. N0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.35\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph Node Stage Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.0013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear of Diagnosis (YOD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.07\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYOD Time Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.92\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large population-based analysis spanning 2000\u0026ndash;2025, we directly compared survival outcomes between LCNEC and SCLC, employing robust methods to control for confounders. Our findings offer important insights into the relative prognoses of these two high-grade neuroendocrine lung cancers. First, in the unmatched (unadjusted) cohort, LCNEC was associated with significantly worse survival than SCLC. By both overall survival and cancer-specific survival endpoints, patients diagnosed with LCNEC had shorter median survival and lower survival probabilities over time compared to those with SCLC. This initial result aligns with the notion that LCNEC is an extremely aggressive malignancy \u0026ndash; in fact, previous reports have noted very poor outcomes for LCNEC in the absence of effective therapy. For example, Derks \u003cem\u003eet al.\u003c/em\u003e observed a median OS of only around 4 months for stage IV LCNEC (versus ~\u0026thinsp;7 months for stage IV SCLC) in a nationwide registry, underscoring the rapid lethality of LCNEC when not controlled​ [5]. Our finding of inferior unadjusted survival for LCNEC is consistent with such data and with the clinical impression that LCNEC, like SCLC, often runs an explosive clinical course. It is worth noting, however, that unadjusted comparisons can be misleading if the two groups differ systematically in factors like stage at diagnosis or treatment received. Therefore, the crude survival disadvantage observed for LCNEC required further scrutiny with adjusted analyses.\u003c/p\u003e \u003cp\u003eAfter performing propensity score matching to balance key covariates between the LCNEC and SCLC groups, we re-examined the survival difference. Notably, even in the PSM-matched cohort, the Kaplan-Meier survival curves for OS and CSS remained separated, with LCNEC patients continuing to experience worse outcomes than matched SCLC patients. This indicates that certain disparities persisted despite matching on factors such as age, sex, race, stage, nodal status, treatment modalities, and diagnosis year.\u003c/p\u003e \u003cp\u003eHowever, when we applied multivariable Cox regression to the matched cohort \u0026ndash; thus adjusting for any residual differences and allowing a refined assessment of histology\u0026rsquo;s impact \u0026ndash; the picture changed. In the adjusted Cox model (on the matched sample), LCNEC was no longer associated with significantly worse OS. In fact, the point estimate of the hazard ratio for LCNEC (relative to SCLC) fell below 1.0, suggesting that \u003cem\u003econditional on equivalent baseline characteristics\u003c/em\u003e, LCNEC patients did \u003cem\u003enot\u003c/em\u003e have higher mortality risk than SCLC patients; their risk was slightly lower. In other words, once we controlled for stage, treatment, and other covariates, LCNEC did not independently predict worse overall survival. This is a critical finding that challenges the raw comparison. It implies that the apparent survival gap seen in unmatched analysis was largely attributable to imbalances in prognostic factors and treatment, rather than an intrinsic difference in tumor behavior. Our result here is in agreement with prior studies that performed multivariate adjustments or matching. For example, Derks \u003cem\u003eet al.\u003c/em\u003e reported that multivariate-adjusted OS in LCNEC was equivalent to that in SCLC, after accounting for stage and therapy​ [12]. Likewise, Yang \u003cem\u003eet al.\u003c/em\u003e analyzed stage IV cases with PSM and found no significant OS or CSS difference between LCNEC and SCLC in the matched groups​ [5]. Our study confirms on a larger scale that, after rigorous adjustment, LCNEC per se does not portend a worse survival than SCLC. The persistence of a gap in the unadjusted and matched-KM analysis, followed by its disappearance in the adjusted Cox, suggests that subtle confounders (likely related to treatment and stage) influenced the crude outcomes.\u003c/p\u003e \u003cp\u003eOne novel aspect of our analysis was the use of a time-dependent Cox model to investigate how the relative hazard of death for LCNEC versus SCLC changed over the follow-up period. Interestingly, this analysis revealed a significant interaction between cancer type and time, indicating that the hazard ratio for LCNEC (vs SCLC) was \u003cem\u003enot constant\u003c/em\u003e but decreased over time. In practical terms, we found that the survival disadvantage of LCNEC was most pronounced early after diagnosis and then narrowed as time went on. During the initial months and first couple of years, LCNEC patients were at higher risk of death than SCLC patients (reflected in the worse short-term OS/CSS). But among those who survived longer-term, the difference in outcomes became much smaller, and later in the follow-up the hazard for LCNEC patients approached or even dipped below that of SCLC patients. This dynamic explains why our Kaplan-Meier curves showed separation early but began to converge later, and it reinforces the idea that the early period following diagnosis is critical for LCNEC outcomes. A plausible interpretation is that any excess mortality with LCNEC occurs primarily in the short term \u0026ndash; perhaps due to suboptimal initial management \u0026ndash; whereas SCLC carries a sustained risk that catches up over time (with its well-known pattern of early relapse even after initial response). To our knowledge, this is the first study to demonstrate a time-varying effect in the LCNEC vs SCLC survival comparison. This finding suggests that when evaluating prognosis, one should consider \u003cem\u003econditional survival\u003c/em\u003e: if an LCNEC patient can overcome the initial high-risk phase (possibly with effective therapy), their longer-term survival outlook may become comparable to that of SCLC survivors.\u003c/p\u003e \u003cp\u003eOur results have several important implications when contextualized with prior literature, and they help reconcile some of the discrepancies among earlier studies. First, the unadjusted analyses in our study (and others) might have been confounded by differences in clinical management between LCNEC and SCLC. Historically, SCLC has an established treatment paradigm of early, intensive therapy \u0026ndash; virtually all SCLC patients with limited-stage disease receive combined chemo-radiotherapy, and those with extensive disease receive prompt platinum-doublet chemotherapy, often with prophylactic cranial irradiation in responders [5]. In contrast, the optimal treatment for LCNEC has been less clear, and prior to its recognition as a high-grade neuroendocrine carcinoma, many LCNEC cases were managed similarly to other NSCLCs​ [13]. In the early 2000s, for example, a patient with LCNEC might have undergone surgery but then \u003cem\u003enot\u003c/em\u003e received adjuvant chemotherapy, because clinicians considered it a type of large-cell carcinoma. Varlotto \u003cem\u003eet al.\u003c/em\u003e noted that the clinical profiles of early-stage LCNEC patients (those undergoing resection) were more akin to other large-cell carcinomas than to SCLC, and their study argued LCNEC should be treated like NSCLC rather than SCLC​ [13]. Indeed, in that analysis, resected LCNEC patients had survival outcomes similar to non-neuroendocrine NSCLC and better than surgically managed SCLC, once again suggesting that when treated with surgery (with or without adjuvant therapy), LCNEC can do well​ [13]. However, the absence of chemotherapy in many resected-LCNEC cases likely contributed to higher recurrence rates. Several reports have documented that surgery alone is often inadequate for LCNEC, with very high relapse rates and poor long-term survival if adjuvant therapy is omitted​ [14]. Our findings support this: the early steep drop in the LCNEC survival curve is consistent with many LCNEC patients succumbing quickly, likely those who did not receive effective systemic therapy upfront.\u003c/p\u003e \u003cp\u003eCrucially, when LCNEC is managed aggressively \u003cem\u003ein the same way as SCLC\u003c/em\u003e, outcomes appear to improve markedly. A number of retrospective studies and a recent meta-analysis have directly compared SCLC-type chemotherapy (platinum-etoposide, the standard for SCLC) versus NSCLC-type regimens (platinum with gemcitabine, taxane, or pemetrexed) in LCNEC patients​ [15]. The consensus emerging from these studies is that SCLC-type chemotherapy yields superior results in LCNEC. For instance, a meta-analysis including\u0026thinsp;~\u0026thinsp;446 LCNEC patients reported that first-line platinum-etoposide was associated with significantly longer OS (pooled HR\u0026thinsp;~\u0026thinsp;0.73) and PFS (HR\u0026thinsp;~\u0026thinsp;0.68) compared to NSCLC chemo regimens​ [15]. This finding held true not only in metastatic disease but also in the adjuvant setting: LCNEC patients who received post-operative SCLC-style chemo had better survival than those who received NSCLC adjuvant chemo​ [15]. Consistent with this, multiple small series have shown higher response rates in advanced LCNEC when treated with SCLC regimens, whereas NSCLC regimens often yield suboptimal results​ [2]. Our study did not directly test chemotherapy regimens, but the fact that LCNEC\u0026rsquo;s survival disadvantage disappeared after matching and adjustment strongly implies that treatment differences were a major driver of the initial disparity. It is likely that in the unmatched population, a substantial subset of LCNEC patients did not receive chemotherapy or received less effective NSCLC-type treatments, contributing to worse outcomes, whereas most SCLC patients (especially in modern years) received appropriate chemo-radiation per guidelines. After matching on treatment variables (and indirectly by adjusting for diagnosis year, which captures changes in practice), we essentially compared LCNEC and SCLC patients with similar therapy \u0026ndash; and in that scenario, their survival was equivalent. This interpretation is further bolstered by the time-interaction finding: the narrowing survival gap over time suggests that once the treatment effect in the early period is accounted for (i.e. those who survived the early phase likely had effective initial therapy), LCNEC patients do as well as SCLC patients subsequently. In sum, our results indicate that the apparent prognostic disadvantage of LCNEC is not due to intrinsically more aggressive biology, but rather due to historically inadequate or delayed treatment in some LCNEC cases. This echoes observations from prior population-based studies: for example, one SEER analysis noted that LCNEC patients were much less likely to receive adjuvant chemotherapy than SCLC patients, even at the same stage, yet when treated comparably their stage-specific survival was on par with SCLC [12]. Similarly, advanced-stage LCNEC, if given the same systemic treatment as SCLC, has shown outcomes indistinguishable from extensive-stage SCLC​ [2]. Our study adds robust evidence that treatment parity eliminates the survival gap. It is important to discuss these findings in light of LCNEC\u0026rsquo;s underlying tumor biology. While we argue that biology is not the primary determinant of the survival difference, LCNEC does have some distinct molecular characteristics worth noting. Recent molecular profiling studies have identified two divergent subtypes of LCNEC: one subtype (often called \u0026ldquo;type I LCNEC\u0026rdquo;) is characterized by co-mutations in TP53 and RB1 \u0026ndash; a profile very similar to classic SCLC \u0026ndash; and the other (\u0026ldquo;type II LCNEC\u0026rdquo;) is characterized by intact RB1 and mutations in genes like STK11, KEAP1, or KRAS, overlapping with adenocarcinoma pathways​ [16]. These subtypes might have different sensitivities to therapy. For example, Derks \u003cem\u003eet al.\u003c/em\u003e found that LCNEC tumors with wild-type RB1 (more NSCLC-like genetically) actually had better survival when treated with NSCLC-platinum doublets, whereas those with RB1 mutations did equally well on SCLC regimens​ [17]. Such data hint at a potential need for molecularly tailored therapy in the future. However, in broad populations like SEER, patients are not selected by subtype, and most would not have had molecular subtyping done. Thus, our population-level findings reflect an average of all subtypes. The fact that we observe no net survival difference after adjustment suggests that neither molecular subtype confers a uniformly worse prognosis than SCLC \u0026ndash; again reinforcing that with appropriate therapy, both subgroups of LCNEC can achieve outcomes comparable to SCLC. Biologically, both LCNEC and SCLC are chemosensitive but prone to relapse. Both have high proliferation and a propensity for early dissemination (e.g. a high frequency of brain, liver, and bone metastases)​ [12]. It stands to reason that both should be managed aggressively. Our data support the view that LCNEC should be approached with the same urgency and comprehensive care as SCLC.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the strengths of a large dataset and advanced analytic methods, this study has several important limitations. First, our analysis is retrospective in nature, making it susceptible to residual confounding and selection bias. We employed propensity score matching to balance measured covariates, but this technique cannot eliminate the influence of unmeasured factors​. Variables such as patient performance status, detailed comorbidities, and physician treatment preferences are not captured in the registry and could affect both treatment selection and outcomes. Thus, even after matching, some hidden biases may remain​. Second, the use of the SEER database comes with inherent data limitations. SEER lacks granular information on systemic therapy regimens, radiation dosing, and newer treatments. We know only whether a patient received chemotherapy or radiation, but not the specific drugs, cycles, or radiation fields and dose \u0026ndash; factors that can critically influence survival. For example, we could not distinguish if LCNEC patients received SCLC-type platinum\u0026ndash;etoposide chemotherapy or an NSCLC-based regimen, important nuance given evidence that SCLC regimens may be more effective​. Similarly, SEER does not record performance status or pulmonary function, nor does it provide details on surgical margins or precise comorbidity indices​. The absence of these clinical details means that our adjustment may be incomplete; differences in general health (which often guide therapy intensity) could confound outcomes. Third, misclassification of histology is a potential concern. In real-world practice, distinguishing LCNEC from SCLC (or other neuroendocrine neoplasms) can be challenging, especially on small biopsies​. Pathologic criteria for LCNEC have evolved (with the WHO refining definitions in 2015), and there may have been inconsistencies in diagnosis across institutions and over the 20-year study period. Prior audits of SEER data have found variability in coding of rare lung histology \u0026ndash; for instance, one study noted that even the broader category of large-cell carcinoma had low concordance between registry data and expert pathology review​. Although SCLC coding is generally reliable​, some cases of LCNEC might have been misdiagnosed or misreported, which could dilute observed differences. We attempted to mitigate this by focusing on high-grade neuroendocrine carcinomas and excluding uncertain cases, but the risk remains that some patients were misclassified, thereby affecting the accuracy of our comparison. Finally, our findings may not be fully generalizable to populations outside the United States. The SEER program covers approximately 28% of the US population and tends to over-represent certain demographic groups (e.g. urban and minority populations)​. Treatment patterns and outcomes in the US healthcare system could differ from those in other countries due to variations in healthcare access, oncology practice, and patient demographics. Indeed, studies from Europe and Asia have reported somewhat different characteristics for LCNEC; for example, a Dutch series observed different stage distributions​, and a Chinese genomic study identified distinct molecular subsets of pulmonary neuroendocrine tumors​. These differences caution that while the overall trends may be similar, the absolute survival rates and optimal management strategies for LCNEC in non-US populations might vary. Therefore, international validation of our results is warranted. In particular, prospective data or trials from regions with differing practices (such as higher utilization of surgery in early LCNEC in Europe or the integration of newer therapies like immunotherapy in the modern era) would be invaluable to confirm that the lack of intrinsic survival difference holds universally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical implications\u003c/h2\u003e \u003cp\u003eFrom a clinical perspective, our study\u0026rsquo;s findings carry several actionable implications. Foremost, they highlight the importance of delivering guideline-concordant therapy to LCNEC patients. In practical terms, for localized disease this means combining surgery with adjuvant chemotherapy. Prior research has demonstrated that LCNEC patients who undergo resection benefit significantly from chemotherapy \u0026ndash; one analysis showed that surgery plus chemotherapy yielded the best survival for stage I\u0026ndash;III LCNEC, whereas surgery alone was associated with much poorer outcomes [18]. Unfortunately, many LCNEC patients in the past did not receive postoperative chemo, perhaps due to advanced age or lack of recognition of the tumor\u0026rsquo;s aggressiveness. Going forward, clinicians should consider even stage I LCNEC as carrying a high risk of micrometastatic disease (akin to SCLC) and strongly consider adjuvant platinum-based chemotherapy in fit patients. For advanced-stage LCNEC, our findings underscore that platinum-etoposide (the SCLC regimen) should be the default choice in the absence of contraindications, given the accumulating evidence of its superiority​ [15]. We also suggest that practices used in SCLC might be extrapolated to LCNEC: for instance, the consideration of prophylactic cranial irradiation in patients with LCNEC who have a good response to initial therapy could be an area for future investigation, as brain metastases are common in both diseases. Additionally, enrollment of LCNEC patients in clinical trials is crucial. To date, there are no dedicated standard treatment guidelines for LCNEC, and management is based on limited data or borrowing from SCLC/NSCLC protocols​ [14]. This lack of standardized guidance has been explicitly acknowledged in the literature: LCNEC treatment remains \u0026ldquo;on debate\u0026rdquo; and there is an unmet need for prospective studies​ [14]. Our results provide a strong rationale for treating LCNEC with SCLC-based multimodality regimens, and prospective trials could formally confirm this approach.\u003c/p\u003e \u003cp\u003eAnother implication relates to emerging therapies. The last two decades (2000\u0026ndash;2020) have seen modest improvements in SCLC outcomes with new treatments such as immune checkpoint inhibitors. The addition of atezolizumab or durvalumab to first-line chemotherapy has become a new standard in extensive-stage SCLC, improving 2-year survival rates​ [5]. LCNEC, which often exhibits a high tumor mutational burden, might similarly benefit from immunotherapy \u0026ndash; indeed, case series have reported some durable responses to PD-1/PD-L1 inhibitors in LCNEC​ [19]. Future research should explore the role of immunotherapy and other novel agents (e.g. DLL3-targeted therapies or PARP inhibitors being studied in SCLC) in LCNEC. Additionally, as comprehensive genomic profiling becomes more routine, stratifying LCNEC patients by molecular subtype in trials could determine if a precision medicine approach (treating \u0026ldquo;SCLC-like\u0026rdquo; LCNEC with SCLC protocols and \u0026ldquo;NSCLC-like\u0026rdquo; LCNEC with NSCLC protocols) improves outcomes further. Our finding that, on average, treatment equalization removes survival differences suggests that a one-size-fits-all SCLC-like approach is effective for most; but it\u0026rsquo;s possible that refining this by subtype could yield incremental benefits.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study provides strong evidence that LCNEC and SCLC have broadly comparable survival outcomes when patients are matched on key prognostic factors and receive appropriate treatment. The worse survival observed for LCNEC in naive comparisons appears to stem from imbalances in stage and, critically, in treatment delivery. When those are accounted for, LCNEC does not behave more aggressively than SCLC. These findings emphasize that clinicians should approach LCNEC with the same aggressive treatment strategies used for SCLC, in order to close the historical survival gap. They also reassure us that the classification of LCNEC as a high-grade neuroendocrine carcinoma alongside SCLC is appropriate from a prognostic standpoint. Finally, our work highlights the need for continued improvements in the care of LCNEC patients \u0026ndash; through adherence to multimodal therapy, development of consensus guidelines, and prospective research to optimize management. By addressing the treatment disparities that have existed, we may substantially improve outcomes for LCNEC, turning a previously dismal prognosis into one that, while still serious, is on par with other aggressive lung cancers rather than worse. Ultimately, the convergence of LCNEC and SCLC survival under equitable treatment is a hopeful message: it suggests that LCNEC\u0026rsquo;s fate is not preordained to be worse than SCLC, and that with proper therapy, LCNEC patients can achieve survival outcomes equivalent to those of SCLC patients. Future studies should build on this knowledge, exploring the best therapeutic approaches for LCNEC (including SCLC-based regimens, targeted therapies, and immunotherapies) and ensuring that all patients with this rare cancer have access to optimal care. Through such efforts, the current gap between evidence and practice in LCNEC management can be narrowed, if not closed, just as our data show the survival gap narrowing when treatment is not a limiting factor.​\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used de‑identified data from the publicly available SEER database and did not involve direct patient contact or the use of individually identifiable health information. Under the U.S. Common Rule, research using only publicly available, de‑identified data is exempt from institutional review board oversight; therefore, ethics approval and patient consent were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analyzed during the current study is available in the SEER repository: https://seer.cancer.gov.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH conceived the study, performed data extraction and statistical analyses, and drafted the manuscript. PS assisted with critical revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the SEER Program tumor registries and the National Cancer Institute for providing access to the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndrini E, Marchese PV, de Biase D, Mosconi C, Siepe G, Panzuto F, Ardizzoni A, Campana D, Lamberti G: \u003cstrong\u003eLarge Cell Neuroendocrine Carcinoma of the Lung: Current Understanding and Challenges\u003c/strong\u003e. \u003cem\u003eJournal of Clinical Medicine \u003c/em\u003e2022, \u003cstrong\u003e11\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eWang J, Ye L, Cai H, Jin M: \u003cstrong\u003eComparative study of large cell neuroendocrine carcinoma and small cell lung carcinoma in high-grade neuroendocrine tumors of the lung: a large population-based study\u003c/strong\u003e. \u003cem\u003eJournal of Cancer \u003c/em\u003e2019, \u003cstrong\u003e10\u003c/strong\u003e:4226 - 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While SCLC has well-established treatment protocols, LCNEC remains poorly defined in clinical guidelines, leading to variability in management. Prior studies comparing their survival outcomes have yielded conflicting results, often limited by inadequate adjustment for confounders and lack of time-dependent modeling.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eUsing the SEER database (2000\u0026ndash;2021), we conducted a retrospective cohort study of 26,930 patients with histologically confirmed SCLC or LCNEC. Patients with stage IV disease or incomplete clinical data were excluded. Propensity score matching (PSM) was performed 1:1 based on age, sex, race, tumor stage (T/N), treatment modalities (chemotherapy, surgery, radiotherapy), and year of diagnosis. Survival was analyzed using Kaplan-Meier curves, Cox proportional hazards models, and time-dependent Cox regression incorporating histology*time interaction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eIn the unmatched cohort, LCNEC was associated with significantly worse overall survival (OS) compared to SCLC (HR\u0026thinsp;=\u0026thinsp;1.31; 95% CI, 1.23\u0026ndash;1.39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). After PSM (n\u0026thinsp;=\u0026thinsp;1898 per group), survival curves remained separated in Kaplan-Meier analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). However, in the adjusted Cox model, LCNEC became associated with better OS (HR\u0026thinsp;=\u0026thinsp;0.82; 95% CI, 0.73\u0026ndash;0.93; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0024). Time-dependent Cox analysis revealed a significant cancer type x time interaction (HRinteraction\u0026thinsp;=\u0026thinsp;0.74; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the survival gap narrowed over time. Compared to patients who did not receive chemotherapy, chemotherapy was associated with improved OS (HR\u0026thinsp;=\u0026thinsp;0.70); compared to no surgery, surgery was associated with improved OS (HR\u0026thinsp;=\u0026thinsp;0.36); and compared to no radiotherapy, radiotherapy was associated with improved OS (HR\u0026thinsp;=\u0026thinsp;0.62).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe observed survival disadvantage of LCNEC in unadjusted analysis was largely driven by differences in stage and treatment. After rigorous adjustment and matching, LCNEC exhibited survival outcomes comparable to SCLC. These findings support managing LCNEC with SCLC-based treatment protocols and suggest that treatment disparities\u0026mdash;not intrinsic tumor biology\u0026mdash;are the primary drivers of prognosis.\u003c/p\u003e","manuscriptTitle":"Unmasking the Survival Disparity Between Large-Cell Neuroendocrine Carcinoma and Small-Cell Lung Cancer: A SEER Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 05:43:26","doi":"10.21203/rs.3.rs-6514503/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":"d77a0ba5-2f67-464b-b205-d3642a9eaaf7","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47585441,"name":"Oncology"}],"tags":[],"updatedAt":"2025-05-05T05:43:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 05:43:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6514503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6514503","identity":"rs-6514503","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}