Examining the effectiveness of follow-up chemotherapy in large cell neuroendocrine carcinoma: special emphasis on stage T1–2N0M0 according to 9^th edition TNM guidelines

Introduction

In the recent World Health Organization Lung Tumor Classification (5^th edition, 2021), pulmonary neuroendocrine tumors are categorized into four types (1). Among these, large cell neuroendocrine carcinomas (LCNECs) are aggressive cancers with a rising incidence (1,2). Treatment for LCNEC is often extrapolated from therapies for other lung cancers due to limited dedicated studies. Furthermore, due to the rarity of LCNEC, most studies suffer from a small sample size, leading to a lack of statistical significance (3-7). Most research on LCNEC still relies on the 7th edition or even older versions of staging, leading to a lack of studies focused on treatment options based on the 9th edition of staging (3,4,7-9). Surgery is commonly used for early-stage LCNEC, and combined treatments (surgery with chemotherapy and/or radiotherapy) have shown better outcomes (10). The American Society of Clinical Oncology suggests platinum-based chemotherapy combined with etoposide or regimens for non-squamous cell carcinoma (11).

However, most studies rely on outdated cancer staging systems, and the 9^th edition of the American Joint Committee on Cancer (AJCC) has introduced new classifications (12). In the 9^th edition of the AJCC staging system, the criteria for T2 stage tumors have been broadened, lowering the size threshold from 7 to 5 cm. Now, tumors with local extension are categorized as T2. Local expansion refers to situations such as atelectasis or obstructive pneumonitis extending into the hilar zone, impacting either a segment or the full lung. It additionally encompasses neoplasms that penetrate the visceral pleura and those affecting the principal bronchus, irrespective of their proximity to the carina, provided that the carina is not directly involved (13). This study aims to utilize the most recent AJCC guidelines and the Surveillance, Epidemiology, and End Results (SEER) data repository for examining the effects of adjuvant chemotherapy in cases of LCNEC staged as T1–2N0M0. The goal is to provide fresh insights into optimal treatment strategies for these cases, considering the updated staging and contemporary perspective. We present this article in accordance with the STROBE reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1642/rc).

Methods

Database access and criteria for patient inclusion

Employing the SEER*Stat application, version 8.4.2, we chose individuals identified with LCNEC in the data pool of the National Cancer Institute’s SEER program (https://seer.cancer.gov/), covering the period from 2004 to December 2015. The identification of LCNEC was in line with the International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3)/World Health Organization 2008 guidelines, where “lung and bronchus” served as the primary tumor site. Subsequently, based on the histologic type (ICD-O-3: 8013/3), we narrowed down to patients labeled as “LCNEC” and extracted their corresponding raw data. To refine our cohort, we adhered to the following criteria. Inclusion: (I) underwent surgical resection; (II) tumor diameter ≤5 cm; (III) no lymph node or distant metastasis; (IV) LCNEC as the first primary tumor; and (V) availability of comprehensive follow-up data. Exclusion: (I) prior radiotherapy; (II) absence of specific clinical or pathological details like gender, age, race, marital status, histology, tumor location, laterality, tumor size, local extension, surgical technique, or chemotherapy status; (III) categorization as TX or T3–4; and (IV) demise within a month of diagnosis. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Data points gathered

Patients were categorized into two groups based on their chemotherapy status: those who received chemotherapy and those who did not. Using the 9^th edition AJCC staging, tumors with a diameter ≤3 cm and accompanied by local extension were designated as T2a stage. For each patient, we gathered essential demographic data (gender, age, race, and marital status) and detailed tumor characteristics (histology, location, laterality, differentiation, size, and local extension). Tumor locations were coded according to ICD-O-3 primary site codes, where “not otherwise specified (NOS)” indicates anatomical sites without further specification in the original pathology report, consistent with SEER cancer registry coding conventions. Treatment details, such as surgical approach, number of examined LNs, and chemotherapy status, were also recorded. Our evaluation of patient prognosis primarily focused on overall survival (OS) and cancer-specific survival (CSS). OS was determined from the date of diagnosis to either the date of death or the last recorded follow-up, while CSS was measured from the date of diagnosis to the date of death, specifically due to LCNEC or the last known follow-up.

Statistical analysis

Demographic and clinical data were summarized using descriptive statistics for both patient cohorts. We used the X-tile software to determine the optimal cutoff point for tumor size. Between the chemotherapy and observation groups, clinicopathological differences were assessed using the Chi-squared test or Fisher’s exact test as appropriate. To minimize baseline confounding, propensity score matching (PSM) was executed in a 1:1 ratio based on clinically relevant covariates (14), though variable selection was not guided by a formal directed acyclic graph (DAG) to distinguish confounders from mediators or colliders. Subgroup analyses were exploratory and not pre-specified based on biological hypotheses or causal reasoning. Given the risk of spurious associations from multiple comparisons and residual confounding in unmatched data, these analyses should be interpreted as hypothesis-generating observations requiring prospective validation. Survival differences in OS and CSS between groups were visualized using Kaplan-Meier (K-M) curves and evaluated by the log-rank test. Within the unmatched cohort, multivariate Cox regression analyses were employed to determine hazard ratios (HRs) for variables and to assess chemotherapy’s impact in subgroup analyses. All statistical analyses, including descriptive statistics, K-M curves, PSM, and Cox regression, were conducted using R software (version 4.2.1). Significance was set at a two-sided P value of less than 0.05.

Results

Features of patients and elements impacting treatment options

Between 2004 and 2015, a total of 1,062 patients were staged as N0M0. Of these, 582 patients meeting the inclusion criteria were selected for the study, among whom 109 underwent adjuvant chemotherapy. Detailed information about the treatment options is shown in Figure 1.

Figure 1 Diagram illustrating the criteria for participant inclusion in the research. LCNEC, large cell neuroendocrine carcinoma; M, metastasis; N, node; NOS, not otherwise specified; SEER, Surveillance, Epidemiology, and End Results; T, tumor.

Before applying PSM, notable disparities were evident between individuals who had post-surgical chemotherapy and those who only had surgical intervention, particularly in aspects like grade, age, tumor stage (T stage), and dimensions of the tumor. After PSM adjustment, no significant differences in clinical characteristics were observed between the two cohorts, achieving a well-balanced comparison. We found that compared to individuals who solely underwent surgery, patients with advanced T staging were more likely to undergo postoperative chemotherapy. Moreover, patients with higher histological grades, younger age, or larger tumor sizes were more likely to choose postoperative chemotherapy within the T1–2N0M0 cohort. All these observations were statistically significant with all P values less than 0.001. Table 1 displays the initial patient attributes both before and subsequent to the application of PSM.

OS analysis

The overall 5-year OS rate for the cohort was 51%, and the CSS rate was 63.3%. Before and after PSM, we analyzed OS and CSS in patients with T1–2N0M0 disease who either underwent adjuvant chemotherapy or surgery alone. Before PSM, the HR for OS was 1.29 [95% confidence interval (CI): 0.99–1.68; P=0.056] with 5-year OS rates of 59.5% for chemotherapy and 49.1% for surgery alone. The HR for CSS was 1.20 with 5-year CSS rates of 67.2% for chemotherapy and 62.3% for surgery alone. After PSM, the HR for OS was 1.07 (95% CI: 0.76–1.50; P=0.70) with 5-year OS rates of 59.5% for chemotherapy and 56.8% for surgery alone. The HR for CSS was 1.00 (95% CI: 0.65–1.55; P=0.99) with 5-year CSS rates of 67.2%for chemotherapy and 67.9% for surgery alone. No marked variations were noted in terms of OS or CSS (Figure 2A-2D).

Figure 2 K-M analyses for OS and CSS in various patient groups: (A) pre-PSM OS; (B) pre-PSM CSS; (C) post-PSM OS; (D) post-PSM CSS. CSS, cancer-specific survival; K-M, Kaplan-Meier; OS, overall survival; PSM, propensity score matching.

Understanding the significance of AJCC staging in prognosis and postoperative adjuvant chemotherapy, we conducted survival analyses separately for T1 and T2 stages. In patients with T1N0M0 disease, adjuvant chemotherapy post-surgery did not significantly affect OS (P=0.69) and CSS (P=0.80). However, in patients with T2N0M0 disease, although no significant difference was observed in CSS (P=0.08) between those who underwent adjuvant chemotherapy and those who had surgery alone, a significant difference was noted in OS (P=0.007) (Figure 3A-3D).

Figure 3 K-M analyses for OS and CSS at T1 and T2 in the unmatched cohort: (A) T1 OS; (B) T1 CSS; (C) T2 OS; (D) T2 CSS. CSS, cancer-specific survival; K-M, Kaplan-Meier; OS, overall survival; T, tumor.

Prognostic factors for OS

In a comprehensive analysis of T1–2N0M0 LCNEC patients, distinct protective and risk factors were identified. Univariate analysis revealed Asian or Pacific Islander (HR =0.197; 95% CI: 0.043–0.902; P=0.04) and Black ethnicity (HR =0.213; 95% CI: 0.051–0.888; P=0.03) as protective factors. In contrast, ’segmentectomy’ surgical method (HR =1.994; 95% CI: 1.277–3.116; P=0.002), being widowed (HR =1.727; 95% CI: 1.198–2.492; P=0.003), age ≥65 years (HR =2.114; 95% CI: 1.698–2.631; P=0.003), and zero regional nodes examined (HR =2.105; 95% CI: 1.559–2.843; P<0.001) emerged as significant risk factors. Subsequent multivariable regression analysis corroborated ‘middle’ (HR =1.765; 95% CI: 1.078–2.891; P=0.02) as risky tumor locations, along with ’segmentectomy’ (HR =1.877; 95% CI: 1.163–3.03; P=0.01), age ≥65 years (HR =2.002; 95% CI: 1.586–2.527; P<0.001), and zero regional nodes examined as persistent risk factors (HR =1.531; 95% CI: 1.035–2.267; P=0.03) (Figure 4).

Figure 4 The forest plot displays the results of the univariate regression analysis and multivariable regression analysis. CI, confidence interval; HR, hazard ratio; NOS, not otherwise specified; T, tumor.

Exploratory subgroup analysis of chemotherapy associations in the unmatched cohort

In our exploratory multivariable Cox model subgroup analysis of the unmatched cohort, we observed statistically significant associations between chemotherapy receipt and differential survival in specific patient subgroups. Specifically, tumors located at lower anatomical positions (HR =1.62; 95% CI: 1.03–2.54; P=0.04), being divorced (HR =2.64; 95% CI: 1.04–6.66; P=0.04), being aged 65 or older (HR =1.54; 95% CI: 1.05–2.25; P=0.03), having a T2a staging (HR =1.69; 95% CI: 1.1–2.58; P=0.02), having local extension (HR =2.15; 95% CI: 1.28–3.62; P=0.004) and having 4 to 7 regional lymph nodes examined (HR =2.03; 95% CI: 1.05–3.91; P=0.03) showed statistical associations with differential survival when stratified by treatment received (Figure 5). Given the exploratory nature of these unmatched analyses and the lack of biological plausibility for some associations (e.g., marital status, nodal examination counts), these findings should be interpreted cautiously as hypothesis-generating observations subject to residual confounding and multiple testing bias.

Figure 5 Forest plot shows HRs comparing the risk of death between postoperative chemotherapy and surgery alone across various subgroups, prior to PSM. An HR value exceeding 1 suggests elevated risk when opting for just surgical treatment, whereas an HR below 1 signifies reduced risk. CI, confidence interval; HR, hazard ratio; Inf, infinity; NOS, not otherwise specified; PSM, propensity score matching; T, tumor.

We further conducted survival analyses on patients aged ≥65 years and those with local extension. The results were consistent with our subgroup analysis. K-M analyses demonstrated statistically significant differences in both OS and CSS between treatment groups in these subsets (all P<0.05) (Figure 6), consistent with the Cox model subgroup findings.

Figure 6 K-M analyses for OS and CSS across various conditions: (A) OS in patients with local spread; (B) CSS in patients with local spread; (C) OS in patients 65 years and older; (D) CSS in patients 65 years and older. CSS, cancer-specific survival; K-M, Kaplan-Meier; OS, overall survival.

Since all patients with tumors smaller than 3 cm in diameter but having local extension were categorized into the T2a group, and both T2a and local extension groups showed statistical associations in the subgroup analysis, we conducted a survival analysis comparing patients in the T2 group with local extension and those solely categorized as T2 due to the original size of the tumor. We found that the OS and CSS of patients within the T2 group with local extension showed statistically significant differences in OS and CSS between treatment groups. However, for patients solely categorized into T2 due to the tumor’s primary size, the 5-year OS was 46.9% (95% CI: 32.4–67.8%) with chemotherapy and 42.9% (95% CI: 30.6–60.0%) without, while the CSS was 57.9% (95% CI: 42.8–78.5%) with chemotherapy and 55.1% (95% CI: 41.6–73.1%) without. No significant difference was observed between these groups (OS: P=0.50; CSS: P=0.79) (Figure 7).

Figure 7 K-M analyses for OS and CSS across various conditions: (A) OS in T2N0M0 patients with local spread; (B) CSS in T2N0M0 patients with local spread; (C) OS in T2N0M0 patients without local spread; (D) CSS in T2N0M0 patients without local spread. CSS, cancer-specific survival; K-M, Kaplan-Meier; M, metastasis; N, node; OS, overall survival; T, tumor.

Discussion

LCNEC is an uncommon yet highly aggressive variant of lung cancer, distinguished by the presence of sizable neuroendocrine cells (1). Its incidence is increasing, yet research specifically focused on LCNEC is limited (2). Consequently, therapeutic approaches for this specific cancer are frequently adapted from guidelines originally designed for different lung cancer subtypes like non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Nonetheless, the systemic toxicity associated with chemotherapeutic agents warrants careful consideration (15). Consequently, there exists an exigent need for evidence-based guidelines to inform the optimal timing for the initiation of chemotherapy.

To our knowledge, this represents the first study to systematically examine the role of adjuvant chemotherapy in T1–2N0M0 LCNEC patients using the SEER database in accordance with the 9^th edition AJCC staging system. Our primary finding, derived from propensity score-matched analysis, shows no significant association between adjuvant chemotherapy and survival benefit in the overall T1–2N0M0 LCNEC population. This negative result has important clinical implications, as it suggests that routine adjuvant chemotherapy may not be warranted for all early-stage, node-negative LCNEC patients, potentially sparing them from unnecessary treatment-related toxicity. While exploratory subgroup analyses suggested potential heterogeneity in treatment associations, these findings require cautious interpretation for methodological reasons discussed below.

Choosing a treatment regimen for LCNEC is challenging due to limited evidence from dedicated prospective trials. A study published in the European Respiratory Journal indicated substantial heterogeneity in chemotherapy response among stage IV LCNEC patients (16), highlighting the potential importance of patient selection and molecular profiling. Our findings extend this observation to early-stage disease, where the absence of OS benefit in unselected T1–2N0M0 patients underscores the need for refined patient selection criteria.

Recent studies have begun to advocate for treatment choices grounded in molecular subtypes. For instance, LCNEC patients with intact retinoblastoma 1 (RB1) expression have demonstrated more favorable outcomes when treated with NSCLC-gemcitabine (GEM)/taxane (TAX) regimens compared to SCLC-platinum-etoposide (PE) chemotherapy (17,18). Additionally, there is a lack of unified agreement concerning the most effective chemotherapy for LCNEC. The rarity of LCNEC poses considerable challenges for conducting large, prospective clinical investigations. Treatment results for this lung cancer subtype are notably limited in contrast to other histological varieties. Various research initiatives have explored the role of programmed death-ligand 1 (PD-L1) expression in LCNEC and its subsequent effect on prognosis (19,20). Data supporting the efficacy of immune checkpoint inhibitors is largely confined to isolated case studies (21-23). Case-based evidence also exists for the effectiveness of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in chemotherapy regimens (24,25).

Even with a constrained sample size, our research constitutes the most extensive evaluation so far concerning the role of post-surgical chemotherapy in T1–2N0M0 staged LCNEC, as per the 9^th edition AJCC staging guidelines. Diverging from many existing studies, our work is specifically tailored to LCNEC. We also took steps to reduce selection bias, ensuring that the cohort opting for only surgical intervention closely resembled the group receiving additional chemotherapy. In pursuit of this objective, we ruled out individuals who had undergone radiation treatment as well as those who passed away within the first month following their diagnosis.

Exploratory subgroup analyses in the unmatched cohort revealed heterogeneous associations between adjuvant chemotherapy and survival. Among these findings, differential survival patterns were most evident in T2N0M0 patients with local extension (visceral pleural invasion or bronchial involvement) compared to those classified as T2 based solely on tumor size. This observation has biological plausibility, as local extension may indicate more aggressive tumor biology with higher micrometastatic potential. However, other subgroup associations—particularly those involving age ≥65 years and marital status—lack biological plausibility and likely represent statistical artifacts rather than true treatment effect heterogeneity.

Several findings warrant particular scrutiny due to internal contradictions. Age ≥65 years emerged as both an adverse prognostic factor in the overall cohort and a statistical correlate of differential treatment-related survival in subgroup analysis—a biologically implausible paradox. Elderly patients typically have worse chemotherapy tolerance and outcomes; thus, apparent “benefit” likely reflects selection bias rather than true treatment effect. Specifically, elderly patients selected for chemotherapy likely possessed unmeasured favorable characteristics (excellent performance status, minimal comorbidities, strong social support) that independently predict survival. Similarly, associations with marital status and nodal examination counts have no mechanistic basis and likely represent chance findings from residual confounding and multiple testing. Without pre-specification of hypotheses and correction for multiple comparisons across 15–20 subgroup variables, spurious P<0.05 findings are statistically expected by chance alone (type I error). These observations underscore that subgroup findings should be viewed as exploratory associations requiring prospective validation rather than clinically actionable evidence.

Future randomized controlled trials should incorporate comprehensive clinical and molecular profiling currently lacking in SEER. Priority data elements include performance status, comorbidity assessment, detailed treatment information (regimens, doses, toxicity), recurrence endpoints, and molecular characterization [RB1, tumor protein p53 (TP53), PD-L1, EGFR] (26). Additionally, formal causal inference frameworks such as DAGs should be employed to prospectively specify confounders and effect modifiers, enabling rigorous assessment of treatment heterogeneity (27). Given the biological rationale observed in our exploratory analysis, T2N0M0 patients with local extension represent a particularly important population for prospective investigation. Until such validation occurs, these findings should not alter current treatment approaches.

This study has several important limitations. Most fundamentally, the absence of a DAG-based causal framework represents a critical methodological limitation (27). Without prospective specification of causal structure using a DAG, we cannot distinguish: (I) true confounders requiring adjustment; (II) mediators that should not be adjusted for; (III) colliders where adjustment introduces bias (e.g., lymph node examination counts may function as a collider reflecting both surgical extent and pathology practices); and (IV) biologically plausible effect modifiers versus spurious associations. Consequently, our observed subgroup associations (e.g., marital status, nodal examination counts) cannot be distinguished from chance findings arising from residual confounding, selection bias, or multiple testing without correction.

Additionally, the retrospective design and SEER data constraints fundamentally limit causal inference. SEER lacks performance status, comorbidity data, pulmonary function tests, detailed chemotherapy information (regimens, doses, completion rates, toxicity), and recurrence data—the latter being particularly problematic as patients receiving “adjuvant” therapy may have been treated for early progression. A critical limitation is the absence of molecular profiling data (RB1, TP53, PD-L1, EGFR). Emerging evidence suggests LCNEC comprises molecularly distinct subtypes with differential chemotherapy responses: RB1-intact tumors may benefit from NSCLC-type regimens, while RB1/TP53-altered tumors respond better to SCLC-type therapy (18). Without molecular stratification, we cannot distinguish whether observed treatment heterogeneity reflects true biological subgroups or unmeasured confounding. While our primary analysis employed PSM, variable selection for PSM was not guided by formal DAG specification, creating potential for inappropriate conditioning on mediators or colliders. Furthermore, subgroup analyses were conducted on unmatched cohorts due to sample size constraints, reintroducing confounding and increasing susceptibility to bias—a methodological challenge similarly encountered in other rare malignancies (28). Multiple subgroup analyses without pre-specification or correction for multiple comparisons increase type I error risk. Selection bias, particularly for elderly patients, where only the fittest receive chemotherapy, likely confounds treatment associations with baseline prognostic differences. Despite these limitations, this represents the largest analysis of adjuvant chemotherapy in T1–2N0M0 LCNEC using the 9^th edition AJCC staging, providing hypothesis-generating data to inform prospective trials.

Conclusions

In this largest analysis to date of T1–2N0M0 LCNEC using the 9^th edition AJCC staging system, propensity score-matched analysis demonstrated no significant survival benefit from adjuvant chemotherapy in the overall population. Exploratory subgroup analyses suggested potential associations with improved outcomes in patients aged ≥65 years and those with T2N0M0 disease with local extension; however, these findings are hypothesis-generating and subject to residual confounding from unmatched analyses. Given the retrospective design and inherent limitations of SEER data, prospective randomized controlled trials are necessary to definitively establish whether specific subgroups benefit from adjuvant chemotherapy. Until such evidence is available, treatment decisions for early-stage LCNEC should continue to rely on multidisciplinary assessment of individual patient characteristics and established clinical guidelines.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1642/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1642/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1642/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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