Prognostic nomograms for overall and cancer-specific survival in individuals with large cell neuroendocrine carcinoma: a population-based study
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Key findings
• A novel nomogram integrating sex, radiotherapy, surgery, T stage, N stage, and brain metastasis status was developed and validated for individualized prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with large cell neuroendocrine carcinoma (LCNEC). The model demonstrated strong predictive performance.
What is known and what is new?
• LCNEC is a rare, aggressive lung cancer with a poor prognosis, yet tools for personalized survival prediction are lacking.
• This study provides the first prognostic nomogram for both OS and CSS in LCNEC, validated in a population-based cohort. Decision curve analysis confirms its clinical utility for risk stratification.
What is the implication, and what should change now?
• This nomogram offers a practical tool for personalized prognosis and risk assessment. It should be integrated into clinical practice to guide patient counseling and individualized treatment planning, pending further external validation.
Introduction
Large cell neuroendocrine carcinoma (LCNEC) is a heterogeneous neoplasm arising from neuroendocrine cells within the pulmonary and bronchial epithelium, characterized by poorly differentiated or undifferentiated neuroendocrine morphology. This histological subtype constitutes approximately 3% of all primary lung malignancies. Initially classified in 2004 by the World Health Organization (WHO) as a subtype of large cell lung carcinoma, LCNEC was reclassified in 2015 as part of the pulmonary neuroendocrine tumor spectrum, which also includes typical carcinoid, atypical carcinoid, and small cell lung carcinoma (Grade 1–3) (1-3).
Over the past decade, the incidence of LCNEC has shown a gradual increase. Given its aggressive clinical course and unfavorable prognosis, timely diagnosis and intervention remain significant clinical challenges (4-6).
The Surveillance, Epidemiology, and End Results (SEER) program of the U.S. National Cancer Institute has provided population-based oncologic data since 1973. From 2004 onward, this database has included expanded clinical variables, enhancing its applicability to research involving rare tumor types (7-9).
Nomograms are graphical prediction models that incorporate multiple prognostic variables. They have been widely adopted in oncology for estimating individualized survival probabilities with improved accuracy compared to conventional staging systems (10-12). Despite their utility, there remains a paucity of research focused on the development of nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in individuals with LCNEC.
The objective of this study is to identify independent prognostic factors influencing OS and CSS in individuals with LCNEC using data derived from the SEER database. Based on these findings, prognostic nomograms were constructed to support clinical decision-making and to enhance individualized survival prediction in this population. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1742/rc).
Methods
Patient selection
Individuals diagnosed with LCNEC between 2010 and 2015 were initially identified from the SEER database (https://seer.cancer.gov/data/), in which all deposited cases came from the United States, using SEER*Stat 8.4.1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.
The inclusion criteria were as follows: (I) primary tumor site coded as “lung and bronchus”; (II) year of diagnosis between 2010 and 2015; (III) the International Classification of Diseases (ICD) code O-3 morphology 8013.
The exclusion criteria were as follows: records with incomplete clinical or survival information were excluded.
Cohort definition and variable recode
The patients were divided into the training and validation cohorts with a ratio of 7:3 using the R function “create Data Partition” to ensure that outcome events were distributed randomly between the two cohorts. The training cohort was used to screen variables and construct the model. The validation cohort was used to validate the results obtained using the training cohort. Extracted variables included demographic data (age at diagnosis, sex, race), year of diagnosis, tumor characteristics (primary site, T stage, N stage, histologic grade), treatment modalities (surgery, chemotherapy, radiotherapy), presence of distant metastases (bone, brain, liver), and survival outcomes.
Statistical analysis
The primary endpoints were OS and CSS. OS was defined as the time from diagnosis to death from any cause, with the last follow-up date serving as the endpoint for cases with censored data. CSS was defined as the time from diagnosis to death attributed specifically to LCNEC. Prognostic factors associated with OS and CSS in the training set were initially identified using univariate Cox proportional hazards regression analysis. Variables with a P value <0.05 were included in the multivariate Cox regression analysis to identify independent prognostic factors. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated for both univariate and multivariate analyses.
Nomograms for predicting 3- and 5-year OS and CSS were subsequently developed based on the identified independent prognostic factors. Predictive performance and model discrimination were evaluated using receiver operating characteristic (ROC) curve analysis, with corresponding area under the curve (AUC) values calculated. Calibration curves were constructed to assess the consistency between predicted and observed outcomes. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomograms across different probability threshold ranges. C-index and AUC values vary from 0.5 to 1.0, where 0.5 represents random chance and 1.0 indicates a perfect fit. Typically, C-index and AUC values greater than 0.7 suggest a reasonable estimation.
Schoenfeld residuals were used to verify the proportional hazards assumption, assess multicollinearity, and avoid unnecessary dichotomization of continuous variables by applying restricted cubic splines. Meanwhile, the variance inflation factor (VIF) was assessed among the covariates in the nomogram, and VIF >4.0 was interpreted as indicating multicollinearity.
Baseline characteristics between the training cohort and validation cohort were analyzed by using the Chi-squared test. All statistical analyses were performed using R software (version 4.3.0) with the following packages: “rms”, “Table1”, “ggDCA”, “survival ROC”, and “survival”.
Table 1
| Characteristic | Training cohort, n=429 | Validation cohort, n=184 | Overall, n=613 | P value |
|---|---|---|---|---|
| Sex | 0.30 | |||
| Female | 196 (45.7) | 75 (40.8) | 271 (44.2) | |
| Male | 233 (54.3) | 109 (59.2) | 342 (55.8) | |
| Age, years | 0.10 | |||
| <60 | 115 (26.8) | 62 (33.7) | 177 (28.9) | |
| ≥60 | 314 (73.2) | 122 (66.3) | 436 (71.1) | |
| Race | 0.89 | |||
| Black | 48 (11.2) | 23 (12.5) | 71 (11.6) | |
| Other | 15 (3.5) | 6 (3.3) | 21 (3.4) | |
| White | 366 (85.3) | 155 (84.2) | 521 (85.0) | |
| Radiotherapy | 0.74 | |||
| None/unknown | 265 (61.8) | 117 (63.6) | 382 (62.3) | |
| Yes | 164 (38.2) | 67 (36.4) | 231 (37.7) | |
| Surgery | 0.81 | |||
| No | 216 (50.3) | 90 (48.9) | 306 (49.9) | |
| Yes | 213 (49.7) | 94 (51.1) | 307 (50.1) | |
| Chemotherapy | 0.69 | |||
| No/unknown | 189 (44.1) | 85 (46.2) | 274 (44.7) | |
| Yes | 240 (55.9) | 99 (53.8) | 339 (55.3) | |
| Primary site | 0.86 | |||
| Lower lobe | 106 (24.7) | 43 (23.4) | 149 (24.3) | |
| Main bronchus | 22 (5.1) | 8 (4.3) | 30 (4.9) | |
| Middle lobe | 18 (4.2) | 5 (2.7) | 23 (3.8) | |
| Other | 33 (7.7) | 14 (7.6) | 47 (7.7) | |
| Upper lobe | 250 (58.3) | 114 (62) | 364 (59.4) | |
| T stage | 0.13 | |||
| T1 | 115 (26.8) | 41 (22.3) | 156 (25.4) | |
| T2 | 136 (31.7) | 74 (40.2) | 210 (34.3) | |
| T3 | 88 (20.5) | 40 (21.7) | 128 (20.9) | |
| T4 | 90 (21.0) | 29 (15.8) | 119 (19.4) | |
| N stage | 0.50 | |||
| N0 | 219 (51.0) | 89 (48.4) | 308 (50.2) | |
| N1 | 51 (11.9) | 29 (15.8) | 80 (13.1) | |
| N2 | 117 (27.3) | 52 (28.3) | 169 (27.6) | |
| N3 | 42 (9.8) | 14 (7.6) | 56 (9.1) | |
| Bone Mets | 0.11 | |||
| No | 385 (89.7) | 156 (84.8) | 541 (88.3) | |
| Yes | 44 (10.3) | 28 (15.2) | 72 (11.7) | |
| Brain Mets | 0.69 | |||
| No | 371 (86.5) | 162 (88.0) | 533 (86.9) | |
| Yes | 58 (13.5) | 22 (12.0) | 80 (13.1) | |
| Liver Mets | 0.96 | |||
| No | 387 (90.3) | 165 (89.7) | 552 (90.0) | |
| Yes | 42 (9.8) | 19 (10.3) | 61 (10.0) | |
| Grade | 0.20 | |||
| I–II | 14 (3.3) | 2 (1.1) | 16 (2.6) | |
| III–IV | 415 (96.7) | 182 (98.9) | 597 (97.2) |
Data are presented as cases (%). LCNEC, large cell neuroendocrine carcinoma; N, node; T, tumor.
Results
Characteristics of patients and disease
The final cohort included 613 patients with histologically confirmed LCNEC, of whom 429 were allocated to the training set and 184 to the validation set by a ratio of 7:3 (Figure 1). The demographic and clinical characteristics of these LCNEC patients are summarized in Table 1. The majority of patients were male (55.8%), with most having not received radiotherapy or having an unknown radiotherapy status (62.3%). Approximately half of the patients (50.1%) underwent surgical treatment. The training and validation cohorts were comparable in terms of demographic and clinical characteristics (P>0.05).
Cox regression analysis for OS and CSS
In the univariate Cox regression analysis, all variables except age, race, chemotherapy, and tumor grade were statistically significantly associated with both OS and CSS (P<0.05; Table 2). These variables were subsequently included in the multivariate Cox regression analysis (Table 3). The final independent prognostic factors associated with both OS and CSS included sex, radiotherapy, surgical treatment, T stage, N stage, and brain metastasis. The VIF values were all <4, indicating that no collinearity existed between screened variables.
Table 2
| Variable | OS | CSS | |||
|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | ||
| Sex | |||||
| Female | – | – | – | – | |
| Male | 1.33 (1.01–1.65) | <0.01 | 1.40 (1.11–1.76) | <0.01 | |
| Age, years | |||||
| <60 | – | – | – | – | |
| ≥60 | 1.2 (0.98–1.60) | 0.07 | 1.26 (0.97–1.62) | 0.08 | |
| Race group | |||||
| Black | – | – | – | – | |
| Other | 1.09 (0.59–2.02) | 0.77 | 1.17 (0.62–2.21) | 0.62 | |
| White | 0.96 (0.68–1.33) | 0.79 | 0.96 (0.68–1.35) | 0.8 | |
| Radiotherapy | |||||
| None/unknown | – | – | – | – | |
| Yes | 1.44 (1.16–1.79) | <0.01 | 1.47 (1.17–1.85) | <0.01 | |
| Surgery | |||||
| No | – | – | – | – | |
| Yes | 0.26 (0.21–0.33) | <0.01 | 0.24 (0.18–0.31) | <0.01 | |
| Chemotherapy | |||||
| No/unknown | – | – | – | – | |
| Yes | 1.11 (0.90–1.38) | 0.32 | 1.14 (0.89–1.44) | 0.3 | |
| Primary site | |||||
| Lower lobe | – | – | – | – | |
| Main bronchus | 2.68 (1.66–4.28) | <0.01 | 2.64 (1.58–4.41) | <0.01 | |
| Middle lobe | 0.85 (0.48–1.49) | 0.56 | 0.92 (0.52–1.63) | 0.78 | |
| Other | 1.82 (1.21–2.74) | <0.01 | 1.98 (1.28–3.05) | <0.01 | |
| Upper lobe | 0.77 (0.59–0.99) | 0.04 | 0.81 (0.61–1.07) | 0.14 | |
| T stage | |||||
| T1 | – | – | – | – | |
| T2 | 1.41 (1.05–1.90) | 0.02 | 1.59 (1.13–2.23) | <0.01 | |
| T3 | 2.44 (1.78–3.33) | <0.01 | 2.77 (1.94–3.96) | <0.01 | |
| T4 | 3.38 (2.47–4.62) | <0.01 | 3.69 (2.75–4.95) | <0.01 | |
| N stage | |||||
| N0 | – | – | – | – | |
| N1 | 2.19 (1.57–3.05) | <0.01 | 2.23 (1.56–3.18) | <0.01 | |
| N2 | 2.62 (2.04–3.36) | <0.01 | 2.96 (2.25–3.89) | <0.01 | |
| N3 | 3.26 (2.29–4.65) | <0.01 | 3.48 (2.41–5.04) | <0.01 | |
| Grade | |||||
| I-II | – | – | – | – | |
| III-IV | 1.61 (0.83–3.11) | 0.16 | 1.59 (0.82–3.09) | 0.17 | |
| Bone Mets | |||||
| No | – | – | – | – | |
| Yes | 2.94 (2.12–4.07) | <0.01 | 2.87 (2.06–4.02) | <0.01 | |
| Brain Mets | |||||
| No | – | – | – | – | |
| Yes | 2.62 (1.96–3.51) | <0.01 | 2.58 (1.92–3.48) | <0.01 | |
| Liver Mets | |||||
| No | – | – | – | – | |
| Yes | 3.23 (2.31–4.51) | <0.01 | 3.23 (2.28–4.56) | <0.01 | |
CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; LCNEC, large cell neuroendocrine carcinoma; N, node; OS, overall survival; T, tumor.
Table 3
| Variable | OS | CSS | |||
|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | ||
| Sex | |||||
| Female | – | – | – | – | |
| Male | 1.37 (1.09–1.71) | <0.01 | 1.3 (1.09–1.78) | <0.01 | |
| Radiotherapy | |||||
| None/unknown | – | – | – | – | |
| Yes | 0.61 (0.47–0.79) | <0.01 | 0.6 (0.47–0.82) | <0.01 | |
| Surgery | |||||
| No | – | – | – | – | |
| Yes | 0.34 (0.25–0.46) | <0.01 | 0.3 (0.24–0.46) | <0.01 | |
| Primary site | |||||
| Lower lobe | – | – | – | – | |
| Main bronchus | 1.12 (0.65–1.92) | 0.69 | 1.0 (0.57–1.83) | 0.94 | |
| Middle lobe | 0.76 (0.43–1.36) | 0.36 | 0.8 (0.46–1.52) | 0.56 | |
| Other | 1.24 (0.78–1.97) | 0.36 | 1.2 (0.79–2.10) | 0.31 | |
| Upper lobe | 0.94 (0.73–1.23) | 0.67 | 1.01 (0.76–1.35) | 0.94 | |
| T stage | |||||
| T1 | – | – | – | – | |
| T2 | 1.17 (0.86–1.60) | 0.31 | 1.29 (0.90–1.84) | 0.17 | |
| T3 | 1.56 (1.12–2.19) | <0.01 | 1.61 (1.10–2.36) | 0.02 | |
| T4 | 1.66 (1.16–2.40) | <0.01 | 1.75 (1.17–2.61) | <0.01 | |
| N stage | |||||
| N0 | – | – | – | – | |
| N1 | 1.49 (1.04–2.15) | 0.03 | 1.56 (1.05–2.31) | 0.03 | |
| N2 | 1.61 (1.21–2.13) | <0.01 | 1.73 (1.27–2.36) | <0.01 | |
| N3 | 1.39 (0.92–2.11) | 0.12 | 1.51 (0.98–2.33) | 0.06 | |
| Bone Mets | |||||
| No | – | – | – | – | |
| Yes | 1.31 (0.91–1.88) | 0.15 | 1.32 (0.91–1.91) | 0.14 | |
| Brain Mets | |||||
| No | – | – | – | – | |
| Yes | 2.10 (1.45–3.05) | <0.01 | 2.09 (1.42–2.09) | <0.01 | |
| Liver Mets | |||||
| No | – | – | – | – | |
| Yes | 1.23 (0.81–1.86) | 0.33 | 1.35 (0.87–2.07) | 0.18 | |
CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; LCNEC, large cell neuroendocrine carcinoma; N, node; OS, overall survival; T, tumor.
We conducted a Fine-Gray competing-risk regression for CSS, treating non-cancer deaths as competing events. The subdistribution hazard ratios (sHRs) for all covariates are summarized in Table S1. Key findings include: male sex (sHR 1.38), radiotherapy (sHR 0.65), surgery (sHR 0.36), higher T and N stages, and brain metastases (sHR 2.02) significantly affecting CSS. These results are consistent with the Cox regression analysis, confirming the robustness of the study conclusions.
Development of nomograms
Nomograms were constructed incorporating the six independent prognostic factors sex, radiotherapy, surgery, T stage, N stage, and brain metastasis to predict 3-year and 5-year OS and CSS (Figure 2). Each variable was assigned a point value; the total score was then mapped to an estimated survival probability using a projection to the survival probability axis.
Among all variables, surgical intervention and the presence of brain metastasis exhibited the greatest influence on predicted survival outcomes in both nomograms.
Nomogram validation
Model performance was evaluated using multiple validation methods. Internal validation using 1000-bootstrap replicates yielded a bias-corrected C-index of 0.732 (95% CI: 0.709–0.758) and a calibration slope of 0.946 (95% CI: 0.939–0.960). We evaluated the prediction accuracy of our Cox model using the Integrated Brier Score (IBS) over 0–119 months. The Cox model achieved an IBS of 0.129, compared with 0.173 for the reference model, indicating good predictive performance. In the training set, ROC curve analysis demonstrated favorable discrimination, with AUCs of 0.838 and 0.845 for 3- and 5-year OS, respectively, and 0.838 and 0.880 for 3- and 5-year CSS (Figure 3). All time-dependent AUC valued exceed 0.7, indicating that the nomogram possessed strong discriminatory capability.
The calibration curves of the nomogram indicated high concordance between predicted and observed survival probability in both the training and validation cohorts (Figure 4). Additionally, DCA confirmed the clinical utility of the nomograms, showing net benefit across a broad range of threshold probabilities at both time points (Figure 5).
Discussion
LCNEC remains a relatively rare malignancy, and most published studies are limited by small sample sizes, contributing to ongoing uncertainty regarding its prognostic factors. Recent advances in diagnostic imaging, therapeutic interventions, and surgical techniques have contributed to incremental improvements in survival outcomes among individuals diagnosed with LCNEC. Nonetheless, the low incidence continues to impede comprehensive investigations into its optimal management and prognostic factors (13,14).
To address this gap, the current study utilized data from the SEER database to analyze a cohort of 613 individuals diagnosed with LCNEC between 2010 and 2015. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent prognostic variables associated with OS and CSS. Based on these findings, nomograms were developed incorporating six independent prognostic factors: sex, receipt of radiotherapy, surgical intervention, T stage, N stage, and presence of brain metastases. These prognostic tools may assist in clinical decision-making and facilitate future research.
Sex-based differences in the incidence and outcomes of lung malignancies have been previously reported, with a higher prevalence observed among males, potentially attributable to greater tobacco exposure and the proliferative effects of androgens and their receptors on neoplastic cells (15-17). Consistent with prior studies, male sex was identified as an independent adverse prognostic factor for both OS and CSS in the current study.
Radiotherapy has demonstrated survival benefits in individuals with LCNEC, particularly among those with brain metastases (18). In the present analysis, radiotherapy was independently associated with improved OS and CSS, supporting its beneficial role in the multidisciplinary management of LCNEC.
Due to the rarity of LCNEC, standardized treatment guidelines remain undefined, and a consensus on optimal therapy is lacking. Surgical resection continues to serve as a cornerstone of curative-intent treatment for individuals with resectable disease, although a substantial risk of postoperative recurrence persists. Despite this, surgical intervention is recommended when feasible. For individuals with locally advanced or metastatic disease particularly those with osseous or cerebral involvement no consensus exists regarding optimal management strategies (18-20). In the present cohort, surgical resection was associated with improved OS and CSS, corroborating findings from previous investigations.
Disease progression in neuroendocrine tumors generally follows a gradual trajectory, with more advanced T and N stages correlating with poorer survival outcomes. Increasing T and N stages reflect larger tumor burden, higher mitotic activity, greater local invasion, and elevated risk of hematogenous dissemination (21-23). In this study, advanced T stage was identified as an independent adverse prognostic factor, while higher N stage was associated with reduced 3- and 5-year OS and CSS, potentially reflecting a propensity for lymphatic spread
Brain metastases are a common complication in individuals with LCNEC, with one study reporting a cumulative incidence of 32.2% over the disease course, and rates of 25.0% and 48.4% at one and two years postoperatively, respectively (24,25). In the current study, the presence of brain metastases was associated with significantly reduced OS and CSS, suggesting a more aggressive tumor phenotype and an overall poorer prognosis.
In summary, this study provides a comprehensive evaluation of clinical characteristics, therapeutic interventions, and survival outcomes in LCNEC using a large, population-based dataset. Prognostic nomograms for OS and CSS were developed based on key independent factors, offering a practical tool for personalized risk assessment and potential clinical application.
Several limitations of our study should be acknowledged. First, as SEER is a retrospective registry, selection bias may exist due to differences in institutional practices and reporting patterns. Second, immortal-time bias is possible, since patients must survive long enough to receive certain treatments, potentially affecting survival estimates. Third, treatment variables such as surgery and radiotherapy may be subject to confounding by indication, as they are influenced by patient condition or tumor characteristics. Furthermore, the SEER database lacks detailed molecular profiling, systemic therapy information, and recurrence data, which limits comprehensive risk prediction and the generalizability of our nomogram. Despite these limitations, our model provides a useful tool for individualized prognostication, but results should be interpreted with caution and validated in external cohorts.
Conclusions
Six independent prognostic factors-sex, radiotherapy, surgical intervention, T stage, N stage, and presence of brain metastases-were identified for individuals diagnosed with LCNEC and subsequently incorporated into the nomogram models developed in this study. Both internal and external validation procedures demonstrated that these nomograms possess robust predictive accuracy for estimating 3-year and 5-year OS and CSS, thereby offering valuable support for evidence-based clinical decision-making.
Acknowledgments
We would like to acknowledge the hard and dedicated work of all the staff that implemented the intervention and evaluation components of the study.
Footnote
Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1742/rc
Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1742/prf
Funding: None.
Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1742/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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