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Background Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is a rare and highly invasive subtype of lung cancer that accounts for fewer than 3% of cases. The prognostic factors for pulmonary LCNEC are unclear in the literature. Methods Patients diagnosed with pulmonary LCNEC between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The CumIncidence function was used for the univariate analysis. Multivariate analysis was performed using Cox regression analysis, subdistribution hazard function analysis, and cause-specific hazard function analysis. Results We finally screened 1246 patients diagnosed with pulmonary LCNEC, among whom 796 died of LCNEC and 141 died from other causes. The univariate analysis showed that sex, primary site, laterality, American Joint Committee on Cancer (AJCC) stage, T stage, N stage, M stage, lymph-node status, surgery, and chemotherapy were significant prognostic factors for pulmonary LCNEC (P<0.05). The multivariate analysis demonstrated that sex, AJCC stage, TNM stage T4, TNM stage N3, lymph-node status, surgery, and chemotherapy were independent risk factors for the prognosis (P<0.05). Conclusion We have conducted a competing-risks analysis of patients with pulmonary LCNEC in the SEER database. The results showed that sex, AJCC stage, TNM stage T4, TNM stage N3, lymph-node status, surgery, and chemotherapy are independent prognostic factors for pulmonary LCNEC patients. The reported data represent reference information that can be used for accurate assessments of the prognosis of pulmonary LCNEC patients.

Background Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is a rare primary malignant tumor with a poor prognosis, and surgery is the main treatment. However, there are no effective predictive tools to assess the prognosis of postoperative patients. Our aim is to identify prognostic factors and construct nomogram to accurately assess prognosis. Methods Patients were identified in the Surveillance, Epidemiology, and End Results (SEER) database. Based on the results of Cox regression analysis, construct nomogram for predicting 1-, 3-, and 5-year survival. The predictive performance of nomogram was evaluated using the consistency index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration plots. Results We finally screened 903 patients with pulmonary LCNEC who underwent surgery. The Cox regression analysis showed that age, SEER stage, T stage, N stage, M stage, tumor size, and chemotherapy were independent prognostic factors for overall survival (P<0.05). The C-index of the nomogram is 0.681 on the training cohort and 0.675 on the validation cohort. The AUC and calibration plots show that the nomogram has good performance. Conclusion We constructed and validated nomogram for predicting 1-, 3-, and 5-year survival of patients with pulmonary LCNEC after surgery. Our nomogram provides reference information for assessing the overall survival of these patients.

Background: Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is a rare primary malignant tumor with a poor prognosis. Our aim was to determine the prognostic factors of patients with pulmonary LCNEC after surgery based on competing risk model. Methods: Patients were identified in the Surveillance, Epidemiology, and End Results (SEER) database. For single outcome events, Kaplan-Meier method and Cox proportional risk model were used for analysis. Competing risk model was used in the analysis of multiple outcome events to adjust potential confounding factors. Competing risk model was used to calculate the cumulative incidence function of LCNEC-specific death. The Fine-Gray model was used for multivariate analysis to determine independent prognostic factors.Results: We finally screened 614 patients with pulmonary LCNEC who underwent surgery. The univariate analysis showed that T stage, N stage, M stage, regional nodes positive (RNP), and radiotherapy were significantly associated with the cumulative incidence of LCSD (P < 0.05). The Fine-Gray model showed that age, T stage, M stage, RNP, radiotherapy, and chemotherapy were independent prognostic factors for patients with pulmonary LCNEC after surgery (P < 0.05). Conclusion: Based on the competing risk model, we estimated a more accurate cumulative incidence of LCNEC-specific death and prognostic factors for patients with pulmonary LCNEC after surgery.