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Abstract：
OBJECTIVE: Metastatic pulmonary large cell neuroendocrine carcinoma (LCNEC) is an aggressive cancer with generally poor outcomes. Effective methods for predicting survival in patients with metastatic LCNEC are needed. This study aimed to identify independent survival predictors and develop nomograms for predicting survival in patients with metastatic LCNEC.
METHODS: We conducted a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database, identifying patients with metastatic LCNEC diagnosed between 2010 and 2017. To find independent predictors of cancer-specific survival (CSS), we performed Cox regression analysis. A nomogram was developed to predict the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC. The concordance index (C-index), area under the receiver operating characteristic (ROC) curves (AUC), and calibration curves were adopted with the aim of assessing whether the model can be discriminative and reliable. Decision curve analyses (DCAs) were used to assess the model\'s utility and benefits from a clinical perspective.
RESULTS: This study enrolled a total of 616 patients, of whom 432 were allocated to the training cohort and 184 to the validation cohort. Age, T staging, N staging, metastatic sites, radiotherapy, and chemotherapy were identified as independent prognostic factors for patients with metastatic LCNEC based on multivariable Cox regression analysis results. The nomogram showed strong performance with C-index values of 0.733 and 0.728 for the training and validation cohorts, respectively. ROC curves indicated good predictive performance of the model, with AUC values of 0.796, 0.735, and 0.736 for predicting the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC in the training cohort, and 0.795, 0.801, and 0.780 in the validation cohort, respectively. Calibration curves and DCAs confirmed the nomogram\'s reliability and clinical utility.
CONCLUSIONS: The new nomogram was developed for predicting CSS in patients with metastatic LCNEC, providing personalized risk evaluation and aiding clinical decision-making.
METHODS: We conducted a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database, identifying patients with metastatic LCNEC diagnosed between 2010 and 2017. To find independent predictors of cancer-specific survival (CSS), we performed Cox regression analysis. A nomogram was developed to predict the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC. The concordance index (C-index), area under the receiver operating characteristic (ROC) curves (AUC), and calibration curves were adopted with the aim of assessing whether the model can be discriminative and reliable. Decision curve analyses (DCAs) were used to assess the model\'s utility and benefits from a clinical perspective.
RESULTS: This study enrolled a total of 616 patients, of whom 432 were allocated to the training cohort and 184 to the validation cohort. Age, T staging, N staging, metastatic sites, radiotherapy, and chemotherapy were identified as independent prognostic factors for patients with metastatic LCNEC based on multivariable Cox regression analysis results. The nomogram showed strong performance with C-index values of 0.733 and 0.728 for the training and validation cohorts, respectively. ROC curves indicated good predictive performance of the model, with AUC values of 0.796, 0.735, and 0.736 for predicting the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC in the training cohort, and 0.795, 0.801, and 0.780 in the validation cohort, respectively. Calibration curves and DCAs confirmed the nomogram\'s reliability and clinical utility.
CONCLUSIONS: The new nomogram was developed for predicting CSS in patients with metastatic LCNEC, providing personalized risk evaluation and aiding clinical decision-making.
摘要：
目的：转移性肺大细胞神经内分泌癌（LCNEC）是一种侵袭性癌症，通常预后较差。需要预测转移性LCNEC患者生存的有效方法。这项研究旨在确定独立的生存预测因子，并开发用于预测转移性LCNEC患者生存的列线图。
方法：我们使用监测进行了回顾性分析，流行病学,和最终结果(SEER)数据库，确定2010年至2017年间诊断为转移性LCNEC的患者。为了找到癌症特异性生存率(CSS)的独立预测因子，我们进行了Cox回归分析.开发了一个列线图来预测6-，12-，转移性LCNEC患者的18个月CSS率。一致性指数(C指数)，接收器工作特征(ROC)曲线(AUC)下面积，并采用校正曲线评估模型是否具有判别性和可靠性。使用决策曲线分析（DCAs）从临床角度评估模型的实用性和益处。
结果：本研究共纳入616名患者，其中432人被分配到训练队列,184人被分配到验证队列.年龄,T分期,N分期，转移部位，放射治疗,根据多变量Cox回归分析结果确定化疗是转移性LCNEC患者的独立预后因素。列线图显示了训练和验证队列的C指数值为0.733和0.728的强劲表现，分别。ROC曲线表明模型具有良好的预测性能，AUC值为0.796、0.735和0.736，用于预测6-，12-，在训练队列中转移性LCNEC患者的18个月CSS率，验证队列中的0.795、0.801和0.780，分别。校准曲线和DCA证实了列线图的可靠性和临床实用性。
结论：新的列线图用于预测转移性LCNEC患者的CSS，提供个性化的风险评估和辅助临床决策。
方法：我们使用监测进行了回顾性分析，流行病学,和最终结果(SEER)数据库，确定2010年至2017年间诊断为转移性LCNEC的患者。为了找到癌症特异性生存率(CSS)的独立预测因子，我们进行了Cox回归分析.开发了一个列线图来预测6-，12-，转移性LCNEC患者的18个月CSS率。一致性指数(C指数)，接收器工作特征(ROC)曲线(AUC)下面积，并采用校正曲线评估模型是否具有判别性和可靠性。使用决策曲线分析（DCAs）从临床角度评估模型的实用性和益处。
结果：本研究共纳入616名患者，其中432人被分配到训练队列,184人被分配到验证队列.年龄,T分期,N分期，转移部位，放射治疗,根据多变量Cox回归分析结果确定化疗是转移性LCNEC患者的独立预后因素。列线图显示了训练和验证队列的C指数值为0.733和0.728的强劲表现，分别。ROC曲线表明模型具有良好的预测性能，AUC值为0.796、0.735和0.736，用于预测6-，12-，在训练队列中转移性LCNEC患者的18个月CSS率，验证队列中的0.795、0.801和0.780，分别。校准曲线和DCA证实了列线图的可靠性和临床实用性。
结论：新的列线图用于预测转移性LCNEC患者的CSS，提供个性化的风险评估和辅助临床决策。
