Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Department of Respiratory and Critical Care Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Cancer Control. 2024 Jan-Dec;31:10732748241274195. doi: 10.1177/10732748241274195.
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.
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.
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.
The new nomogram was developed for predicting CSS in patients with metastatic LCNEC, providing personalized risk evaluation and aiding clinical decision-making.
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。需要有效的方法来预测转移性 LCNEC 患者的生存情况。本研究旨在确定独立的生存预测因素，并为转移性 LCNEC 患者的生存预测制定列线图。
我们使用监测、流行病学和最终结果（SEER）数据库进行了回顾性分析，确定了 2010 年至 2017 年期间诊断为转移性 LCNEC 的患者。为了找到癌症特异性生存（CSS）的独立预测因素，我们进行了 Cox 回归分析。开发了一个列线图来预测转移性 LCNEC 患者的 6、12 和 18 个月 CSS 率。采用一致性指数（C 指数）、接收者操作特征（ROC）曲线下面积（AUC）和校准曲线来评估模型的判别能力和可靠性。决策曲线分析（DCAs）用于从临床角度评估模型的实用性和益处。
本研究共纳入 616 例患者，其中 432 例患者被分配到训练队列，184 例患者被分配到验证队列。多变量 Cox 回归分析结果显示，年龄、T 分期、N 分期、转移部位、放疗和化疗是转移性 LCNEC 患者的独立预后因素。列线图在训练队列中的 C 指数值分别为 0.733 和 0.728，验证队列中的 C 指数值分别为 0.733 和 0.728，表现出较强的性能。ROC 曲线表明该模型具有良好的预测性能，预测转移性 LCNEC 患者 6、12 和 18 个月 CSS 率的 AUC 值分别为 0.796、0.735 和 0.736，在训练队列中分别为 0.795、0.801 和 0.780，在验证队列中分别为 0.795、0.801 和 0.780。校准曲线和 DCA 证实了列线图的可靠性和临床实用性。
本研究开发了一种新的列线图，用于预测转移性 LCNEC 患者的 CSS，提供个性化的风险评估，并辅助临床决策。
