Dai Huiping, Li Guang, Zhang Cheng, Huo Qi, Tang Tingting, Ding Fei, Wang Jianjun, Duan Guangliang
Department of Cardiothoracic Surgery, The Affiliated Hospital of Hangzhou Normal University, 310015, Zhejiang, China.
Department of Cardiothoracic Surgery, The Sixth People's Hospital of Zhengzhou, 450052, Henan, China.
Eur J Surg Oncol. 2025 Jul;51(7):109729. doi: 10.1016/j.ejso.2025.109729. Epub 2025 Feb 25.
Evidence is limited on whether patients with advanced pulmonary carcinoid (APC) benefit from comprehensive pulmonary resection (CPR), chemotherapy, or radiotherapy. Existing prognostic models for APC are limited and do not guide treatment selection. This study aims to develop and evaluate a multivariable machine learning model to predict overall survival in APC patients and provide a web-based prognostic tool.
Clinical data of APC patients were obtained from SEER database. Propensity score matching reduced retrospective study bias. Kaplan-Meier analysis evaluated survival differences between CPR vs. nonCPR, chemotherapy (Chem) vs. no chemotherapy (nonChem), and radiotherapy (Radio) vs. no radiotherapy (nonRadio). Univariate and multivariate Cox regression identified survival-associated variables. Using these clinical variables, 91 machine learning models were developed to predict APC survival, and the best model led to a web-based prognostic tool.
Among 1077 APC patients, 37.0 % underwent CPR, 30.2 % received chemotherapy, and 19.9 % received radiotherapy. After matching, overall survival was significantly improved in the CPR compared to the nonCPR. However, there were no significant differences in survival between the Chem and nonChem groups or between the Radio and nonRadio groups. Eight out of 13 clinical variables were significant prognostic variables. Models with eight variables reached a mean C-index of 0.770 and a 5-year AUC of 0.835. Using all 13 variables, a C-index of 0.785 and an AUC of 0.850 was achieved. An online tool (https://apcmodel.shinyapps.io/APCsp/) displays survival curves for different treatments.
The developed prognostic model enables individualized survival predictions and supports evidence-based treatment decisions for APC patients.
关于晚期肺类癌（APC）患者是否能从全面肺切除（CPR）、化疗或放疗中获益的证据有限。现有的APC预后模型存在局限性，无法指导治疗选择。本研究旨在开发并评估一种多变量机器学习模型，以预测APC患者的总生存期，并提供基于网络的预后工具。
从监测、流行病学和最终结果（SEER）数据库中获取APC患者的临床数据。倾向评分匹配减少了回顾性研究偏差。Kaplan-Meier分析评估了CPR与非CPR、化疗（Chem）与非化疗（nonChem）、放疗（Radio）与非放疗（nonRadio）之间的生存差异。单因素和多因素Cox回归确定了与生存相关的变量。利用这些临床变量，开发了91个机器学习模型来预测APC的生存期，最佳模型形成了一个基于网络的预后工具。
在1077例APC患者中，37.0%接受了CPR，30.2%接受了化疗，19.9%接受了放疗。匹配后，与非CPR相比，CPR组的总生存期显著改善。然而，化疗组与非化疗组之间或放疗组与非放疗组之间的生存率无显著差异。13个临床变量中有8个是显著的预后变量。包含8个变量的模型平均C指数为0.770，5年曲线下面积（AUC）为0.835。使用全部13个变量时，C指数为0.785，AUC为0.850。一个在线工具（https://apcmodel.shinyapps.io/APCsp/）展示了不同治疗方法的生存曲线。
所开发的预后模型能够进行个性化生存预测，并为APC患者的循证治疗决策提供支持。
