Surgical endoscopy · 2026
Predicting long-term survival after gastrectomy in elderly patients with esophagogastric junction adenocarcinomas.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Shi-Jun, He Long-Peng, Xu Fu-Bin, Chen Yan-Min, Hu Jian-Xiong, Huang Chao-Xiong, Zheng Chang-Yue, Yang Shao-Bin, Wu Ying-Xin
Аннотация
BACKGROUND: The long-term survival outcomes of older patients with esophagogastric junction adenocarcinoma (EGJA) remain unclear. This study aimed to develop and validate a machine learning model to predict postoperative survival in patients aged ≥ 75 years with EGJA.
METHODS: This retrospective study analyzed patients aged ≥ 75 years who underwent surgical resection for EGJA between 2009 and 2018 across nine tertiary hospitals in China. A random survival forest (RSF) model was developed to predict overall survival (OS) and disease-free survival (DFS) using key clinicopathological variables. Model performance was compared with traditional Cox proportional hazards (CPH) regression and TNM staging.
RESULTS: The study included 1,202 patients. In the training set, the RSF model demonstrated superior discriminatory ability for 5-year OS (time-dependent AUC: 0.887 vs. 0.78) and DFS (0.836 vs. 0.767) compared to the CPH model. The RSF model also showed better calibration with higher C-index (CiD) for OS (0.723 vs. 0.704) and DFS (0.746 vs. 0.701). In the test set, the RSF model maintained robust performance, with CiD values of 0.719 for OS and 0.738 for DFS, outperforming the CPH model (0.704 and 0.697, respectively). In the independent external validation cohort, the RSF model maintained favorable performance and outperformed the CPH model for OS and DFS prediction, with CiD values of 0.720 for OS and 0.711 for DFS, compared with 0.687 and 0.679 for the CPH model, respectively. The RSF model exhibited particularly strong predictive power for both OS and DFS between 12 and 36 months post-surgery. Key predictors included pN stage, pT stage, age, and age-adjusted Charlson Comorbidity Index.
CONCLUSION: The machine learning-based RSF model provides individualized and accurate survival predictions for older patients with EGJA after surgical resection. This model may assist clinicians in postoperative risk stratification and follow-up planning.
METHODS: This retrospective study analyzed patients aged ≥ 75 years who underwent surgical resection for EGJA between 2009 and 2018 across nine tertiary hospitals in China. A random survival forest (RSF) model was developed to predict overall survival (OS) and disease-free survival (DFS) using key clinicopathological variables. Model performance was compared with traditional Cox proportional hazards (CPH) regression and TNM staging.
RESULTS: The study included 1,202 patients. In the training set, the RSF model demonstrated superior discriminatory ability for 5-year OS (time-dependent AUC: 0.887 vs. 0.78) and DFS (0.836 vs. 0.767) compared to the CPH model. The RSF model also showed better calibration with higher C-index (CiD) for OS (0.723 vs. 0.704) and DFS (0.746 vs. 0.701). In the test set, the RSF model maintained robust performance, with CiD values of 0.719 for OS and 0.738 for DFS, outperforming the CPH model (0.704 and 0.697, respectively). In the independent external validation cohort, the RSF model maintained favorable performance and outperformed the CPH model for OS and DFS prediction, with CiD values of 0.720 for OS and 0.711 for DFS, compared with 0.687 and 0.679 for the CPH model, respectively. The RSF model exhibited particularly strong predictive power for both OS and DFS between 12 and 36 months post-surgery. Key predictors included pN stage, pT stage, age, and age-adjusted Charlson Comorbidity Index.
CONCLUSION: The machine learning-based RSF model provides individualized and accurate survival predictions for older patients with EGJA after surgical resection. This model may assist clinicians in postoperative risk stratification and follow-up planning.