Hu Junxi, Liu Qingwen, He Wenbo, Wu Jun, Zhang Dong, Sun Chao, Lu Shichun, Wang Xiaolin, Shu Yusheng
Clinical Medical College, Yangzhou University, Yangzhou, 225001, China.
Department of Thoracic Surgery, Northern Jiangsu People's Hospital, Yangzhou, 225001, China.
Surg Endosc. 2025 May 2. doi: 10.1007/s00464-025-11759-5.
Anastomotic strictures (AS) frequently occurs in patients following esophageal cancer surgery, significantly affecting their long-term quality of life. This study aims to develop a machine learning model to predict high-risk AS, enabling early intervention and precise management.
A total of 1549 patients underwent radical esophageal cancer surgery and were split into a training set (1084) and a validation set (465). Adaptive Synthetic Sampling (ADASYN) handled class imbalance, while Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation refined key features. High-correlation features (r > 0.8) were assessed using variance inflation factors (VIFs) and clinical relevance. Machine learning models were trained and evaluated using area under curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA). Shapley Additive exPlanations (SHAP) analysis improved model interpretability.
Seven critical variables were finalized, including anastomotic leakage (AL), neoadjuvant therapy (NCRT), suture method (SM), endoscopic assistance (EA), white blood cell count (WBC), albumin (Alb), and Suture site (SS). The Gradient Boosting Machine (GBM) model achieved the highest AUC, with 0.886 in the training set and 0.872 in the validation set. Shapley Additive Explanations (SHAP) analysis indicated that AL, SM, and NCRT were the most significant variables for model predictions.
The GBM machine learning model constructed in this study can effectively identify high-risk patients for AS following esophageal cancer surgery, offering strong support for earlier postoperative detection and precise clinical management.
吻合口狭窄（AS）在食管癌手术后的患者中经常出现，严重影响他们的长期生活质量。本研究旨在开发一种机器学习模型来预测高危AS，以便进行早期干预和精准管理。
共有1549例患者接受了食管癌根治术，并被分为训练集（1084例）和验证集（465例）。自适应合成采样（ADASYN）处理类别不平衡问题，而具有交叉验证的Boruta和最小绝对收缩与选择算子（LASSO）优化关键特征。使用方差膨胀因子（VIF）和临床相关性评估高相关性特征（r > 0.8）。使用曲线下面积（AUC）、准确性、敏感性、特异性、校准曲线和决策曲线分析（DCA）对机器学习模型进行训练和评估。Shapley加法解释（SHAP）分析提高了模型的可解释性。
最终确定了七个关键变量，包括吻合口漏（AL）、新辅助治疗（NCRT）、缝合方法（SM）、内镜辅助（EA）、白细胞计数（WBC）、白蛋白（Alb）和缝合部位（SS）。梯度提升机（GBM）模型的AUC最高，训练集中为0.886，验证集中为0.872。Shapley加法解释（SHAP）分析表明，AL、SM和NCRT是模型预测中最重要的变量。
本研究构建的GBM机器学习模型能够有效识别食管癌手术后发生AS的高危患者，为术后早期检测和精准临床管理提供有力支持。
