Interpretable machine learning model for predicting anastomotic leak after esophageal cancer surgery via LightGBM
Xiaodong Yang, Fulin Dou, Guoshuo Tang, Ruipu Xiu, Xiaogang Zhao
Abstract
BACKGROUND: Postoperative anastomotic leakage (AL) is a severe complication following esophageal cancer surgery, that often leads to a poor prognosis. This study aims to develop an interpretable machine learning (ML) model to predict AL occurrence and identify associated risk factors. METHODS: A retrospective case‒control study analyzed clinical and laboratory data from esophageal cancer patients obtained via a case management system. Nine machine learning (ML) models were compared to identify the best-performing model and its optimal feature set. The selected LightGBM-based model underwent internal cross-validation and external validation. Performance was evaluated via metrics such as ROC, DCA, and PR curves. To enhance interpretability, the SHapley Additive exPlanations (SHAP) method was applied for feature analysis. RESULTS: Data from a total of 406 esophageal cancer patients were collected, and the LightGBM-based model showed the best performance. The model included the following features: lesion length, McKeown surgery, gastrointestinal decompression drainage (GID) volume on postoperative day 1, and prealbumin difference. SHAP dependence plots were created for each variable to understand their impact on the outcome. The model achieved an AUC of 0.956 (95% CI: 0.934–0.978). CONCLUSION: This study successfully developed an interpretable ML model based on the LightGBM to predict postoperative AL in patients with esophageal cancer.