Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study

作者：Bo-Yu Kang, Yihuan Qiao, Jun Zhu, Bo Hu, Ze-Cheng Zhang, Jipeng Li, Yan-Jiang Pei · 发表于：World Journal of Gastroenterology · 年份：2025 · DOI：10.3748/wjg.v31.i19.105283 · 被引用次数：4 · 研究领域：Colorectal Cancer Surgical Treatments、Artificial Intelligence in Healthcare and Education、Colorectal Cancer Screening and Detection

BACKGROUND: Despite the promising prospects of utilizing artificial intelligence and machine learning (ML) for comprehensive disease analysis, few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations. In contrast to previous studies with small sample sizes and limited model interpretability, we developed a transparent eXtreme Gradient Boosting (XGBoost)-based model supported by multi-center data, using patients' basic information and clinical indicators to forecast the occurrence of anastomotic leakage (AL) after rectal cancer resection surgery. The model demonstrated robust predictive performance and identified clinically relevant thresholds, which may assist physicians in optimizing perioperative management. AIM: To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions. METHODS: Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery, Xijing Hospital of Digestive Diseases, Air Force Medical University, and Shaanxi Provincial People's Hospital, were retrospectively collected from January 2011 to December 2021,. Ten ML models were integrated to analyze the data and develop the predictive models. Receiver operating characteristic (ROC) curves, calibration curve, decision curve analysis, accuracy, sensitivity, specificity, p...