Interpretable machine learning model for predicting anastomotic leak after esophageal cancer surgery via LightGBM.

Journal: BMC cancer
Published Date: Jun 1, 2025

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.

Authors

  • Xiaodong Yang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Fulin Dou
    Department of Nephrology, The Second Hospital of Shandong University, Jinan, Shandong Province, China.
  • Guoshuo Tang
    Department of Thoracic Surgery, The Second Hospital of Shandong University, Beiyuan Street, Jinan City, 250021, Shandong Province, China.
  • Ruipu Xiu
    Department of Thoracic Surgery, The Second Hospital of Shandong University, Beiyuan Street, Jinan City, 250021, Shandong Province, China.
  • Xiaogang Zhao
    Department of Thoracic Surgery, The Second Hospital of Shandong University, Beiyuan Street, Jinan City, 250021, Shandong Province, China. zhaoxiaogang@sdu.edu.cn.

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