Machine learning-based prediction of duodenal stump leakage following laparoscopic gastrectomy for gastric cancer.

Journal: Surgery
PMID: 39732029

Abstract

BACKGROUND: Duodenal stump leakage is one of the most critical complications following gastrectomy surgery, with a high mortality rate. The present study aimed to establish a predictive model based on machine learning for forecasting the occurrence of duodenal stump leakage in patients who underwent laparoscopic gastrectomy for gastric cancer.

Authors

  • Yanqi Li
    Department of Gastrointestinal Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, China. Electronic address: lyqyst@gmail.com.
  • Yang Su
    School of Computer and Information, Dongguan City College, Dongguan 523419, China.
  • Shengli Shao
    The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiaokun Liu
    Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Jichao Qin
    Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Molecular Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Department of Gastrointestinal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address: jcqin2024@mail.zju.edu.cn.

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