Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients

Machine learning-based real-time prediction of duodenal stump leakage from gastrectomy in gastric cancer patients

PurposeThis study aimed to develop a machine learning (ML) model for real-time prediction of duodenal stump leakage (DSL) following gastrectomy in patients with gastric cancer (GC) using a comprehensive set of clinical variables to improve postoperative outcomes and monitoring efficiency.MethodsA re...

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Bibliographic Details
Main Authors: Jae Hun Chung, Yushin Kim, Dongjun Lee, Dongwon Lim, Sun-Hwi Hwang, Si-Hak Lee, Woohwan Jung
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Surgery
Subjects:
duodenal stump leakage
gastrectomy
machine learning
gastric cancer
predictive modeling
Online Access:https://www.frontiersin.org/articles/10.3389/fsurg.2025.1550990/full
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https://www.frontiersin.org/articles/10.3389/fsurg.2025.1550990/full

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