ArticleJGH open : an open access journal of gastroenterology and hepatology2025
Machine-Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study.
Article in JGH open : an open access journal of gastroenterology and hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Hemorrhagic Complications in Gastric Cancer: Current Evidence and Multidisciplinary Management Strategies.Cancers · 2026Review
- Establishment and validation of a predictive scoring system for titanium clip retention at six months after endoscopic submucosal dissection for gastric lesions.Scientific reports · 2026Article
- Predicting post-ESD bleeding in elderly patients with early gastrointestinal cancer: an explainable model based on endoscopic features.Frontiers in medicine · 2026Article
- Multidimensional decision support by artificial intelligence across the full workflow of endoscopic submucosal dissection for early gastric cancer.Frontiers in oncology · 2026Review
- Machine-Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study.JGH open : an open access journal of gastroenterology and hepatology · 2025Article
- An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction.PloS one · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastric cancer (GC); however, post-ESD bleeding remains a serious and unpredictable complication. This study aimed to develop machine-learning (ML) models to predict post-ESD bleeding and identify associated risk factors, ensuring accurate and interpretable predictions. Methods: A retrospective, multicenter clinical database was constructed for patients who underwent ESD for early GC. An ML model was developed using patient characteristics and perioperative findings to predict bleeding within 28 days post-ESD. Its performance was compared with that of a logistic regression-based non-ML model. Feature importance analysis was performed to aid interpretation. Results: Among 1084 patients (median age: 75 years), post-ESD bleeding occurred in 63 (5.8%). The area under the curve of the ML model was better than that of the non-ML model (0.80 vs. 0.71, Conclusions: The ML model effectively rules out post-ESD bleeding and identifies clinically meaningful risk factors, supporting its use in personalized prophylactic strategies.
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Registered trials
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