Machine‐Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study
The ML model effectively rules out post-ESD bleeding and identifies clinically meaningful risk factors, supporting its use in personalized prophylactic strategies.
Authors
- Soichi Sugitani
- Kazuya Takahashi (ORCID: https://orcid.org/0000-0002-3097-9841)
- Shuji Terai (ORCID: https://orcid.org/0000-0002-5439-635X)
- Hiroki Sato (ORCID: https://orcid.org/0000-0002-6994-1840)
- Ken‐ichi Mizuno (ORCID: https://orcid.org/0000-0002-9622-9250)
- Nao Nakajima (ORCID: https://orcid.org/0000-0002-0111-6537)
- Hiroki Maruyama (ORCID: https://orcid.org/0000-0002-0067-3235)
- Kosuke Kojima
Institutions
- Niigata University (JP)
Publication Details
- Journal
- JGH Open
- Published
- 2025-06-29
- DOI
- https://doi.org/10.1002/jgh3.70203
- Citations
- 2
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 3.04