Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection

doi:10.3748/wjg.v31.i22.107197

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Jun 14, 2025 (publication date) through Mar 2, 2026

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Letter to the Editor

World J Gastroenterol. Jun 14, 2025; 31(22): 107197
Published online Jun 14, 2025. doi: 10.3748/wjg.v31.i22.107197

Clinical implications of a machine learning model predicting colorectal polyp recurrence after endoscopic mucosal resection

Yoshinori Kagawa

Yoshinori Kagawa, Department of Gastroenterological Surgery, Osaka International Cancer Institute, Osaka 541-8567, Japan

Author contributions: Kagawa Y wrote the full manuscript of this letter, read and approved the final version of the manuscript to be published.

Conflict-of-interest statement: I have no conflict of interest.

Corresponding author: Yoshinori Kagawa, MD, PhD, Chief Physician, Department of Gastroenterological Surgery, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka 541-8567, Japan. yoshinori.kagawa@oici.jp

Received: March 19, 2025
Revised: April 11, 2025
Accepted: April 23, 2025
Published online: June 14, 2025
Processing time: 85 Days and 20.6 Hours

Core Tip

Core Tip: This predictive model notably enhanced clinical decision-making for colorectal polyp surveillance, demonstrating high accuracy and ease of clinical implementation through a user-friendly online risk calculator. Although promising, its real-world utility depends on external validation, clinician training, and integration with the existing clinical guidelines.