Isakov V. Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up. World J Gastroenterol 2025; 31(19): 106628 [DOI: 10.3748/wjg.v31.i19.106628]
Corresponding Author of This Article
Vasily Isakov, MD, PhD, Professor, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, 21 Kashirskoye Shosse, Moscow 115446, Russia. vasily.isakov@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. May 21, 2025; 31(19): 106628 Published online May 21, 2025. doi: 10.3748/wjg.v31.i19.106628
Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up
Vasily Isakov
Vasily Isakov, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow 115446, Russia
Author contributions: Isakov V wrote and edited the manuscript and reviewed the literature.
Supported by Ministry of Science and Higher Education of the Russian Federation, No. FGMF-2025-0003.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Vasily Isakov, MD, PhD, Professor, Department of Gastroenterology and Hepatology, Federal Research Center of Nutrition, Biotechnology and Food Safety, 21 Kashirskoye Shosse, Moscow 115446, Russia. vasily.isakov@gmail.com
Received: March 4, 2025 Revised: April 6, 2025 Accepted: May 6, 2025 Published online: May 21, 2025 Processing time: 78 Days and 19.3 Hours
Abstract
Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, but recurrence rates remain high. Traditional surveillance strategies rely on polyp characteristics and completeness of the resection potentially missing key risk factors. Machine learning (ML) offers a transformative approach by integrating patient-specific data to refine risk stratification. Recent studies highlight ML models, such as Extreme Gradient Boosting, which outperform conventional methods in predicting polyp recurrence within one-year post-EMR. These models incorporate factors like age, smoking status, family history, and pathology, optimizing follow-up recommendations and minimizing unnecessary procedures. Artificial intelligence (AI)-driven tools and web-based calculators enhance clinical workflow by providing real-time, personalized risk assessments. However, challenges remain in external validation, model interpretability, and clinical integration. Future surveillance strategies should combine expert judgment with AI insights to optimize patient outcomes. As gastroenterology embraces AI, ML-driven surveillance represents a paradigm shift, advancing precision medicine in colorectal polyp management. This editorial explores AI’s role in transforming post-EMR follow-up, addressing benefits, limitations, and future directions.
Core Tip: The recurrence rates of colorectal polyps after endoscopic mucosal resection remain high. Traditional surveillance strategies rely only on polyp characteristics, potentially missing important risk factors. Machine learning-based models leveraging patient- and polyp-related factors may accurately predict polyp recurrence. Personalized machine-learning-driven risk stratification may optimize surveillance, reduce unnecessary procedures, and improve early cancer detection and cost-effectiveness. Future models should be validated across diverse populations.
