| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Retrospective Study |
| Article Title |
Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yi-Heng Shi, Jun-Liang Liu, Cong-Cong Cheng, Wen-Ling Li, Han Sun, Xi-Liang Zhou, Hong Wei and Su-Juan Fei |
| Funding Agency and Grant Number |
|
| Corresponding Author |
Su-Juan Fei, Chief Physician, MD, Professor, Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, No. 99 West Huaihai Road, Xuzhou 221002, Jiangsu Province, China. xyfyfeisj99@163.com |
| Key Words |
Colorectal polyps; Machine learning; Predictive model; Risk factors; Shapley Additive Explanation |
| Core Tip |
This study is the first to use machine learning methods to construct and validate a prediction model for one year recurrence of colorectal polyps after endoscopic mucosal resection. Key predictors included age, smoking, family history, diarrhea, hazard classification, Helicobacter pylori infection, number and size of polyps. According to receiver operating characteristic curves, sensitivity, specificity, accuracy, precision, and F1 scores, eXtreme Gradient Boosting model has the best performance. Based on this model, an online web calculator was built to help clinicians better distinguish high-risk groups and provide patients with personalized colonoscopy follow-up recommendations. |
| Publish Date |
2025-03-13 10:10 |
| Citation |
<p>Shi YH, Liu JL, Cheng CC, Li WL, Sun H, Zhou XL, Wei H, Fei SJ. Construction and validation of machine learning-based predictive model for colorectal polyp recurrence one year after endoscopic mucosal resection. <i>World J Gastroenterol</i> 2025; 31(11): 102387</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i11/102387.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i11.102387 |
