Publication Name	World Journal of Gastrointestinal Surgery
Manuscript ID	107977
Country	China

Category	Gastroenterology & Hepatology
Manuscript Type	Retrospective Study
Article Title	Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning
Manuscript Source	Unsolicited Manuscript
All Author List	Wang-Shuo Yang, Yang Su, Yan-Qi Li, Jun-Bo Hu, Meng-Die Liu and Lu Liu
Funding Agency and Grant Number
Corresponding Author	Yang Su, Full Professor, Department of Thoracic Surgery, Zhongnan Hospital of Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan 430071, Hubei Province, China. yangsueinfo@163.com
Key Words	Machine learning; Rectal cancer; Parastomal Hernia; Shapley additive explanation algorithms; Predictive model
Core Tip	This research proposed and validated a predictive model based on machine learning techniques to assess the risk of parastomal hernia following prophylactic ostomy in individuals with rectal cancer. Among multiple algorithms, the random forest (RF) model achieved the best performance. Shapley additive explanations identified tumor distance from the anal verge, body mass index, and preoperative hypertension as key predictors. An online risk prediction tool based on the RF model has been created to support early screening and individualized postoperative management, offering practical value for clinical decision-making.
Citation	Yang WS, Su Y, Li YQ, Hu JB, Liu MD, Liu L. Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning. World J Gastrointest Surg 2025; In press

Received	2025-04-02 02:53
Peer-Review Started	2025-04-02 02:53
To Make the First Decision
Return for Revision	2025-04-20 05:01
Revised	2025-05-14 09:43
Second Decision	2025-07-31 02:40
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-07-31 06:38
Articles in Press	2025-07-31 06:38
Publication Fee Transferred	2025-05-15 16:13
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-9366 (online)
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