Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 21, 2024; 30(23): 2991-3004
Published online Jun 21, 2024. doi: 10.3748/wjg.v30.i23.2991
Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer
Li-Qun Cai, Da-Qing Yang, Rong-Jian Wang, He Huang, Yi-Xiong Shi
Li-Qun Cai, Da-Qing Yang, Rong-Jian Wang, He Huang, Department of Colorectal and Anal Surgery, Whenzhou Central Hospital, Wenzhou 325000, Zhejiang Province, China
Yi-Xiong Shi, Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang Province, China
Author contributions: Cai LQ designed the study; Cai LQ, Yang DQ, Wang RJ, and He H performed the study; Yang DQ, Wang RJ collected the data; Cai LQ and Shi YX analysed the data and wrote the manuscript; all authors read and approved the final manuscript.
Institutional review board statement: This study has been reviewed and approved by the Clinical Research Ethics Committee of Wenzhou Central Hospital and the First Hospital Affiliated to Wenzhou Medical University, No. KY2024-R016.
Informed consent statement: The present study was retrospective and a dispensation from informed consent has been requested.
Conflict-of-interest statement: All authors declare that there is no conflict of interest involved in this study.
Data sharing statement: Data can be obtained by contacting the corresponding author.
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: Yi-Xiong Shi, MD, Attending Doctor, Staff Physician, Department of Colorectal and Anorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Nanbaixiang Street, Ouhai District, Wenzhou 325000, Zhejiang Province, China. danshiyixiong@163.com
Received: March 25, 2024
Revised: May 7, 2024
Accepted: May 20, 2024
Published online: June 21, 2024
Processing time: 87 Days and 14.5 Hours
Core Tip

Core Tip: This study developed a machine learning model to predict unplanned reoperations in colorectal cancer patients, using data from two hospitals over two years. It employed support vector machine, least absolute shrinkage and selection operator, and extreme gradient boosting for feature selection and logistic regression to identify key risk factors. The model showed good predictive accuracy, validated by receiver operating characteristic curves, calibration curves, and decision curve analysis. Key predictors included age, gender, prior surgeries, and nutritional status. This predictive tool aims to enhance clinical decision-making, reduce reoperation rates, and improve patient outcomes in colorectal cancer care.