| 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 Cohort Study |
| Article Title |
Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Ting-Feng Huang, Cong Luo, Luo-Bin Guo, Hong-Zhi Liu, Jiang-Tao Li, Qi-Zhu Lin, Rui-Lin Fan, Wei‐Ping Zhou, Jing-Dong Li, Ke-Can Lin, Shi-Chuan Tang and Yong-Yi Zeng |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Key Research and Development Program |
2022YFC2407304 |
| Major Research Project for Middle-Aged and Young Scientists of Fujian Provincial Health Commission |
2021ZQNZD013 |
| The National Natural Science Foundation of China |
62275050 |
| Fujian Province Science and Technology Innovation Joint Fund Project |
2019Y9108 |
| Major Science and Technology Projects of Fujian Province |
2021YZ036017 |
|
| Corresponding Author |
Yong-Yi Zeng, MD, PhD, Professor, Department of Hepatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, No. 312 Xihong Road, Fuzhou 350025, Fujian Province, China. lamp197311@126.com |
| Key Words |
Intrahepatic cholangiocarcinoma; Textbook outcome; Interpretable machine learning; Prediction; Prognosis |
| Core Tip |
This study developed a machine learning model to preoperatively predict the Textbook outcome (TO), a measure of surgical quality and short-term prognosis, and utilized the SHapley Additive exPlanations technique to enhance model transparency. Based on the analysis of 376 intrahepatic cholangiocarcinoma patients from four Chinese medical institutions, logistic regression identified key preoperative factors, including Child-Pugh classification, Eastern Cooperative Oncology Group score, hepatitis B status, and tumor size. The EXtreme Gradient Boosting algorithm was used to construct the prediction model, while SHAP visualized its decision-making process. The model effectively stratified recurrence-free survival, demonstrating its utility in preoperative TO prediction. |
| Publish Date |
2025-03-13 10:10 |
| Citation |
<p>Huang TF, Luo C, Guo LB, Liu HZ, Li JT, Lin QZ, Fan RL, Zhou W, Li JD, Lin KC, Tang SC, Zeng YY. Preoperative prediction of textbook outcome in intrahepatic cholangiocarcinoma by interpretable machine learning: A multicenter cohort study. <i>World J Gastroenterol</i> 2025; 31(11): 100911</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i11/100911.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i11.100911 |
