| ISSN |
1948-5204 (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) 2024. 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 |
Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Xiao-Lin Ji, Shuo Xu, Xiao-Yu Li, Jin-Huan Xu, Rong-Shuang Han, Ying-Jie Guo, Li-Ping Duan and Zi-Bin Tian |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
81802777 |
|
| Corresponding Author |
Li-Ping Duan, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, Beijing Key Laboratory for Helicobacter Pylori Infection and Upper Gastrointestinal Diseases, Peking University Third Hospital, No. 49 Garden Road, Haidian District, Beijing 100191, China. duanlp@bjmu.edu.cn |
| Key Words |
Colorectal cancer; Machine learning; Prognostic prediction model; Survival times; Important variables |
| Core Tip |
We developed and validated a promising machine learning architecture for predicting the 3-category survival times (cutoff values of 3 and 5 years) for 4 survival times (overall, disease-free, recurrence-free, and distant metastasis-free survival) and screened corresponding important variables. Fivefold cross validation and bootstrap validation were conducted. The models were evaluated with the area under the curve (AUC); moreover, the effectiveness of our variable screening methods was evaluated by comparing the models’ pre- and postscreening AUCs. SHapley Additive exPlanations were used to explain the decision-making process. Nomograms were drawn for various applications. |
| Publish Date |
2024-11-12 12:23 |
| Citation |
<p>Ji XL, Xu S, Li XY, Xu JH, Han RS, Guo YJ, Duan LP, Tian ZB. Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning. <i>World J Gastrointest Oncol</i> 2024; 16(12): 4597-4613</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v16/i12/4597.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v16.i12.4597 |
