| 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 |
Medical Informatics |
| Manuscript Type |
Retrospective Study |
| Article Title |
Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Da-Long Zhu, Alimu Tulahong, Chang Liu, Ayinuer Aierken, Wei Tan, Rexiati Ruze, Zhong-Dian Yuan, Lei Yin, Tie-Min Jiang, Ren-Yong Lin, Ying-Mei Shao and Tuerganaili Aji |
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Natural Science Foundation of Xinjiang Uygur Autonomous Region |
2022D01D17 |
| State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia |
SKL-HIDCA-2024-2 |
|
| Corresponding Author |
Tuerganaili Aji, Chief Physician, Director, PhD, Professor, Department of Hepatobiliary and Echinococcosis Surgery, The First Affiliated Hospital of Xinjiang Medical University, No. 137 South Liyushan Road, Urumqi 830054, Xinjiang Uygur Autonomous Region, China. tuergan1@163.com |
| Key Words |
Surgical approach; Hepatectomy; Ex vivo liver resection and autotransplantation; Vascular invasion; Explainability |
| Core Tip |
This study explored machine-learning applications in hepatic alveolar echinococcosis surgical decision-making. Through feature selection methods and model comparisons, we identified key factors such as vascular invasion type influencing surgical choices. The XGBoost model showed good performance and clinical benefit. Preoperative assessment combined with model assistance can enhance personalized surgical planning and improve patient outcomes, offering valuable evidence for clinical practice. |
| Publish Date |
2025-09-25 03:33 |
| Citation |
<p>Zhu DL, Tulahong A, Liu C, Aierken A, Tan W, Ruze R, Yuan ZD, Yin L, Jiang TM, Lin RY, Shao YM, Aji T. Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning. <i>World J Gastroenterol</i> 2025; 31(37): 111038</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i37/111038.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i37.111038 |
