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Articles Published Processes
4/29/2025 9:13:40 AM | Browse: 9 | Download: 22
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Received |
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2025-03-03 01:09 |
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Peer-Review Started |
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2025-03-03 01:09 |
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To Make the First Decision |
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Return for Revision |
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2025-03-13 03:09 |
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Revised |
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2025-03-13 14:32 |
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Second Decision |
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2025-03-19 02:38 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2025-03-19 06:37 |
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Articles in Press |
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2025-03-19 06:37 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2025-03-23 22:48 |
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Typeset the Manuscript |
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2025-04-23 08:06 |
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Publish the Manuscript Online |
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2025-04-29 09:13 |
| 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) 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 |
Surgery |
| Manuscript Type |
Letter to the Editor |
| Article Title |
Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Eyad Gadour and Mohammed S AlQahtani |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Eyad Gadour, Associate Professor, CCST, Consultant, FACP, FRCP, MD, MRCP, Multiorgan Transplant Centre of Excellence, Liver Transplantation Unit, King Fahad Specialist Hospital, Ammar Bin Thabit Street, Dammam 32253, Saudi Arabia. eyadgadour@doctors.org.uk |
| Key Words |
Intrahepatic cholangiocarcinoma; Textbook outcome; Machine learning; Predictive model; Shapley additive explanations; Preoperative assessment; Surgical outcomes; Disease-free survival; Extreme gradient boosting; Clinical decision-making |
| Core Tip |
The extreme gradient boosting model used in conjunction with the Shapley additive explanation algorithm-machine learning model, as described by Huang et al, offers a revolutionary outlook into the future of surgical oncology for intrahepatic cholangiocarcinoma patients. This model identifies crucial preoperative factors that influence patient outcomes, enhances understanding of disease progression and treatment efficacy, and underscores its utility in clinical decision-making for patient care and surgical interventions. Moreover, the accurate predictive prognostic potential of this machine learning model offers insights into successful treatment mechanisms and personalized care strategies. |
| Publish Date |
2025-04-29 09:13 |
| Citation |
<p>Gadour E, AlQahtani MS. Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma. <i>World J Gastroenterol</i> 2025; 31(17): 106592</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i17/106592.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i17.106592 |
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