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Articles Published Processes
6/26/2026 9:50:21 AM | Browse: 0 | Download: 0
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Received |
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2026-02-24 09:06 |
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Peer-Review Started |
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2026-02-24 09:09 |
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First Decision by Editorial Office Director |
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2026-03-03 09:24 |
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Return for Revision |
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2026-03-03 09:24 |
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Revised |
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2026-03-15 19:53 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-05-29 03:06 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-29 08:00 |
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Articles in Press |
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2026-05-29 08:00 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-06-16 01:03 |
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Publish the Manuscript Online |
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2026-06-26 09:50 |
| ISSN |
1948-5182 (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: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© Author(s) (or their employer(s)) 2026. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc. |
| 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 |
Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Annabel Koivu, Ghazal Azarfar, Maryam Shojaee, Naomi K T Hlaing, Sameera Rizvi, Divya Sharma, Saba Maleki and Mamatha Bhat |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Annabel Koivu, MD, Department of Medicine, University Health Network, 500 University Avenue, Suite 602, Toronto ON M5G 1V7, Ontario, Canada. annabel.koivu2@uhn.ca |
| Key Words |
Artificial intelligence; Liver transplantation; Graft fibrosis prediction; Transient elastography; Machine learning; Liver stiffness measurements |
| Core Tip |
Accurate non-invasive assessment of graft fibrosis after liver-transplantation remains challenging. In this study, we developed an extreme gradient boosting-based machine learning model integrating transient elastography-derived liver stiffness measurements with clinical and laboratory variables to predict clinically significant graft fibrosis. The model demonstrated strong diagnostic performance and highlights the potential of multimodal data integration to improved non-invasive fibrosis assessment in transplant recipients. External validation in multicenter cohorts will be required before clinical implementation. |
| Publish Date |
2026-06-26 09:50 |
| Citation |
Koivu A, Azarfar G, Shojaee M, Hlaing NKT, Rizvi S, Sharma D, Maleki S, Bhat M. Machine learning model integrating transient elastography and clinical data for prediction of graft fibrosis after liver transplantation. World J Hepatol 2026; 18(6): 120258
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| URL |
https://www.wjgnet.com/1948-5182/full/v18/i6/120258.htm |
| DOI |
https://doi.org/10.4254/wjh.120258 |
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