| 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 |
Observational Study |
| Article Title |
Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Eleni Myrto Trifylli, Athanasios Angelakis, Anastasios G Kriebardis, Nikolaos Papadopoulos, Sotirios P Fortis, Vasiliki Pantazatou, John Koskinas, Hariklia Kranidioti, Evangelos Koustas, Panagiotis Sarantis, Spilios Manolakopoulos and Melanie Deutsch |
| Funding Agency and Grant Number |
|
| Corresponding Author |
Nikolaos Papadopoulos, Chief, Director, MD, PhD, The Second Department of Internal Medicine, 401 General Army Hospital of Athens, 138 Mesogeion Ave, Athens 11525, Greece. nipapmed@gmail.com |
| Key Words |
Metabolic dysfunction-associated steatotic liver disease; Extracellular vesicles; Non-invasive biomarkers; Machine learning; Explainable artificial intelligence; Transient elastography; Metabolic dysfunction; Hepatic steatosis |
| Core Tip |
This study evaluates circulating plasma extracellular vesicles (EVs) as metabolic dysfunction-associated steatotic liver disease (MASLD) biomarkers for steatosis identification and staging using machine learning (ML) and explainable artificial intelligence (XAI). EV-based ML models demonstrated that mean size and concentration of EVs, are key predictors, and they effectively distinguish the absence of significant steatosis (S0) in patients with metabolic dysfunction, and the severe steatosis (S3) when they are combined with clinical and anthropomorphic data. The utilization of ML and XAI demonstrated non-linear patterns, outperforming the conventional linear statistical analysis, shedding light on the complexity of the disease, and providing interpretable MASLD staging insights. Further, large multicenter studies, comparison with advanced imaging methods, and histopathology validation are required to confirm EV’s clinical utility. |
| Publish Date |
2025-06-12 03:45 |
| Citation |
<p>Trifylli EM, Angelakis A, Kriebardis AG, Papadopoulos N, Fortis SP, Pantazatou V, Koskinas J, Kranidioti H, Koustas E, Sarantis P, Manolakopoulos S, Deutsch M. Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence. <i>World J Gastroenterol</i> 2025; 31(22): 106937</p> |
| URL |
https://www.wjgnet.com/1007-9327/full/v31/i22/106937.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v31.i22.106937 |
