Observational Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 14, 2025; 31(22): 106937
Published online Jun 14, 2025. doi: 10.3748/wjg.v31.i22.106937
Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence
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, Melanie Deutsch
Eleni Myrto Trifylli, John Koskinas, Hariklia Kranidioti, Spilios Manolakopoulos, Melanie Deutsch, Gastrointestinal-Liver Unit, The 2nd Department of Internal Medicine, National and Kapodistrian University of Athens, General Hospital of Athens “Hippocratio,” Athens 11521, Greece
Eleni Myrto Trifylli, Anastasios G Kriebardis, Sotirios P Fortis, Vasiliki Pantazatou, Laboratory of Reliability and Quality Control in Laboratory Hematology, Department of Biomedical Sciences, Section of Medical Laboratories, School of Health & Caring Sciences, University of West Attica, Egaleo 12243, Attikí, Greece
Athanasios Angelakis, Department of Epidemiology and Data Science, Amsterdam University Medical Center, Amsterdam 1105, Netherlands
Athanasios Angelakis, Department of Methodology, Digital Health, Amsterdam Public Health Research Institute, Amsterdam 1105, Netherlands
Athanasios Angelakis, Data Science Center, University of Amsterdam, Amsterdam 1098, Netherlands
Nikolaos Papadopoulos, The Second Department of Internal Medicine, 401 General Army Hospital of Athens, Athens 11525, Greece
Evangelos Koustas, Department of Oncology, General Hospital Evangelismos, Athens 10676, Greece
Panagiotis Sarantis, Department of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, Athens 11527, Greece
Co-first authors: Eleni Myrto Trifylli and Athanasios Angelakis.
Author contributions: Trifylli EM and Angelakis A are co-first authors and both contributed to the conception of the study; Trifylli EM, Angelakis A, Kriebardis AG, Papadopoulos N, Fortis SP, Manolakopoulos S, and Deutsch M contributed to the design of the study; Trifylli EM contributed to data acquisition and interpretation and drafting, reviewing, and editing the manuscript; Angelakis A contributed to data processing, analysis, and interpretation and drafting, reviewing, editing, and supervising the manuscript; Kriebardis AG contributed to the supervision, data acquisition, sample processing, analysis, and reviewing and editing the manuscript; Papadopoulos N contributed to the data acquisition, transient elastography operation, data interpretation, and reviewing and editing the manuscript; Fortis SP contributed to sample processing and analysis, data acquisition, data interpretation, and manuscript review; Pantazatou V contributed to sample processing; Koskinas J and Kranidioti H contributed to data acquisition and review of the manuscript; Koustas E and Sarantis P contributed to the review of the manuscript; Angelakis A, Kriebardis AG, Papadopoulos N, Manolakopoulos S, and Deutsch M critically revised the manuscript for important intellectual content; All authors approved the final version of the manuscript to be published and ensured that questions related to the accuracy or integrity of the work were appropriately investigated.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the General Hospital of Athens “Hippocratio” in 1st Health Authority of Greece, Attica (No. 24, dated 15 November 2022).
Informed consent statement: Informed consent was obtained from all subjects involved in the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: Our methodological pipeline is transparently documented for reproducibility; however, the dataset is not publicly accessible due to ethical and privacy restrictions but is available upon reasonable request following institutional approval. The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request at nipapmed@gmail.com.
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/
Corresponding author: Nikolaos Papadopoulos, MD, PhD, Chief, Director, The Second Department of Internal Medicine, 401 General Army Hospital of Athens, 138 Mesogeion Ave, Athens 11525, Greece. nipapmed@gmail.com
Received: March 11, 2025
Revised: April 18, 2025
Accepted: May 22, 2025
Published online: June 14, 2025
Processing time: 93 Days and 10.9 Hours
Core Tip

Core Tip: This study evaluated circulating plasma extracellular vesicles (EVs) as metabolic dysfunction-associated steatotic liver disease biomarkers for steatosis identification and staging using machine learning and explainable artificial intelligence. EV-based machine learning models demonstrated that mean size and concentration of EVs are key predictors that effectively distinguish the absence of significant steatosis in patients with metabolic dysfunction and the presence of severe steatosis (S3) when they are combined with clinical and anthropomorphic data. Further, large multicenter studies, comparison with advanced imaging methods, and histopathology validation are required to confirm the clinical utility of EVs.