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ORIGINAL RESEARCH article

Front. Med.
Sec. Pathology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1480866
This article is part of the Research Topic Insights in Pathology: New Aspects in 2024 View all 3 articles

Role of Artificial Intelligence in staging and assessing of treatment response in MASH patients

Provisionally accepted
Reha Akpinar Reha Akpinar 1,2Davide Panzeri Davide Panzeri 3Camilla De Carlo Camilla De Carlo 1,2Vincenzo Belsito Vincenzo Belsito 1Barbara Durante Barbara Durante 1,2Giuseppe Chirico Giuseppe Chirico 3Rosa Lombardi Rosa Lombardi 4,5Anna L. Fracanzani Anna L. Fracanzani 4,5Marco Maggioni Marco Maggioni 6Ivan Arcari Ivan Arcari 2,7Massimo Roncalli Massimo Roncalli 1,2Luigi M. Terracciano Luigi M. Terracciano 1,2Donato Inverso Donato Inverso 8Alessio Aghemo Alessio Aghemo 2,7Nicola Pugliese Nicola Pugliese 2,7*Laura Sironi Laura Sironi 3*Luca D. Tommaso Luca D. Tommaso 1,2*
  • 1 Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy
  • 2 Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
  • 3 Department of Physics, University of Milano-Bicocca, Milan, Lombardy, Italy
  • 4 SC Medicina Indirizzo Metabolico, IRCCS Ca 'Granda Foundation Maggiore Policlinico Hospital, Milan, Lombardy, Italy
  • 5 Department of Pathophysiology and Transplantation, University of Milan, Milan, Lombardy, Italy
  • 6 Department of Pathology, IRCCS Ca 'Granda Foundation Maggiore Policlinico Hospital, Milan, Lombardy, Italy
  • 7 Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy
  • 8 Division of Immunology, Transplantation and Infectious Diseases IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Lombardy, Italy

The final, formatted version of the article will be published soon.

    Background & Aims. The risk of disease progression in MASH increases proportionally to the pathological stage of fibrosis. This latter is evaluated through a semi-quantitative process, which has limited sensitivity in reflecting changes in disease or response to treatment.This study aims to test the clinical impact of Artificial Intelligence (AI) in characterizing liver fibrosis in MASH patients.Methods. The study included 60 patients with clinical pathological diagnosis of MASH. Among these, 17 received a medical treatment and underwent a post-treatment biopsy. For each biopsy (n = 77) a Sirius Red digital slide (SR-WSI) was obtained. AI extracts >30 features from SR-WSI, including estimated collagen area (ECA) and entropy of collagen (EnC).Results. AI highlighted that different histopathological stages are associated with progressive and significant increase of ECA (F2: 2.6%±0.4; F3: 5.7%±0.4; F4: 10.9%±0.8; p: 0.0001) and EnC (F2: 0.96±0.05; F3: 1.24±0.06; F4: 1.80±0.11, p: 0.0001); disclosed the heterogeneity of fibrosis among pathological homogenous cases; revealed post treatment fibrosis modification in 76% of the cases (vs 56% detected by histopathology).Conclusions. AI characterizes the fibrosis process by its true, continuous, and non-categorical nature, thus allowing for better identification of the response to anti-MASH treatment.

    Keywords: Liver, MASH, Fibrosis, Treatment, artificial intelligence

    Received: 14 Aug 2024; Accepted: 26 Sep 2024.

    Copyright: © 2024 Akpinar, Panzeri, De Carlo, Belsito, Durante, Chirico, Lombardi, Fracanzani, Maggioni, Arcari, Roncalli, Terracciano, Inverso, Aghemo, Pugliese, Sironi and Tommaso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Nicola Pugliese, Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090, Italy
    Laura Sironi, Department of Physics, University of Milano-Bicocca, Milan, 20126, Lombardy, Italy
    Luca D. Tommaso, Department of Pathology, IRCCS Humanitas Research Hospital, Rozzano, Italy

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