Nature medicine · 2024
Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Pulaski Hanna, Harrison Stephen A, Mehta Shraddha S, Sanyal Arun J, Vitali Marlena C, Manigat Laryssa C, Hou Hypatia, Madasu Christudoss Susan P, Hoffman Sara M, Stanford-Moore Adam, Egger Robert, Glickman Jonathan, Resnick Murray, Patel Neel, Taylor Cristin E, Myers Robert P, Chung Chuhan, Patterson Scott D, Sejling Anne-Sophie, Minnich Anne, Baxi Vipul, Subramaniam G Mani, Anstee Quentin M, Loomba Rohit, Ratziu Vlad, Montalto Michael C, Anderson Nick P, Beck Andrew H, Wack Katy E
Аннотация
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.