Artificial intelligence scoring of liver biopsies in a phase II trial of semaglutide in nonalcoholic steatohepatitis

作者：Vlad Ratziu, Sven Francque, Cynthia Behling, Vanja Cejvanovic, Helena Cortez‐Pinto, Janani Iyer, Niels Krarup, Quang Le, Anne‐Sophie Sejling, Dina Tiniakos, Stephen A. Harrison · 发表于：Hepatology · 年份：2023 · DOI：10.1097/hep.0000000000000723 · 被引用次数：39 · 研究领域：Liver Disease Diagnosis and Treatment、Hepatocellular Carcinoma Treatment and Prognosis、Liver Diseases and Immunity

BACKGROUND AND AIMS: Artificial intelligence-powered digital pathology offers the potential to quantify histological findings in a reproducible way. This analysis compares the evaluation of histological features of NASH between pathologists and a machine-learning (ML) pathology model. APPROACH AND RESULTS: This post hoc analysis included data from a subset of patients (n=251) with biopsy-confirmed NASH and fibrosis stage F1-F3 from a 72-week randomized placebo-controlled trial of once-daily subcutaneous semaglutide 0.1, 0.2, or 0.4 mg (NCT02970942). Biopsies at baseline and week 72 were read by 2 pathologists. Digitized biopsy slides were evaluated by PathAI's NASH ML models to quantify changes in fibrosis, steatosis, inflammation, and hepatocyte ballooning using categorical assessments and continuous scores. Pathologist and ML-derived categorical assessments detected a significantly greater percentage of patients achieving the primary endpoint of NASH resolution without worsening of fibrosis with semaglutide 0.4 mg versus placebo (pathologist 58.5% vs. 22.0%, p < 0.0001; ML 36.9% vs. 11.9%; p =0.0015). Both methods detected a higher but nonsignificant percentage of patients on semaglutide 0.4 mg versus placebo achieving the secondary endpoint of liver fibrosis improvement without NASH worsening. ML continuous scores detected significant treatment-induced responses in histological features, including a quantitative reduction in fibrosis with semaglutide 0.4 mg versus placebo ...