Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis

IF 58.7 1区医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Nature Medicine Pub Date : 2024-11-04 DOI:10.1038/s41591-024-03301-2

Hanna Pulaski, Stephen A. Harrison, Shraddha S. Mehta, Arun J. Sanyal, Marlena C. Vitali, Laryssa C. Manigat, Hypatia Hou, Susan P. Madasu Christudoss, Sara M. Hoffman, Adam Stanford-Moore, Robert Egger, Jonathan Glickman, Murray Resnick, Neel Patel, Cristin E. Taylor, Robert P. Myers, Chuhan Chung, Scott D. Patterson, Anne-Sophie Sejling, Anne Minnich, Vipul Baxi, G. Mani Subramaniam, Quentin M. Anstee, Rohit Loomba, Vlad Ratziu, Michael C. Montalto, Nick P. Anderson, Andrew H. Beck, Katy E. Wack

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Abstract

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.

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基于人工智能的病理学工具对代谢功能障碍相关脂肪性肝炎评分的临床验证

代谢功能障碍相关性脂肪性肝炎（MASH）是肝脏相关疾病发病率和死亡率的主要原因，但治疗方案却很有限。肝脏活检的人工评分是目前临床试验入组和终点评估的黄金标准，但其读者变异性很高。本研究是对基于人工智能（AI）的病理系统--基于人工智能的代谢功能障碍相关脂肪性肝炎测量（AIM-MASH）--进行的最全面的多点分析和临床验证，以协助病理学家进行MASH试验组织学评分。与人工评分相比，AIM-MASH 具有很高的重复性和再现性。在准确评估炎症、球囊扩张、MAS ≥ 4 且各评分类别≥1 以及 MASH 分辨率方面，MASH 病理专家的 AIM-MASH 辅助读数优于无辅助读数，同时在脂肪变性和纤维化评估方面保持非劣势。这些研究结果表明，AIM-MASH 可以减少阅读器的变异性，为 MASH 临床试验提供更可靠的治疗评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Medicine 医学-生化与分子生物学

CiteScore

100.90

自引率

0.70%

发文量

525

审稿时长

1 months

期刊介绍： Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine. The publication focuses on originality, timeliness, interdisciplinary interest, and the impact on improving human health. In addition to research articles, Nature Medicine also publishes commissioned content such as News, Reviews, and Perspectives. This content aims to provide context for the latest advances in translational and clinical research, reaching a wide audience of M.D. and Ph.D. readers. All editorial decisions for the journal are made by a team of full-time professional editors. Nature Medicine consider all types of clinical research, including: -Case-reports and small case series -Clinical trials, whether phase 1, 2, 3 or 4 -Observational studies -Meta-analyses -Biomarker studies -Public and global health studies Nature Medicine is also committed to facilitating communication between translational and clinical researchers. As such, we consider “hybrid” studies with preclinical and translational findings reported alongside data from clinical studies.