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Abstract Details
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH
Hepatology. 2021 Feb 11. doi: 10.1002/hep.31750. Online ahead of print.
Amaro Taylor-Weiner1, Harsha Pokkalla1, Ling Han2, Catherine Jia2, Ryan Huss2, Chuhan Chung2, Hunter Elliott1, Benjamin Glass1, Kishalve Pethia1, Oscar Carrasco-Zevallos1, Chinmay Shukla1, Urmila Khettry3, Robert Najarian4, Ross Taliano5, G Mani Subramanian2, Robert P Myers2, Ilan Wapinski1, Aditya Khosla1, Murray Resnick15, Michael C Montalto1, Quentin M Anstee6, Vincent Wai-Sun Wong7, Michael Trauner8, Eric J Lawitz9, Stephen A Harrison10, Takeshi Okanoue11, Manuel Romero-Gomez12, Zachary Goodman1314, Rohit Loomba15, Andrew H Beck1, Zobair M Younossi1314
Author information
1PathAI, Boston, MA, USA.
2Gilead Sciences, Inc, Foster City, CA, USA.
3Lahey Hospital & Medical Center (emeritus), Burlington, MA, USA.
4University Gastroenterology, Portsmouth, RI, USA.
5Warren Alpert Medical School of Brown University, Providence, RI, USA.
6Translational & Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
7Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong.
8Division of Gastroenterology and Hepatology, Medical University of Vienna, Austria.
9Texas Liver Institute, UT Health San Antonio, Texas, United States.
10Pinnacle Clinical Research, San Antonio, TX, USA, Boston.
11Saiseikai Suita Hospital, Suita City, Osaka, Japan.
12Hospital Universitario Virgen del Rocio, Sevilla, Spain.
13Department of Medicine, Inova Fairfax Medical Campus, Falls Church, VA, USA.
14Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, USA.
15NAFLD Research Center, University of California at San Diego, La Jolla, CA, USA.
Abstract
Background & aims: Manual histologic assessment is currently the accepted standard for diagnosing and monitoring disease progression in nonalcoholic steatohepatitis (NASH), but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response.
Approach & results: Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We utilize samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histologic features in NASH including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a new heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment (DELTA) Liver Fibrosis score, which measured anti-fibrotic treatment effects that went undetected by manual pathological staging and was concordant with histologic disease progression.
Conclusions: Our ML method has shown reproducibility, sensitivity, and was prognostic for disease progression demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of novel therapies.