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Abstract Details |
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A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH |
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Hepatology. 2021 Feb 11. doi: 10.1002/hep.31750. Online ahead of print.
Amaro Taylor-Weiner 1, Harsha Pokkalla 1, Ling Han 2, Catherine Jia 2, Ryan Huss 2, Chuhan Chung 2, Hunter Elliott 1, Benjamin Glass 1, Kishalve Pethia 1, Oscar Carrasco-Zevallos 1, Chinmay Shukla 1, Urmila Khettry 3, Robert Najarian 4, Ross Taliano 5, G Mani Subramanian 2, Robert P Myers 2, Ilan Wapinski 1, Aditya Khosla 1, Murray Resnick 1 5, Michael C Montalto 1, Quentin M Anstee 6, Vincent Wai-Sun Wong 7, Michael Trauner 8, Eric J Lawitz 9, Stephen A Harrison 10, Takeshi Okanoue 11, Manuel Romero-Gomez 12, Zachary Goodman 13 14, Rohit Loomba 15, Andrew H Beck 1, Zobair M Younossi 13 14
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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.
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