A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH. Issue 1 (24th June 2021)

• Record Type: Journal Article
• Authors: Taylor‐Weiner, Amaro; Pokkalla, Harsha; Han, Ling; Jia, Catherine; Huss, Ryan; Chung, Chuhan; Elliott, Hunter; Glass, Benjamin; Pethia, Kishalve; Carrasco‐Zevallos, Oscar; Shukla, Chinmay; Khettry, Urmila; Najarian, Robert; Taliano, Ross; Subramanian, G. Mani; Myers, Robert P.; Wapinski, Ilan; Khosla, Aditya; Resnick, Murray; Montalto, Michael C.; Anstee, Quentin M.; Wong, Vincent Wai‐Sun; Trauner, Michael; Lawitz, Eric J.; Harrison, Stephen A.; Okanoue, Takeshi; Romero‐Gomez, Manuel; Goodman, Zachary; Loomba, Rohit; Beck, Andrew H.; Younossi, Zobair M.;
• Is Part Of: Hepatology. Volume 74:Issue 1(2021)
• Page Start: 133
• Page End: 147
• Publication Date: 2021-06-24
• Subjects: Heart -- Diseases -- Nursing -- Periodicals; Lungs -- Diseases -- Nursing -- Periodicals; Intensive care nursing -- Periodicals; Foie -- Maladies -- Périodiques; 616.362
• DOI: 10.1002/hep.31750
• Languages: English
• ISSNs: 0270-9139

Abstract

Abstract : Background and Aims: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in 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 and 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 use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological 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 heterogeneity‐sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. Conclusions: Our ML method has shown reproducibility and 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 therapies.