A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

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.