A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH

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. APP ROA CH AND RESULT S: 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. (Hepatology 2021;74:133-147)

A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis.

BACKGROUND The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm. METHODS NASH patients with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI; Boston, MA). Using trichrome-stained biopsies in the training set (n=130), an ML model was developed to recognize fibrosis patterns associated with HVPG and the resultant ML HVPG score was validated in a held-out test set (n=88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH, HVPG ≥10 mm Hg) were determined. RESULTS The ML HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ=0.47 vs ρ=0.28; p<0.001). The ML HVPG score differentiated patients with normal (0-5 mmHg) and elevated HVPG (5.5-9.5 mmHg), and CSPH (median: 1.51 vs 1.93 vs 2.60; all p<0.05). The AUROCs (95%CI) of the ML HVPG score for CSPH were 0.85 (0.80,0.90) and 0.76 (0.68,85) in the training and test sets, respectively. Discrimination of the ML HVPG score for CSPH improved with addition of a ML parameter for nodularity, ELF, platelets, AST, and bilirubin (AUROC in test set: 0.85;95%CI 0.78,0.92). While baseline ML HVPG score was not prognostic, changes were predictive of clinical events (HR 2.13; 95%CI 1.26,3.59) and associated with hemodynamic response and fibrosis improvement. CONCLUSIONS A ML-model based on trichrome-stained liver biopsy slides can predict CSPH in NASH patients with cirrhosis

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

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. 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. 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