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

• Published in: Hepatology (Baltimore, Md.) Vol. 74; no. 6; pp. 3146 - 3160
• Main Authors: Bosch, Jaime, Chung, Chuhan, Carrasco‐Zevallos, Oscar M., Harrison, Stephen A., Abdelmalek, Manal F., Shiffman, Mitchell L., Rockey, Don C., Shanis, Zahil, Juyal, Dinkar, Pokkalla, Harsha, Le, Quang Huy, Resnick, Murray, Montalto, Michael, Beck, Andrew H., Wapinski, Ilan, Han, Ling, Jia, Catherine, Goodman, Zachary, Afdhal, Nezam, Myers, Robert P., Sanyal, Arun J.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2021
• Subjects: Aspartate aminotransferase; Bilirubin; Biopsy; Cirrhosis; Clinical significance; Clinical Trials, Phase II as Topic; Collagen (type I); Diagnosis, Differential; Female; Fibrosis; Hepatology; Humans; Hypertension; Hypertension, Portal - diagnosis; Hypertension, Portal - etiology; Image Processing, Computer-Assisted - methods; Learning algorithms; Liver; Liver - pathology; Liver cirrhosis; Liver Cirrhosis - complications; Liver Cirrhosis - pathology; Machine Learning; Male; Middle Aged; Morphometry; Neural networks; Non-alcoholic Fatty Liver Disease - complications; Non-alcoholic Fatty Liver Disease - pathology; Patients; Portal Pressure; Prognosis; Randomized Controlled Trials as Topic; ROC Curve
• ISSN: 0270-9139; 1527-3350
• DOI: 10.1002/hep.32087

Background and Aims 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. Approach and Results Patients with NASH 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. 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 mm Hg) and elevated (5.5‐9.5 mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all P < 0.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML‐HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML‐HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although 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 An ML model based on trichrome‐stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.