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

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; ... (2021). A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis. Hepatology, 74(6), pp. 3146-3160. Wiley 10.1002/hep.32087

Preview

Text hep.32087.pdf - Accepted Version Available under License Publisher holds Copyright. Download (2MB) | Preview

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.

Interest & Impact

Downloads

560 since deposited on 13 Oct 2021
203 in the past 12 months

Citations

5 Citations in Web of Science ®
6 Citations in Scopus

Search

in Google Scholar™

Services

Actions (login required)

Edit item

Actions (login required)

Edit item