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A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis.
- Source :
-
Hepatology (Baltimore, Md.) [Hepatology] 2021 Dec; Vol. 74 (6), pp. 3146-3160. - Publication Year :
- 2021
-
Abstract
- 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.<br />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.<br />Conclusions: An ML model based on trichrome-stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.<br /> (© 2021 by the American Association for the Study of Liver Diseases.)
- Subjects :
- Biopsy
Clinical Trials, Phase II as Topic
Diagnosis, Differential
Female
Humans
Hypertension, Portal etiology
Liver Cirrhosis pathology
Machine Learning
Male
Middle Aged
Non-alcoholic Fatty Liver Disease pathology
Portal Pressure
Prognosis
ROC Curve
Randomized Controlled Trials as Topic
Hypertension, Portal diagnosis
Image Processing, Computer-Assisted methods
Liver pathology
Liver Cirrhosis complications
Non-alcoholic Fatty Liver Disease complications
Subjects
Details
- Language :
- English
- ISSN :
- 1527-3350
- Volume :
- 74
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Hepatology (Baltimore, Md.)
- Publication Type :
- Academic Journal
- Accession number :
- 34333790
- Full Text :
- https://doi.org/10.1002/hep.32087