z-logo
Premium
A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH
Author(s) -
TaylorWeiner Amaro,
Pokkalla Harsha,
Han Ling,
Jia Catherine,
Huss Ryan,
Chung Chuhan,
Elliott Hunter,
Glass Benjamin,
Pethia Kishalve,
CarrascoZevallos Oscar,
Shukla Chinmay,
Khettry Urmila,
Najarian Robert,
Taliano Ross,
Subramanian G. Mani,
Myers Robert P.,
Wapinski Ilan,
Khosla Aditya,
Resnick Murray,
Montalto Michael C.,
Anstee Quentin M.,
Wong Vincent WaiSun,
Trauner Michael,
Lawitz Eric J.,
Harrison Stephen A.,
Okanoue Takeshi,
RomeroGomez Manuel,
Goodman Zachary,
Loomba Rohit,
Beck Andrew H.,
Younossi Zobair M.
Publication year - 2021
Publication title -
hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.488
H-Index - 361
eISSN - 1527-3350
pISSN - 0270-9139
DOI - 10.1002/hep.31750
Subject(s) - cirrhosis , medicine , fibrosis , liver disease , disease , clinical trial , randomized controlled trial , pathological , steatosis , pathology , radiology
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. Approach and 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 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.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here