RT Journal Article
T1 A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.
A1 Taylor-Weiner, Amaro
A1 Pokkalla, Harsha
A1 Han, Ling
A1 Jia, Catherine
A1 Huss, Ryan
A1 Chung, Chuhan
A1 Elliott, Hunter
A1 Glass, Benjamin
A1 Pethia, Kishalve
A1 Carrasco-Zevallos, Oscar
A1 Shukla, Chinmay
A1 Khettry, Urmila
A1 Najarian, Robert
A1 Taliano, Ross
A1 Subramanian, G Mani
A1 Myers, Robert P
A1 Wapinski, Ilan
A1 Khosla, Aditya
A1 Resnick, Murray
A1 Montalto, Michael C
A1 Anstee, Quentin M
A1 Wong, Vincent Wai-Sun
A1 Trauner, Michael
A1 Lawitz, Eric J
A1 Harrison, Stephen A
A1 Okanoue, Takeshi
A1 Romero-Gomez, Manuel
A1 Goodman, Zachary
A1 Loomba, Rohit
A1 Beck, Andrew H
A1 Younossi, Zobair M
AB 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.
YR 2021
FD 2021-06-24
LK http://hdl.handle.net/10668/17146
UL http://hdl.handle.net/10668/17146
LA en
DS RISalud
RD Apr 17, 2025