Abstract
Non-alcoholic fatty liver disease (NAFLD) is exceptionally common, with an estimated one hundred million
afflicted people in the United States. Detection and risk stratification of this very common disease remains a
major challenge. Despite recent advances, including development of numerous therapeutic agents presently in
phase 2 and 3 trials, NAFLD remains a silent disease in which the vast majority of patients accumulate
progressive liver damage without signs or symptoms and, undiagnosed, receive no medical care. The NAFLD
patients at highest risk of cirrhosis are those with moderate or greater liver fibrosis at the time of diagnosis, a
group of patients who are described as having high risk non-alcoholic steatohepatitis (hrNASH). The current
reference standard for identifying people with hrNASH is liver biopsy, which is expensive, invasive, and limited
by interobserver variability. The focus of this project is to develop and validate low cost non-invasive diagnostic
technology to diagnose hrNASH. We propose to accomplish this in three Specific Aims. First, we will expand
and annotate an existing database of patients with chronic liver disease from 328 subjects to 1,000 subjects,
~40% of whom will have NAFLD. The database will contain ~20,000 images (~10,000 ultrasound elastography
images and ~ 10,000 conventional ultrasound images) and multiple demographic and clinical data points for
each subject (a total of ~30,000 clinical, laboratory, and demographic data points). We have previously
developed advanced image processing techniques to make ultrasound elastography more accurate and less
variable. We will use this large database to develop, customize and refine our image processing techniques for
NAFLD evaluation (Aim 1), with the goal of improving ultrasound elastography diagnosis of hrNASH. Second,
we will combine conventional ultrasound elastography imaging, conventional ultrasound imaging, our advanced
image analysis techniques, and the demographic, clinical, and laboratory data in a machine learning model to
predict hrNASH and will compare the performance of our predictive model with the FIB4, a widely-used blood
test-based prediction rule (Aim 2). Third, we will validate our predictive model in an independent prospective
cohort of NAFLD subjects undergoing biopsy for NAFLD risk stratification (Aim 3). We hypothesize that the
combination of image processing-enhanced elastography and conventional ultrasound imagery combined with
demographic, clinical, and laboratory data will have greater predictive power for hrNASH than clinical or
sonographic data alone. The proposed predictive models have the potential to (1) reduce the number of liver
biopsies performed for hrNASH detection, (2) facilitate recruitment for clinical trials of NAFLD therapeutics, and
(3) improve care quality for the most common liver disease in the United States.
