Abstract
To develop a quantitative ultrasound (QUS)- and elastography-based model to improve classification of steatosis grade, inflammation grade, and fibrosis stage in patients with chronic liver disease in comparison with shear wave elastography alone, using histopathology as the reference standard. This ancillary study to a prospective institutional review-board approved study included 82 patients with non-alcoholic fatty liver disease, chronic hepatitis B or C virus, or autoimmune hepatitis. Elastography measurements, homodyned K-distribution parametric maps, and total attenuation coefficient slope were recorded. Random forests classification and bootstrapping were used to identify combinations of parameters that provided the highest diagnostic accuracy. Receiver operating characteristic (ROC) curves were computed. For classification of steatosis grade S0 vs. S1-3, S0-1 vs. S2-3, S0-2 vs. S3, area under the receiver operating characteristic curve (AUC) were respectively 0.60, 0.63, and 0.62 with elasticity alone, and 0.90, 0.81, and 0.78 with the best tested model combining QUS and elastography features. For classification of inflammation grade A0 vs. A1-3, A0-1 vs. A2-3, A0-2 vs. A3, AUCs were respectively 0.56, 0.62, and 0.64 with elasticity alone, and 0.75, 0.68, and 0.69 with the best model. For classification of liver fibrosis stage F0 vs. F1-4, F0-1 vs. F2-4, F0-2 vs. F3-4, F0-3 vs. F4, AUCs were respectively 0.66, 0.77, 0.72, and 0.74 with elasticity alone, and 0.72, 0.77, 0.77, and 0.75 with the best model. Random forest models incorporating QUS and shear wave elastography increased the classification accuracy of liver steatosis, inflammation, and fibrosis when compared to shear wave elastography alone.
Highlights
Chronic liver disease (CLD) is one of the top ten leading causes of death in the United States [1]
Nonalcoholic fatty liver disease (NAFLD) is characterized by vacuoles of fat and may lead to nonalcoholic steatohepatitis (NASH), which is characterized by inflammation
Estimation was based on a hybrid state-of-the-art method [30]. For both total and local attenuation estimation methods, quantitative ultrasound (QUS) and machine learning for assessment chronic liver disease a calibrated reference phantom was used to compensate for acquisition settings [8]
Summary
Chronic liver disease (CLD) is one of the top ten leading causes of death in the United States [1]. Nonalcoholic fatty liver disease (NAFLD) is the most common cause of CLD, affecting up to one third of the adult Western population [2]. NAFLD is characterized by vacuoles of fat and may lead to nonalcoholic steatohepatitis (NASH), which is characterized by inflammation. All causes of CLD may evolve to liver fibrosis, a scarring process, which may progress to cirrhosis and liver failure. Liver biopsy is the established reference standard for classification of steatosis grade, inflammation grade, and fibrosis stage, it has several limitations including cost, sampling error, and procedure-related morbidity and mortality [4]
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