Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics.

Progression of liver fibrosis to cirrhosis, a severe non-reversible process, is one of the most critical risk factors in developing hepatocellular carcinoma and liver failure. Detection of liver fibrosis at an early stage is therefore essential for better patient management. Ultrasound (US) imaging can provide a noninvasive alternative to biopsies. This study evaluates quantitative US texture features to improve early-stage versus advanced liver fibrosis detection. 157 B-mode US images of different liver lobes acquired from early and advanced fibrosis rat cases were used for analysis. 5-6 regions of interest were placed on each image. Twelve quantitative features that describe liver texture changes were extracted from the images, including first-order histogram, run length (RL), and gray level co-occurrence matrix (GLCM). The diagnostic performance of individual features was high with AUC ranging from 0.80 to 0.94. Logistic regression with leave-one-out cross-validation was used to evaluate the performance of the combined features. All features combined showed a slight improvement in performance with AUC = 0.95, sensitivity = 96.8%, and specificity = 93.7%. Quantitative US texture features characterize liver fibrosis changes with high accuracy and can differentiate early from advanced disease. Quantitative ultrasound, if validated in future clinical studies, can have a potential role in identifying fibrosis changes that are not easily detected by visual US image assessments.