Abstract
BACKGROUND & AIMS: The evaluation of the stage of liver fibrosis is essential in patients with chronic liver disease. However, due to the low quality of ultrasound images, the non-invasive diagnosis of liver fibrosis based on ultrasound images is still an outstanding question. This study aimed to investigate the diagnostic accuracy of a deep learning-based method in ultrasound images for liver fibrosis staging in multicentre patients. METHODS: In this study, we proposed a novel deep learning-based approach, named multi-scale texture network (MSTNet), to assess liver fibrosis, which extracted multi-scale texture features from constructed image pyramid patches. Its diagnostic accuracy was investigated by comparing it with APRI, FIB-4, Forns and sonographers. Data of 508 patients who underwent liver biopsy were included from 4 hospitals. The area-under-the ROC curve (AUC) was determined by receiver operating characteristics (ROC) curves for significant fibrosis (≥F2) and cirrhosis (F4). RESULTS: The AUCs (95% confidence interval) of MSTNet were 0.92 (0.87-0.96) for ≥F2 and 0.89 (0.83-0.95) for F4 on the validation group, which significantly outperformed APRI, FIB-4 and Forns. The sensitivity and specificity of MSTNet (85.1% (74.5%-92.0%) and 87.6% (78.0%-93.6%)) were better than those of three sonographers in assessing ≥F2. CONCLUSIONS: The proposed MSTNet is a promising ultrasound image-based method for the non-invasive grading of liver fibrosis in patients with chronic HBV infection.