Background: We developed the deep learning Radiomics of elastography (DLRE) which adopted Convolutional Neural
Network (CNN) based on transfer learning as a noninvasive method to assess liver fibrosis stages, which is essential for
prognosis, surveillance of chronic hepatitis B (CHB) patients. Methods: 297 patients were prospectively enrolled from 4
hospitals, and finally 1485 images were included into analysis randomly. DLRE adopted the Convolutional Neural
Network (CNN) based on transfer learning, one of the deep learning radiomic techniques, for the automatic analysis of
2D-SWE images. This study was conducted to assess the accuracy of DLRE in comparison with 2D-SWE, transient
elastography (TE), transaminase-to-platelet ratio index (APRI), and fibrosis index based on the four factors (FIB-4), by
using liver biopsy as the gold standard. Results: AUCs of DLRE were both 0.98 for cirrhosis (95% confidence interval
[CI]: 0.95-0.99) and advanced fibrosis (95% CI: 0.94-0.99), which were significantly better than other methods, as well
as 0.76 (95% CI: 0.72-0.81) for significance fibrosis (significantly better than APRI and FIB-4). Conclusions: DLRE
shows the best overall performance in predicting liver fibrosis stages comparing with 2D-SWE, TE, and serological
examinations.