The accurate non-invasive staging of liver fibrosis using deep learning radiomics based on transfer learning of shear wave elastography

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.