Purpose: We developed the deep learning Radiomics of elastography (DLRE) as a noninvasive method to assess liver fibrosis stages,which is essential for prognosis, surveillance of chronic hepatitis B (CHB) patients.

Methods and Materials: 402 patients were prospectively enrolled from 12 hospitals, and finally 2010 images were included into analysis randomly.DLRE adopted the CNN method, 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. Analysis of receiver operating characteristic (ROC) curve were performed to calculate optimal area under it (AUC) for cirrhosis (F4), advanced fibrosis (≥F3), and significance fibrosis (≥F2) for all diagnostic approaches.

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).

Conclusion: DLRE shows the best overall performance in predicting liver fibrosis stages comparing with 2D-SWE, TE, and serological examinations. It is valuable and practical for popularized application of noninvasive diagnosis of liver fibrosis stages in HBV infected patients.

Citation Format: Hui Zhou, Kun Wang, Jie Tian. The accurate noninvasive staging of liver fibrosis using deep learning radiomics: A prospective multicenter study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5298.