Deep learning radiomics model accurately predicts hepatocellular carcinoma occurrence in chronic hepatitis B patients: a five-year follow-up.

ABSTRACT: An early and accurate prediction of hepatocellular carcinoma (HCC) is beneficial for individualized treatment and follow-up of chronic hepatitis B (CHB) patients. We aimed to establish a prediction model for HCC by radiomics analysis in CHB patients and compare performance with liver stiffness measurement (LSM) and other clinical prognostic scores. Initially, 1215 patients were included and finally 434 CHB patients with 5-year follow-up were enrolled, 96.3% of them underwent liver biopsy. Deep learning radiomics analysis was performed on 2170 two-dimensional shear wave elastography (2D-SWE) and corresponding B-mode ultrasound (US) images. These high-throughput imaging features were also combined with low-dimensional serological clinical data by deep learning radiomics to establish differen

SUBMITTER: Jin J 

PROVIDER: S-EPMC7868753 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature