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 different HCC prediction models and to overcome challenges of an unbalanced sample. The best model which is simple with high accuracy was selected. Prediction performance of the selected model was compared with LSM and other clinical prognostic scores. During 5-year follow-up, 32 (7.4%) of 434 patients developed HCC. The best prediction model was HCC-R, which included 2D-SWE and B-mode US images, sex and age. This model showed a high predictive value with areas under the receiver operating characteristic curve (AUCs) of 0.981, 0.942 and 0.900 in training, validation and testing cohorts for predicting 5-year prognosis of HCC. These predictive values were significantly higher than that of LSM (AUC 0.676~0.784, p < 0.05) and better than that of other clinical prognostic scores (AUC 0.544~0.869). HCC-R radiomics model based on 2D-SWE and B-mode US images, sex and age comprehensively reflected biomechanical and morphological information of patients and can accurately predict HCC occurrence; thus, this model has great value for treatment and follow-up of CHB patients.