Deep learning-based radiomics based on contrast-enhanced ultrasound predicts early recurrence and survival outcome in hepatocellular carcinoma

BACKGROUND Hepatocellular carcinoma (HCC) is the most common primary liver malignancy. AIM To predict early recurrence (ER) and overall survival (OS) in patients with HCC after radical resection using deep learning-based radiomics (DLR). METHODS A total of 414 consecutive patients with HCC who underwent surgical resection with available preoperative grayscale and contrast-enhanced ultrasound images were enrolled. The clinical, DLR, and clinical + DLR models were then designed to predict ER and OS. RESULTS The DLR model for predicting ER showed satisfactory clinical benefits [area under the curve (AUC)] = 0.819 and 0.568 in the training and testing cohorts, respectively), similar to the clinical model (AUC = 0.580 and 0.520 in the training and testing cohorts, respectively; P > 0.05). The C-index of the clinical + DLR model in the prediction of OS in the training and testing cohorts was 0.800 and 0.759, respectively. The clinical + DLR model and the DLR model outperformed the clinical model in the training and testing cohorts (P < 0.001 for all). We divided patients into four categories by dichotomizing predicted ER and OS. For patients in class 1 (high ER rate and low risk of OS), retreatment (microwave ablation) after recurrence was associated with improved survival (hazard ratio = 7.895, P = 0.005). CONCLUSION Compared to the clinical model, the clinical + DLR model significantly improves the accuracy of predicting OS in HCC patients after radical resection.