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.