Deep Fusion Models of Multi-Phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

Abstract

Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is one of the leading causes of death. The prediction of the ER of HCC before treatment contributes to guiding treatment and follow-up protocols. In recent years, CT radiomics signatures have been proven effective in several studies in predicting early recurrence of HCC, there are still two major challenges. First, the radiomics features extracted were low or mid-level features, which may not fully characterize HCC heterogeneity. Second, the fusion approach of clinical textual data and image information is in little consensus. In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical model. The accuracy and area under the curve (AUC) of receiver operating characteristics of three models was 69.52%/0.723, 67.04%/0.64, 76.03%/0.75, respectively. In addition, we proposed four fusion models to combine clinical data and high-level features. Among them, Fusion model D performed best, achieving a higher prediction accuracy of 78.66% and AUC of 0.8248. Moreover, fusion models with a joint loss function can further improve the prediction performance to 80.49% and 0.8331.

Keywords

• Hepatocellular carcinoma
• early recurrence
• deep learning
• multi-phase CT images
• clinical data
• fusion model