Self-supervised multi-modal feature fusion for predicting early recurrence of hepatocellular carcinoma

Abstract

Surgical resection stands as the primary treatment option for early-stage hepatocellular carcinoma (HCC) patients. Postoperative early recurrence (ER) is a significant factor contributing to the mortality of HCC patients. Therefore, accurately predicting the risk of ER after curative resection is crucial for clinical decision-making and improving patient prognosis. This study leverages a self-supervised multi-modal feature fusion approach, combining multi-phase MRI and clinical features, to predict ER of HCC. Specifically, we utilized attention mechanisms to suppress redundant features, enabling efficient extraction and fusion of multi-phase features. Through self-supervised learning (SSL), we pretrained an encoder on our dataset to extract more generalizable feature representations. Finally, we achieved effective multi-modal information fusion via attention modules. To enhance explainability, we employed Score-CAM to visualize the key regions influencing the model’s predictions. We evaluated the effectiveness of the proposed method on our dataset and found that predictions based on multi-phase feature fusion outperformed those based on single-phase features. Additionally, predictions based on multi-modal feature fusion were superior to those based on single-modal features.