Microvascular invasion (MVI) of Hepatocellular carcinoma (HCC) is a crucial histopathologic prognostic factor leading to recurrence after liver transplantation or hepatectomy, and preoperative prediction of MVI is significant in clinical practice. In this work, we propose a deep learning framework based on 3D Convolutional Neural Network (CNN) to extract discriminative information from HCCs in Contrast-enhanced MR images for MVI prediction. Experimental results demonstrated that the proposed deep learning framework could make full use of the spatial and temporal information of HCCs from Contrast-enhanced MR for MVI prediction, outperforming the radiomics based approach and the method of 3D deep feature concatenation.
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