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Abstract #0880

3D Convolutional Neural Network with Contrast-enhanced MR for Microvascular invasion prediction of hepatocellular carcinoma

Wu Zhou1, Yaoqin Xie2, and Guangyi Wang3

1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, 2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 3Department of Radiology, Guangdong General Hospital, Guangzhou, China

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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