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Abstract #2990

Deep Learning 3D Convolutional Neural Network for Noninvasive Evaluation of Pathologic Grade of HCC Using Contrast-enhanced MRI

Ying Zhao1, Han Wen2,3, Ailian Liu1, Yu Yao2,3, Tao Lin1, Qingwei Song1, Xin Li4, Yan Guo4, and Tingfan Wu4
1The First Affiliated Hospital of Dalian Medical University, Dalian, China, 2Chengdu Institute of CoChinese Academy of Sciences, Chengdu, China, 3University of Chinese Academy of Sciences, Beijing, China, 4GE Healthcare (China), Shanghai, China

In recent years, convolutional neural networks (CNNs) have become one of the most advanced deep learning networks. Deep learning with CNNs has reportedly achieved good performance in the pattern recognition of images. In the present study, 3D-CNN based on contrast-enhanced (CE)-MR images was demonstrated to be capable to evaluate pathologic grade of hepatocellular carcinoma (HCC) treated with surgical resection, which will provide more prognostic information and facilitate clinical management.

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