Abstract #2990

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

Ying Zhao¹, Han Wen^2,3, Ailian Liu¹, Yu Yao^2,3, 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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