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Abstract #1652

Using convolutional neural network predicts microvascular invasion in hepatocellular carcinoma based on Gd-EOB-DTPA-enhanced MRI

Baoer Liu1, Pingjing Wang2, Jianbin Huang1, Wu Zhou2, and Yikai Xu1
1Nanfang Hospital, Southern Medical University, Guangzhou, China, 2Guangzhou University of Chinese Medicine, Guangzhou, China

Synopsis

Keywords: Liver, Cancer, Gd-EOB-DTPA-enhanced MRIAn accurate preoperative assessment of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is of great clinical importance in choosing appropriate surgical interventions. We aimed to investigate diagnostic performance of Gd-EOB-DTPA-enhanced MRI for prediction of MVI in HCC using convolutional neural network (CNN). The CNN model based on hepatobiliary phase (HBP) images had great diagnostic efficiency for the prediction of MVI with the AUC of 0.858 (range, 0.854, 0.893). Deep learning with CNN based on Gd-EOB-DTPA-enhanced MRI can be conducive to preoperative prediction of MVI in HCC.

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