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Abstract #0943

Evaluating the cytokeratin 19 (CK-19) status via neural network model established by the SWI-derived radiomics features 

Zhijun Geng1, Yunfei Zhang2, Chuanmiao Xie1, and Yongming Dai2
1Sun Yat-sen University Cancer Center, Guangzhou, China, 2Central Research Institute, United Imaging Healthcare, Shanghai, China

Susceptibility Weighted Imaging (SWI) has shown tremendous clinical significance for identifying the hepatic micro-structural abnormalities such as micro-bleeding, vascularity, nodule and so forth. Recent studies concluded that cytokeratin 19 (CK-19) is an important marker for prognostic prediction of hepatocellular carcinoma (HCC). We hypothesized that the neural network model can be established by means of extracting high throughput radiomics features from SWI images for noninvasively evaluating the CK-19 status with high accuracy. The results demonstrated that such deep learning based neural network model yielded excellent diagnostic performance for predicting the CK-19 status.

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