Abstract #3214

A combined deep learning and radiomics model predicts Ki-67 expression in HCC using MRI and multifrequency MR elastography

Xumei Hu¹, Ruokun Li², Jiahao Zhou², Jing Guo³, Ingolf Sack³, Weibo Chen⁴, He Wang⁵, Fuhua Yan², and Chengyan Wang¹

¹Human Phenome Institute,Fudan University, Shanghai, China, ²Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³Department of Radiology, Charité–Universitätsmedizin Berlin, Berlin, Germany, ⁴Philips Healthcare, Shanghai, China, ⁵Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China

Synopsis

In this study, a combined deep learning and radiomics (DLR) approach using six different network architectures was tested and compared for the prediction of high Ki-67 expressions in patients with hepatocellular carcinoma (HCC). The model was based primarily on data from MRI and tomoelastography, a multifrequency MR elastography technique. Xception delivered the best performance and recognized seven prominent features among which four were obtained from tomoelastography. Our findings demonstrated that biomechanical properties, especially viscosity and the fluid behavior of the tumor, are crucial imaging features that are important for imaging-based cancer diagnostics.

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