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Abstract #1144

Comparison of Radiomics and Deep Learning for Differentiation of Spinal Metastases Coming from Lung Cancer and Other Primary Cancers

Yang Zhang1, Ning Lang2, Enlong Zhang2, Jiahui Zhang2, Daniel Chow1, Peter Chang1, Hon J. Yu1, Huishu Yuan2, and Min-Ying Lydia Su1

1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiology, Peking University Third Hospital, Beijing, China

For patients found to have spinal metastasis, a confirmed pathological diagnosis is needed to proceed with appropriate treatment. This study compared ROI analysis, radiomics, and deep learning for differentiation of primary cancer coming from 30 lung and 31 other tumors. Radiomics using GLCM texture and histogram parameters from the segmented 3D tumor achieved accuracy of 0.71, while the deep learning using recurrent CLSTM network with the entire 12 sets of DCE images reached an accuracy of 0.81. The wash-out slope in DCE kinetics measured from hot-spot was the best diagnostic parameter, which could be easily performed in a clinical setting.

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