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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