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Abstract #1149

Classification of Spinal Metastases Coming from Different Primary Cancer Origin by Using Quantitative Radiomics Analysis with Multi-Class SVM

Yongye Chen1, Yang Zhang2, Enlong Zhang1, Xiaoying Xing1, Qizheng Wang1, Huishu Yuan1, Min-Ying Su2, and Ning Lang1
1Department of Radiology, Peking University Third Hospital, Beijing, China, 2Department of Radiological Sciences, University of California, Irvine, CA, United States

For patients suspected to have spinal metastasis, a confirmed pathological diagnosis is needed to proceed with appropriate treatment. This study applied quantitative radiomics to differentiate 5 groups of patients with metastatic cancers in the spine, including 28 lung, 11 breast, 7 kidney, 11 prostate and 18 thyroid. The analysis was done on post-contrast images. A total of 107 features, including 32 first order and 75 texture, were extracted for each case by using PyRadiomics. The group differentiation was done by using multi-class support vector machine (SVM). The overall accuracy was 80%, with the highest accuracy of 27/28=96% for lung mets.

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