Abstract #0013

Using MR Radiomics to Improve Prediction of Local Tumor Control after Radiosurgery in Brain Metastases

Chien-Yi Liao¹, Cheng-Chia Lee^2,3,4, Huai-Che Yang^2,3, Wen-Yuh Chung^2,3, Hsiu-Mei Wu^3,5, Wan-Yuo Guo^3,5, Ren-Shyan Liu^1,6,7, and Chia-Feng Lu^1,8

¹Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, Taipei, Taiwan, ²Department of Neurosurgery, Neurological Institute, Taipei Veteran General Hospital, Taipei, Taiwan, Taipei, Taiwan, ³School of Medicine, National Yang-Ming University, Taipei, Taiwan, Taipei, Taiwan, ⁴Brain Research Center, National Yang-Ming University, Taipei, Taiwan, Taipei, Taiwan, ⁵Department of Radiology, Taipei Veteran General Hospital, Taipei, Taiwan, Taipei, Taiwan, ⁶Department of Medical Imaging, Cheng-Hsin General Hospital, Taipei, Taiwan, Taipei, Taiwan, ⁷Molecular and Genetic Imaging Core, Taiwan Animal Consortium, Taipei, Taiwan, Taipei, Taiwan, ⁸Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan, Taipei, Taiwan

Patients with non-small cell lung cancer have a high probability to develop brain metastasis during the course of the disease. The prediction of treatment response after Gamma Knife stereotactic radiosurgery (GKRS) can benefit patient management. In addition to the clinically available information (Karnofsky performance status, number of tumors, tumor volume, and primary tumor control), we proposed an MR radiomics approach to provide added values to predict the local tumor control after GKRS. We suggested that imaging characteristics extracted from preradiosurgical MRIs combined with clinical information can effectively predict local tumor control.

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