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Abstract #0013

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

Chien-Yi Liao1, Cheng-Chia Lee2,3,4, Huai-Che Yang2,3, Wen-Yuh Chung2,3, Hsiu-Mei Wu3,5, Wan-Yuo Guo3,5, Ren-Shyan Liu1,6,7, and Chia-Feng Lu1,8
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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