Abstract #3743
A Deep Survival Model based on MR Radiomics and EGFR status to Predict Overall Survival 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 Liu6, and Chia-Feng Lu1,7
1Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, 2Department of Neurosurgery, Neurological Institute, Taipei Veteran General Hospital, Taipei, Taiwan, 3School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 4Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, 5Department of Radiology, Taipei Veteran General Hospital, Taipei, Taiwan, 6Department of Medical Imaging, Cheng-Hsin General Hospital, Taipei, Taiwan, 7Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei, Taiwan
Synopsis
About 30% of Non-small cell lung cancer (NSCLC) patients develop brain metastases (BMs) during the course of the disease. MR radiomics and EGFR mutation status were reported with the potential to predict the local tumor control of Gamma Knife stereotactic radiosurgery (GKRS). The prediction of overall survival after GKRS can further benefit the management of BM patients. We proposed a deep learning-based model using the clinical information, EGFR mutation status, and MR radiomic features to predict the overall survival after GKRS. We suggested that pre-GKRS MRI characteristics combined with gene and clinical information can improve the prediction of overall survival.
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