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 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 Liu⁶, and Chia-Feng Lu^1,7

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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