Abstract #0259

Exploiting radiogenomics data for personalised prediction of glioblastoma

Paul Blakeley^1,2, Chia-Feng Lu^2,3,4, Fei-Ting Hsu^2,5, Li-Chun Hsieh^2,5, Yu-Chieh Jill Kao^2,3, Huai-Lu Chen^1,2, Ping-Huei Tsai^2,3,5, Hua-Shan Liu^2,6, Gilbert Aaron Lee^1,2, and Cheng-Yu Chen^2,3,5

¹Department of Medical Research, Taipei Medical University Hospital, Taipei, Taiwan, ²Translational Imaging Research Center, College of Medicine, Taipei Medical University, Taipei, Taiwan, ³Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ⁴Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan, ⁵Department of Medical Imaging, Taipei Medical University Hospital, Taipei, Taiwan, ⁶School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan

The present study demonstrates the feasibility of machine learning in radiogenomics to predict patient outcome. The Random Forest Survival model is able to predict patient survival based on apparent diffusion coefficients or gene expression data without any prior knowledge.

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