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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.