Abstract #2036

Investigating relevance of tumor shape features in overall survival prediction of glioblastoma multiforme patients using machine learning and multi-channel MR images

Parita Sanghani¹, Ang Beng Ti², Nicolas Kon Kam King², and Hongliang Ren¹

¹Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore, ²Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore

In this work, we study the impact of combining shape features with texture and volumetric features derived from glioblastoma multiforme (GBM) tumors for overall survival (OS) prediction. A comprehensive set of features were obtained from multichannel MR images of 163 GBM patients. Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was used for feature selection, followed by SVM regression for survival prediction. The shape features used in this study have not yet been used for OS prediction in GBM patients and were found to improve the prediction accuracy.

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.