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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.

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