Intra-tumoral-susceptibility-signal (ITSS) has been increasingly proven to play a major role in glioma grading, progression assessment and follow-up. Quantitative ITSS assessment involves segmentation of ITSS from SWI images, separating vasculature ITSS from hemorrhage ITSS and finally quantifying the ITSS-vasculature-volume (IVV) to grade the glioma non-invasively. This study involves radiomic feature extraction, random-forest based feature selection and classification to indicate that radiomic features can significantly differentiate between 3Dvasculature and 3DHemorrhage mask regions in SWI-magnitude images. This is also one of the first studies that explores the vasculature and hemorrhage radiomic properties extracted from SWI-magnitude images through machine-learning in grade-IV GBM patients.
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