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Abstract #3250

Differentiating hemorrhage and vasculature ITSS in SWI-magnitude images in intracranial Glioma: machine-learning and radiomic based approach

Rupsa Bhattacharjee1,2, Rakesh Kumar Gupta3, Suhail P Parvaze4, Rana Patir5, Sandeep Vaishya5, Sunita Ahlawat6, and Anup Singh1,7
1Center for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, India, 2Philips Health Systems, Philips India Limited, Gurugram, India, 3Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, 4Philips Health Systems, Philips Innovation Campus, Bangalore, India, 5Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India, 6SRL Diagnostics, Gurugram, India, 7Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

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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