Abstract #3250

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

Rupsa Bhattacharjee^1,2, Rakesh Kumar Gupta³, Suhail P Parvaze⁴, Rana Patir⁵, Sandeep Vaishya⁵, Sunita Ahlawat⁶, and Anup Singh^1,7

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 3D_vasculature and 3D_Hemorrhage 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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