Abstract #2660

ITSS vasculature volume and Radiomics Features from MR SWI: Performance in Glioma Grading

Rupsa Bhattacharjee^1,2, Rakesh Kumar Gupta³, Rana Patir⁴, Sandeep Vaishya⁴, Suneeta Ahlawat⁵, and Anup Singh^1,6

¹Center for Biomedical Engineering, Indian Institute of Technology (IIT), Delhi, New Delhi, India, ²Department of Radiology & Biomedical Engineering, University of California, San Francisco (UCSF), San Francisco, CA, United States, ³Department of Radiology and Imaging, Fortis Memorial Research Institute, Gururam, India, ⁴Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India, ⁵SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, India, ⁶Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

Synopsis

Majority of the glioma studies rely on using radiomic-features extracted only from conventional MRI and noticeably limited in SWI. Main objective of the current study is to evaluate the role of SWI in glioma grading aided by machine-learning (ML) methods, in terms of ITSS-vasculature-volume(IVV), radiomic features and by combining both. We conclude SWI to be the one of the useful sequences in the glioma-analysis as it contributes to calculate the IVV, which can singlehandedly improve the glioma grading. When IVV is combined with radiomic-analysis used in combination with PCA feature reduction and RF ML classifier, the performance is marginally improved.

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