Abstract #1893

On Differentiation of Pilocytic Astrocytoma from High-Grade-Glioma Tumor using Machine Learning Based upon Quantitative T1 Perfusion MRI

Anup Singh^1,2, Neha Vats¹, Virendra Kumar Yadav¹, Anirban Sengupta³, Rakesh Kumar Gupta⁴, Sumeet Agarwal⁵, Mamta Gupta⁶, Rana Patir⁷, Sunita Ahlawat⁶, and Jitender Saini⁸

¹Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ²Biomedical Engineering, AIIMS, New Delhi, India, ³Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, ⁵Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India, ⁶Fortis Memorial Research Institute, Gurugram, India, ⁷Neurosurgery, Fortis Memorial Research Institute, Gurugram, India, ⁸Department of Neuroimaging and Interventional Radiology (NIIR), NIMHANS Bangalore, Bangalore, India

Imaging based diagnosis of Pilocytic Astrocytoma (PA) is quite important for better prognosis. PA can easily be misdiagnosed since its location, growth pattern, and contrast enhancement often mimic a more aggressive high-grade glioma(HGG) tumor. In the current study, quantitative analysis of T1-Perfusion(DCE) MRI data was performed followed by extraction of various features from tumor region and development of an optimized support-vector-machine(SVM) classifier for automatic differentiation of PA vs HGG. The proposed machine learning based approach which uses features derived from quantitative T1 perfusion MRI and tumor volume fraction can enable accurate diagnosis of PA and HGG tumors.

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