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Abstract #1893

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

Anup Singh1,2, 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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