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Abstract #3268

Delineating parkinsonian disorders using T1-weighted MRI based radiomics

Priyanka Tupe Waghmare1, Archith Rajan2, Shweta Prasad3, Jitender Saini4, Pramod Kumar Pal5, and Madhura Ingalhalikar6
1E &TC, Symbiosis Institute of Technology, Pune, India, 2Symbiosis Centre for Medical Image Analysis, Symbiosis Centre for Medical Image Analysis, Pune, India, 3Department of Clinical Neurosciences and Neurology, National Institute of Mental Health & Neurosciences, Bangalore, India, 4Department of Neuroimaging & Interventional Radiology, National Institute of Mental Health & Neurosciences, Bangalore, India, 5Department of Neurology, National Institute of Mental Health & Neurosciences, Bangalore, India, 6Symbiosis Center for Medical Image Analysis and Symbiosis Institute of Technology, Pune, India

Parkinson’s disease (PD), multiple system atrophy (MSA), and progressive supra-nuclear palsy (PSP) are neurodegenerative disorders which have parkinsonism as a core clinical feature. In the early stages PD and atypical parkinsonian syndrome (APS) (MSA and PSP) may often be indistinguishable and differential diagnosis is therefore crucial. Our work employs radiomics based features extracted from standard T1 weighted MRI images that are used in a machine learning framework to differentiate PD from APS. Results demonstrate a superior test accuracy of 92% that support our underlying hypothesis that radiomics on T1-weighted images can provide a discriminatory feature space between PD and APS.

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