The cerebellum and its connections (cerebellar peduncles) have been implicated to play a significant role in the pathogenesis of essential tremor (ET). However, these abnormalities may not be grossly evident on basic structural imaging. To this end, we employ radiomics on 3D-T1 weighted images to capture subtle features of pathology and use it in a machine learning framework to deliniate patients with ET. We demonstrate a test accuracy of 83% with radiomics features from dentate nucleus contributing the most, followed by the right V. It thus suggests the potential utility of radiomics features from these structures for diagnosis of ET.
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