Abstract #3279

Using MRI and Radiomics to Predict Pain in a Cohort of Trigeminal Neuralgia Patients Treated With Radiosurgery

Kellen Mulford¹, Sean Moen², Andrew W. Grande², Donald R. Nixdorf³, and Pierre-Francois Van de Moortele¹

¹Center for Magnetic Resonance Imaging, University of Minnesota, Minneapolis, MN, United States, ²Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States, ³Department of Diagnostic and Biological Science, University of Minnesota, Minneapolis, MN, United States

Due to a lack of objective markers, trigeminal neuralgia is difficult to diagnose and classify. This difficulty results in frequent pain recurrences following medication and invasive therapies. In this work, we show that a radiomics based model with features extracted from MRI images can be used to identify painful nerves. Using a cohort of trigeminal neuralgia patients, our predictive model achieves an accuracy of 78% and an AUC of 0.84 in distinguishing between nerves affected and nonaffected by trigeminal neuralgia.

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