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Abstract #4709

Quantitative assessment of Nigrosome-1 volume and susceptibility in Parkinson’s Disease

Marida De Maria1, Ilaria Chimento1, Umberto Sabatini2, Maria Celeste Bonacci1, Jolanda Buonocore2, Federica Aracri1, Aldo Quattrone1, Andrea Quattrone1,2, and Maria Eugenia Caligiuri1
1Department of Medical and Surgical Sciences, Neuroscience Research Center, Magna Graecia University, Catanzaro, Italy, 2Department of Medical and Surgical Sciences, Institute of Neurology, Magna Græcia University of Catanzaro, Catanzaro, Italy

Synopsis

Keywords: Parkinson's Disease, Analysis/Processing, Nigrosome, QSM, ML

Motivation: Parkinson’s disease diagnosis, in early stages, still strongly relies on qualitative clinical evaluation, rather than quantitative data, often resulting in misdiagnosis.

Goal(s): The goal was to identify PD patients when Nigrosome-1 is still visible on MRI imaging

Approach: We extracted quantitative structural data of Nigrosome-1 and trained a Machine Learning model to perform a classification task

Results: Quantitative features, Volume of Nigrosome-1 in particular, proved to be a good feature to differentiate PD from HC, performing 0.87 accuracy, and 0.94 AUC-ROC

Impact: These results support the need to integrate visual assessment of N1 with a quantitative assessment of its structure and susceptibility properties to better characterize PD pathology

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Keywords