Abstract #2273

Automated 3D MRI Decision Tree to guide referral and diagnosis of parkinsonian disorders

Jisoo Kim¹, Andrew S Willett², Grace F Crotty³, Merlyne Mesidor², Camden Bay⁴, Xiaoyin Xu⁵, Lei Qin⁶, Vikram Khurana², and Geoffrey S Young¹

¹Neuroradiology, Brigham and Women's Hospital, Boston, MA, United States, ²Movement Disorders Division, Neurology, Brigham and Women's Hospital, Boston, MA, United States, ³Neurology, Massachusetts General Hospital, Boston, MA, United States, ⁴Center for Clinical Investigation, Brigham and Women's Hospital, Boston, MA, United States, ⁵Diagnostic Radiology, Brigham and Women's Hospital, Boston, MA, United States, ⁶Imaging, Dana Farber Cancer Institute, Boston, MA, United States

Synopsis

For screening and diagnosis of Parkinson disease (PD), progressive supranuclear palsy (PSP), and multiple system atrophy-parkinsonian/cerebellar subtypes (MSA-P/MSA-C), we propose a two-step decision tree using a novel MRI biomarker and volumetric measures. MRIs from 50 MSA-C, 22 MSA-P, 50 PSP, 50 PD, and 172 age/legal-sex-matched control were used for fully automated volumetric analysis of the brain and brainstem. Sensitivity/specificity for diseased vs control was 90.1/90.7%. Then, a comprehensive tree with the diseased group yielded sensitivity/specificity of 81.9/93.0% (MSA), 94.0/88.5% (MSA-C), 54.5/96.0% (MSA-P), 74.0/86.0% (PD), 76.0/98.4% (PSP). We provide a reproducible and specific diagnostic tool for screening and initial differential diagnosis.

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