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
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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