Abstract
Parkinsonism has heterogeneous nature, showing distinctive patterns of disease progression and prognosis. We aimed to find clusters of parkinsonism based on 18 F-fluoropropyl-carbomethoxyiodophenylnortropane (FP-CIT) PET as a data-driven approach to evaluate heterogenous dopaminergic neurodegeneration patterns. Two different cohorts of patients who received FP-CIT PET were collected. A labeled cohort (n = 94) included patients with parkinsonism who underwent a clinical follow-up of at least 3 years (mean 59.0 ± 14.6 months). An unlabeled cohort (n = 813) included all FP-CIT PET data of a single-center. All PET data were clustered by a dimension reduction method followed by hierarchical clustering. Four distinct clusters were defined according to the imaging patterns. When the diagnosis of the labeled cohort of 94 patients was compared with the corresponding cluster, parkinsonism patients were mostly included in two clusters, cluster "0" and "2." Specifically, patients with progressive supranuclear palsy were significantly more included in cluster 0. The two distinct clusters showed significantly different clinical features. Furthermore, even in PD patients, two clusters showed a trend of different clinical features. We found distinctive clusters of parkinsonism based on FP-CIT PET-derived heterogeneous neurodegeneration patterns, which were associated with different clinical features. Our results support a biological underpinning for the heterogeneity of neurodegeneration in parkinsonism.
Keywords: 18F-FP-CIT PET; Parkinson's disease; clustering; parkinsonism; unsupervised learning.
Authors
Suh M, Im JH, Choi H, Kim HJ, Cheon GJ, Jeon B.