Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-123-FP-CIT indices

ObjectivesTo provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative I-123-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis.Methods(123)I-FP-CIT SPECT was performed at a single-center level on 370 individuals with PS, including 280 patients with Parkinson's disease (PD), 21 with multiple system atrophy-parkinsonian type (MSA-P), 41 with progressive supranuclear palsy (PSP) and 28 with corticobasal syndrome (CBS) (mean age 70.3years, 47% female, mean disease duration at scan 1.4year), as well as 208 age- and gender-matched control subjects. Striatal volumes-of-interest (VOIs) uptake, VOIs asymmetry indices (AIs) and caudate/putamen (C/P) ratio were used as input for SVM individual classification using fivefold cross-validation.ResultsUnivariate analyses showed significantly lower VOIs uptake, higher striatal AI and C/P ratio for each PS in comparison to controls (all p70% accuracy. Overall, striatal AI and C/P ratio on the more affected side had the highest weighting factors.ConclusionSemiquantitative I-123-FP-CIT SPECT striatal evaluation combined with SVM represents a promising approach to disentangle PD from non-degenerative conditions and from atypical PS at the early stage.