Abstract
The aim of this study was to investigate whether visual assessment of (123)I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropan ((123)I-FP-CIT) single photon emission computed tomography (SPECT) in addition to quantitative analyses can help to differentiate idiopathic Parkinson's disease (PD) from atypical parkinsonian syndromes (APS). From a consecutive series of patients examined with (123)I-FP-CIT SPECT (n = 190) over a three-year period we identified 165 patients with a clinical diagnosis of PD (n = 120) or APS (n = 45). (123)I-FP-CIT SPECT results were analysed visually and quantitatively and compared for PD and APS and for the subgroup of patients with early PD and APS (disease duration <5 years). According to predefined visual patterns of dopaminergic degeneration the results were graded as normal (grade 5) or abnormal (grade 1-4), distinguishing a posterior-anterior degeneration pattern ("egg shape") from a global and severe degeneration pattern ("burst striatum"). Visual assessment of (123)I-FP-CIT SPECT showed significant different dopaminergic degeneration patterns for PD and APS patients. A grade 1 ("burst striatum") degeneration pattern was predominantly associated with APS patients. In contrast to that, a grade 2 (egg shape) degeneration pattern was the characteristic finding in PD patients. In a subgroup of patients with early disease, visual assessment with identification of the burst striatum degeneration pattern provided 90% positive predictive value and 99% specificity for the diagnosis of APS. Quantitative analysis of striatal binding ratios failed to depict these different degeneration patterns in PD and APS patients. Visual assessment of the pattern of dopaminergic loss in (123)I-FP-CIT SPECT shows different patterns of dopaminergic degeneration for PD and APS patients. Therefore, it could provide valuable information to distinguish APS from PD patients, especially in early stages of disease. Within the first 5 years of disease, the occurrence of a burst striatum degeneration pattern has a high positive predictive value of APS.
Published Version
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