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Differential diagnosis of parkinsonism early upon symptom onset is often challenging for clinicians and stressful for patients. Several neuroimaging methods have been previously evaluated; however specific routines remain to be established. The aim of this study was to systematically assess the diagnostic accuracy of a previously developed 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) based automated algorithm in the diagnosis of parkinsonian syndromes, including unpublished data from a prospective cohort. A series of 35 patients prospectively recruited in a movement disorder clinic in Stockholm were assessed, followed by systematic literature review and meta-analysis. In our cohort, automated image-based classification method showed excellent sensitivity and specificity for Parkinson Disease (PD) vs. atypical parkinsonian syndromes (APS), in line with the results of the meta-analysis (pooled sensitivity and specificity 0.84; 95% CI 0.79–0.88 and 0.96; 95% CI 0.91 –0.98, respectively). In conclusion, FDG-PET automated analysis has an excellent potential to distinguish between PD and APS early in the disease course and may be a valuable tool in clinical routine as well as in research applications.

Parkinson’s disease (PD) is the second more common neurodegenerative disease with increasing incidence worldwide associated to the population ageing. Despite increasing awareness and significant research advancements, treatment options comprise dopamine repleting, symptomatic therapies that have significantly increased quality of life and life expectancy, but no therapies that halt or reverse disease progression, which remain a great, unmet goal in PD research. Large biomarker development programs are undertaken to identify disease signatures that will improve patient selection and outcome measures in clinical trials. In this review, we summarize PD-related mechanisms that can serve as targets of therapeutic interventions aiming to slow or modify disease progression, as well as previous and ongoing clinical trials in each field, and discuss future perspectives.