Purpose: Human post mortem studies have described the topographical patterns of tau pathology in progressive supranuclear palsy (PSP). Recent advances in tau PET tracers are expected to herald the next era of PSP investigation for early detection of tau pathology in living brains. This study aimed to investigate whether ¹⁸F-Florzolotau PET imaging may capture the distribution patterns and regional vulnerability of tau pathology in PSP, and to devise a novel image-based staging system. Methods: The study cohort consisted of 148 consecutive patients with PSP who had undergone ¹⁸F-Florzolotau PET imaging. The PSP rating scale (PSPrs) was used to measure disease severity. Similarities and differences of tau deposition among different clinical phenotypes were examined at the regional and voxel levels. An ¹⁸F-Florzolotau pathological staging system was devised according to the scheme originally developed for post mortem data. In light of conditional probabilities for the sequence of events, an ¹⁸F-Florzolotau modified staging system by integrating clusters at the regional level was further developed. The ability of ¹⁸F-Florzolotau staging systems to reflect disease severity in terms of PSPrs score was assessed by analysis of variance. Results: The distribution patterns of ¹⁸F-Florzolotau accumulation in living brains of PSP showed a remarkable similarity to those reported in post mortem studies, with the binding intensity being markedly higher in Richardson’s syndrome. Moreover, ¹⁸F-Florzolotau PET imaging allowed detecting regional vulnerability and tracking tau accumulation in an earlier fashion compared with post mortem immunostaining. The ¹⁸F-Florzolotau staging systems were positively correlated with clinical severity as reflected by PSPrs scores. Conclusions: ¹⁸F-Florzolotau PET imaging can effectively capture the distribution patterns and regional vulnerability of tau pathology in PSP. The ¹⁸F-Florzolotau modified staging system holds promise for early tracking of tau deposition in living brains.

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson’s disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94–0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87–0.93) for glucose metabolism (¹⁸F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 − 0.95) for presynaptic DA, 0.79 (95% CI: 0.75–0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96–0.99) for ¹⁸F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

A new Parkinson’s disease (PD) subtyping model has been recently proposed based on the initial location of α-synuclein inclusions, which divides PD patients into the brain-first subtype and the body-first subtype. Premotor RBD has proven to be a predictive marker of the body-first subtype. We found compared to PD patients without possible RBD (PD^pRBD–, representing the brain-first subtype), PD patients with possible premotor RBD (PD^pRBD+, representing the body-first subtype) had lower Movement Disorders Society Unified Parkinson’s Disease Rating Scale part III (MDS UPDRS-III) score (p = 0.022) at baseline but presented a faster progression rate (p = 0.009) in MDS UPDRS-III score longitudinally. The above finding indicates the body-first subtype exhibited a faster disease progression in motor impairments compared to the brain-first subtype and further validates the proposed subtyping model.