Lancet: FDG-PET could distinguish between Parkinsonian disorders

Twitter icon
Facebook icon
LinkedIn icon
e-mail icon
Google icon

FDG-PET imaging-based classification has high specificity to differentiate individual patients with idiopathic Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy and could help in selecting treatment for early-stage patients and identifying participants for clinical trials, according to research published online Jan. 11 in Lancet Neurology.

David Eidelberg, MD, director of the center for neurosciences at the Feinstein Institute for Medical Research in Manhasset, N.Y., and colleagues assessed whether metabolic brain imaging combined with spatial covariance analysis could accurately differentiate between patients with Parkinsonism who had different underlying disorders.

In the study, 167 patients who had Parkinsonian features but uncertain clinical diagnosis had an 18F-FDG PET scan. The researchers developed an automated image-based classification procedure to differentiate individual patients with Parkinsonian disorders and the accuracy was assessed by comparison with the final clinical diagnosis.

Eidelberg said that out of the 167 patients assessed, image-based classification for idiopathic Parkinson’s disease had 84 percent sensitivity, 97 percent specificity, 98 percent positive predictive value (PPV) and 82 percent negative predictive value (NPV).

Eidelberg and colleagues found that imaging classifications were also accurate for multiple system atrophy (85 percent sensitivity, 96 percent specificity, 97 percent PPV, and 83 percent NPV) and progressive supranuclear palsy (88 percent sensitivity, 94 percent specificity, 91 percent PPV and 92 percent NPV).

In an accompanying commentary, Angelo Antonini, MD, at IRCCS San Camillo, Venice and Parkinson Institute in Milan, Italy, wrote that the "clinical and research relevance of these findings should not be underestimated. Neuroprotective and disease-modifying drug research is intensifying and results mostly rely on accurate early diagnosis."

"The excellent specificity and PPV of the imaging classification makes this test suitable for diagnostic use rather than as a screening tool," Eidelberg noted.

"Although imaging might be cost effective for early diagnosis, I expect that these procedures will find their natural application in the identification of suitable candidates for drug trials or complex surgical procedures (example, deep brain stimulation, stem-cell transplantation or fetal tissue transplantation). However, additional blinded, prospective, multicenter studies will first be needed to confirm the accuracy of this pattern-based categorization procedure," Antonini concluded.