FDG-PET for differentiating PD, PSP, and MSA

This article on the use of FDG-PET in differentiating PD, MSA, and PSP was published today in The Lancet Neurology. I haven’t had a chance to wade through all of the article yet but it seems to be one of the better articles we’ve seen lately on PSP and MSA. 167 patients in the NY area participated in the study. Only 9 of these patients have died and donated brain tissue thus far. So I’m not sure how “real” the diagnostic accuracy percentages are. And the diagnosis of CBD was not part of the study.

Below, I’ve copied a HealthImaging.com article on this research as well as the abstract.

Robin


The Lancet Neurology, Early Online Publication, 11 January 2010.

Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis

Chris C Tang MD, Kathleen L Poston MD, Thomas Eckert MD, Andrew Feigin MD, Steven Frucht MD, Mark Gudesblatt MD, Vijay Dhawan PhD, Martin Lesser PhD, Jean-Paul Vonsattel MD, Stanley Fahn MD, David Eidelberg MD

Summary

Background
Idiopathic Parkinson’s disease can present with symptoms similar to those of multiple system atrophy or progressive supranuclear palsy. We aimed to assess whether metabolic brain imaging combined with spatial covariance analysis could accurately discriminate patients with parkinsonism who had different underlying disorders.

Methods
Between January, 1998, and December, 2006, patients from the New York area who had parkinsonian features but uncertain clinical diagnosis had fluorine-18-labelled-fluorodeoxyglucose-PET at The Feinstein Institute for Medical Research. We developed an automated image-based classification procedure to differentiate individual patients with idiopathic Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy. For each patient, the likelihood of having each of the three diseases was calculated by use of multiple disease-related patterns with logistic regression and leave-one-out cross-validation. Each patient was classified according to criteria defined by receiver-operating-characteristic analysis. After imaging, patients were assessed by blinded movement disorders specialists for a mean of 2·6 years before a final clinical diagnosis was made. The accuracy of the initial image-based classification was assessed by comparison with the final clinical diagnosis.

Findings
167 patients were assessed. Image-based classification for idiopathic Parkinson’s disease had 84% sensitivity, 97% specificity, 98% positive predictive value (PPV), and 82% negative predictive value (NPV). Imaging classifications were also accurate for multiple system atrophy (85% sensitivity, 96% specificity, 97% PPV, and 83% NPV) and progressive supranuclear palsy (88% sensitivity, 94% specificity, 91% PPV, and 92% NPV).

Interpretation
Automated image-based classification has high specificity in distinguishing between parkinsonian disorders and could help in selecting treatment for early-stage patients and identifying participants for clinical trials.

Funding
National Institutes of Health and General Clinical Research Center at The Feinstein Institute for Medical Research.

http://www.healthimaging.com/index.php? … e&id=20154

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Lancet: FDG-PET could distinguish between Parkinsonian disorders
Written by Editorial Staff, HealthImaging.com
January 11, 2010

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.

Last updated on January 11, 2010 at 12:48 pm EST