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


RESULTS
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).


CONCLUSIONS
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.


BACKGROUND
National Institutes of Health and General Clinical Research Center at The Feinstein Institute for Medical Research.
Tang
Chris C
CC
Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA.
Poston
Kathleen L
KL
Eckert
Thomas
T
Feigin
Andrew
A
Frucht
Steven
S
Gudesblatt
Mark
M
Dhawan
Vijay
V
Lesser
Martin
M
Vonsattel
Jean-Paul
JP
Fahn
Stanley
S
Eidelberg
David
D
D000368:Aged; D001921:Brain / Q000000981:diagnostic imaging / Q000378:metabolism; D001931:Brain Mapping; D003937:Diagnosis, Differential; D005260:Female; D019788:Fluorodeoxyglucose F18 / Q000378:metabolism; D006801:Humans; D008297:Male; D008875:Middle Aged; D020734:Parkinsonian Disorders / Q000145:classification / Q000175:diagnosis* / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D049268:Positron-Emission Tomography; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D019275:Radiopharmaceuticals / Q000378:metabolism; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18