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MRI, PET, and CSF Biomarkers Improve Prediction of Alzheimer's Disease

 
WEDNESDAY, Dec. 12 (HealthDay News) -- Adding data from magnetic resonance imaging (MRI), fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers to routine clinical testing can improve the ability to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD), according to research published online Dec. 11 in Radiology.

Jennifer L. Shaffer, M.D., of Duke University Medical Center in Durham, N.C., and colleagues conducted a study involving 97 subjects with MCI who underwent MRI and FDG PET as well as CSF analysis to examine the extent to which multiple AD biomarkers predict future decline.

Compared with clinical testing alone, the researchers found that the combination of MRI, FDG PET, and CSF biomarker data correlated with a significant increase in the accuracy of predicting conversion to AD, with a decrease in the misclassification rate from 41.3 to 28.4 percent. Of the additional tests, FDG PET contributed more information to routine tests than either CSF or MRI data.

"In summary, a model combining clinical information with MRI, FDG PET, and CSF markers yielded the highest accuracy for predicting future MCI conversion. However, the most efficient model included only FDG PET with the clinical covariates," the authors write. "Thus, in patients with MCI in whom ApoE4 genotype and cognitive testing is already available, FDG PET would likely yield the greatest additional value."

Several authors disclosed financial ties to the pharmaceutical industry.
 

Abstract
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