Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study

Abstract

An emerging issue in neuroimaging is to assess the diagnostic reliability of PET and its application in clinical practice. We aimed at assessing the accuracy of brain FDG-PET in discriminating patients with MCI due to Alzheimer's disease and healthy controls. Sixty-two patients with amnestic MCI and 109 healthy subjects recruited in five centers of the European AD Consortium were enrolled. Group analysis was performed by SPM8 to confirm metabolic differences. Discriminant analyses were then carried out using the mean FDG uptake values normalized to the cerebellum computed in 45 anatomical volumes of interest (VOIs) in each hemisphere (90 VOIs) as defined in the Automated Anatomical Labeling (AAL) Atlas and on 12 meta-VOIs, bilaterally, obtained merging VOIs with similar anatomo-functional characteristics. Further, asymmetry indexes were calculated for both datasets. Accuracy of discrimination by a Support Vector Machine and the AAL VOIs was tested against a validated method (PALZ). At the voxel level SMP8 showed a relative hypometabolism in the bilateral precuneus, and posterior cingulate, temporo-parietal and frontal cortices. Discriminant analysis classified subjects with an accuracy ranging between .91 and .83 as a function of data organization. The best values were obtained from a subset of 6 meta-VOIs plus 6 asymmetry values reaching an area under the ROC curve of .947, significantly larger than the one obtained by the PALZ score. High accuracy in discriminating MCI converters from healthy controls was reached by a non-linear classifier based on SVM applied on predefined anatomo-functional regions and inter-hemispheric asymmetries. Data pre-processing was automated and simplified by an in-house created Matlab-based script encouraging its routine clinical use. Further validation toward nonconverter MCI patients with adequately long follow-up is needed.

DOI: 10.1016/j.nicl.2014.11.007

Extracted Key Phrases

3 Figures and Tables

0200400201520162017
Citations per Year

299 Citations

Semantic Scholar estimates that this publication has 299 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Pagani2015VolumeOI, title={Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study}, author={Marco Pagani and Fabrizio De Carli and Silvia Morbelli and Johanna {\"{O}berg and Andrea Chincarini and G. B. Frisoni and Samantha Galluzzi and Robert Perneczky and Alex Drzezga and B.N.M. van Berckel and Rik Ossenkoppele and Mira Didic and Eric Guedj and Andrea Brugnolo and Agnese Picco and Dario Arnaldi and Michela Ferrara and Ambra Buschiazzo and Gianmario Sambuceti and Flavio Nobili}, booktitle={NeuroImage: Clinical}, year={2015} }