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 (SVM) 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.
Highlights
FDGPET has been included in the National Institute of Aging–Alzheimer Association (NIA–AA) diagnostic criteria of Mild Cognitive Impairment (MCI) due to Alzheimer3s Disease (AD) (Albert et al, 2011; Sperling et al, 2011) while the recently proposed International Working Group (IWG)-2 research criteria hypothesize its role as a disease evolution rather than as a pure diagnostic biomarker (Dubois et al, 2014)
General Linear Model (GLM) analysis of normalized regional FDG values in the control subjects as a function of age and gender showed a significant effect of region, and region × gender interaction for both the partition into 90 Anatomical Labeling (AAL) regions and for the partition into 24 meta-volumes of interest (VOIs)
Less accurate classification was obtained with the 24 meta-VOIs (24R) and with the 90 AAL regions with associated asymmetry values (90R + 45A)
Summary
FDGPET has been included in the NIA–AA diagnostic criteria of Mild Cognitive Impairment (MCI) due to AD (Albert et al, 2011; Sperling et al, 2011) while the recently proposed IWG-2 research criteria hypothesize its role as a disease evolution rather than as a pure diagnostic biomarker (Dubois et al, 2014). All these new criteria are based on evidence accumulated since 1984 (McKhann et al, 1984) but need to be applied and verified, i.e., validated, in large patient populations. This process is ongoing and available data are encouraging (Lucignani and Nobili, 2010)
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