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Dement Geriatr Cogn Disord. 2009;28(3):259-66. doi: 10.1159/000241879. Epub 2009 Sep 25.

Performance of FDG PET for detection of Alzheimer's disease in two independent multicentre samples (NEST-DD and ADNI).

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  • 1Max Planck Institute for Neurological Research, Cologne, Germany.

Abstract

AIM:

We investigated the performance of FDG PET using an automated procedure for discrimination between Alzheimer's disease (AD) and controls, and studied the influence of demographic and technical factors.

METHODS:

FDG PET data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) [102 controls (76.0 +/- 4.9 years) and 89 AD patients (75.7 +/- 7.6 years, MMSE 23.5 +/- 2.1) and the Network for Standardisation of Dementia Diagnosis (NEST-DD) [36 controls (62.2 +/- 5.0 years) and 237 AD patients (70.8 +/- 8.3 years, MMSE 20.9 +/- 4.4). The procedure created t-maps of abnormal voxels. The sum of t-values in predefined areas that are typically affected by AD (AD t-sum) provided a measure of scan abnormality associated with a preset threshold for discrimination between patients and controls.

RESULTS:

AD patients had much higher AD t-sum scores compared to controls (p < 0.01), which were significantly related to dementia severity (ADNI: r = -0.62, p < 0.01; NEST-DD: r = -0.59, p < 0.01). Early-onset AD patients had significantly higher AD t-sum scores than late-onset AD patients (p < 0.01). Differences between databases were mainly due to different age distributions. The predefined AD t-sum threshold yielded a sensitivity and specificity of 83 and 78% in ADNI and 78 and 94% in NEST-DD, respectively.

CONCLUSION:

The automated FDG PET analysis procedure provided good discrimination power, and was most accurate for early-onset AD.

Copyright 2009 S. Karger AG, Basel.

PMID:
19786778
[PubMed - indexed for MEDLINE]
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