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

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.