Vol. 28, No. 3, 2009
Issue release date: October 2009
Free Access
Dement Geriatr Cogn Disord 2009;28:259–266
Original Research Article
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Performance of FDG PET for Detection of Alzheimer’s Disease in Two Independent Multicentre Samples (NEST-DD and ADNI)

Haense C.a, b · Herholz K.b · Jagust W.J.c · Heiss W.D.a
aMax Planck Institute for Neurological Research, Cologne, Germany; bWolfson Molecular Imaging Centre, University of Manchester, Manchester, UK; cSchool of Public Health and Helen Wills Neuroscience Institute, University of California, Berkeley, Calif., USA
email Corresponding Author

 goto top of outline Key Words

  • Alzheimer’s disease
  • Healthy control
  • 18F-FDG PET
  • Automated analysis
  • Discrimination analysis
  • Biomarker
  • ADNI

 goto top of outline 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

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 goto top of outline Author Contacts

Prof. Karl Herholz
University of Manchester, Wolfson Molecular Imaging Centre
27 Palatine Road
Manchester M20 3LJ (UK)
Tel. +44 161 275 0000, Fax +44 161 275 0003, E-Mail karl.herholz@manchester.ac.uk

 goto top of outline Article Information

Data used in the preparation of this article were obtained from the Network for Standardisation of Dementia Diagnosis (NEST-DD) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within NEST-DD and ADNI contributed to the design and implementation of those studies and/or provided data, but did not participate in the analysis or writing of this report, except K.H. and W.J.J. (complete list of ADNI investigators available at: www.loni.ucla.edu/ADNI/About/About_InvestigatorsTable.shtml; NEST-DD principal investigators were D. Perani, Milan, S. Sorbi, Florence, E. Salmon, Liege, V. Holthoff, Dresden, and J.C. Baron, Caen).

Accepted: July 29, 2009
Published online: September 25, 2009
Number of Print Pages : 8
Number of Figures : 2, Number of Tables : 2, Number of References : 39
Additional supplementary material is available online - Number of Parts : 1

 goto top of outline Publication Details

Dementia and Geriatric Cognitive Disorders

Vol. 28, No. 3, Year 2009 (Cover Date: October 2009)

Journal Editor: Chan-Palay V. (New York, N.Y.)
ISSN: 1420-8008 (Print), eISSN: 1421-9824 (Online)

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