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Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

To introduce, evaluate and validate a voxel-based analysis method of 18F-FDG PET imaging for determining the probability of Alzheimer’s disease (AD) in a particular individual.

Methods

The subject groups for model derivation comprised 80 healthy subjects (HS), 36 patients with mild cognitive impairment (MCI) who converted to AD dementia within 18 months, 85 non-converter MCI patients who did not convert within 24 months, and 67 AD dementia patients with baseline FDG PET scan were recruited from the AD Neuroimaging Initiative (ADNI) database. Additionally, baseline FDG PET scans from 20 HS, 27 MCI and 21 AD dementia patients from our institutional cohort were included for model validation. The analysis technique was designed on the basis of the AD-related hypometabolic convergence index adapted for our laboratory-specific context (AD-PET index), and combined in a multivariable model with age and gender for AD dementia detection (AD score). A logistic regression analysis of different cortical PET indexes and clinical variables was applied to search for relevant predictive factors to include in the multivariable model for the prediction of MCI conversion to AD dementia (AD-Conv score). The resultant scores were stratified into sixtiles for probabilistic diagnosis.

Results

The area under the receiver operating characteristic curve (AUC) for the AD score detecting AD dementia in the ADNI database was 0.879, and the observed probability of AD dementia in the six defined groups ranged from 8 % to 100 % in a monotonic trend. For predicting MCI conversion to AD dementia, only the posterior cingulate index, Mini-Mental State Examination (MMSE) score and apolipoprotein E4 genotype (ApoE4) exhibited significant independent effects in the univariable and multivariable models. When only the latter two clinical variables were included in the model, the AUC was 0.742 (95 % CI 0.646 – 0.838), but this increased to 0.804 (95 % CI 0.714 – 0.894, bootstrap p = 0.027) with the addition of the posterior cingulate index (AD-Conv score). Baseline clinical diagnosis of MCI showed 29.7 % of converters after 18 months. The observed probability of conversion in relation to baseline AD-Conv score was 75 % in the high probability group (sixtile 6), 34 % in the medium probability group (merged sixtiles 4 and 5), 20 % in the low probability group (sixtile 3) and 7.5 % in the very low probability group (merged sixtiles 1 and 2). In the validation population, the AD score reached an AUC of 0.948 (95 % CI 0.625 – 0.969) and the AD-Conv score reached 0.968 (95 % CI 0.908 – 1.000), with AD patients and MCI converters included in the highest probability categories.

Conclusion

Posterior cingulate hypometabolism, when combined in a multivariable model with age and gender as well as MMSE score and ApoE4 data, improved the determination of the likelihood of patients with MCI converting to AD dementia compared with clinical variables alone. The probabilistic model described here provides a new tool that may aid in the clinical diagnosis of AD and MCI conversion.

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Acknowledgments

This work was supported in part by the Government of Spain, Institute of Health Carlos III, the Ministry of Health grant 01/0809, and the Ministry of Science and Innovation grant ADE 10/00028, and CB06/05/0077 CIBERNED (Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas).

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; National Institutes of Health grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129 and K01 AG030514.

Conflict of interest

M.W. Weiner is the principal investigator of ADNI and declares the above-mentioned organizations as contributors to the Foundation for NIH and thus to the NIA-funded ADNI. The remaining authors have no conflicts of interest to declare.

Author information

Author notes

  1. P. Martínez-Lage

    Present address: Fundación CITA-Alzheimer Fundazioa, Center for Research and Advanced Therapies Alzheimer, Parque Tecnológico de San Sebastián, Mikeletegi 71, bajo, 20009, San Sebastián, Spain

  2. M. T. Gómez-Isla

    Present address: Neurology Department, Massachusetts General Hospital, WACC Suite 715, 15 Parkman St, Boston, MA, 02114, USA

Authors and Affiliations

  1. Department of Nuclear Medicine, Clínica Universidad de Navarra, Avenida Pio XII 36, 31008, Pamplona, Spain

    Javier Arbizu, E. Prieto, J. M. Martí-Climent, I. Peñuelas & J. A. Richter

  2. Department of Neurology, Clínica Universidad de Navarra, Avenida Pio XII 36, 31008, Pamplona, Spain

    P. Martínez-Lage, I. Lamet, P. Pastor, M. Riverol & M. T. Gómez-Isla

  3. Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Calle Irunlarrea 1, 31008, Pamplona, Spain

    M. García-Granero

  4. Center for Imaging of Neurodegenerative Diseases (CIND), San Francisco VA Medical Center, 4150 Clement Street (114M), San Francisco, CA, 94121, USA

    M. W. Weiner

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for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Javier Arbizu.

Additional information

J. Arbizu and E. Prieto contributed equally to this study and should be considered equal first authors.

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Arbizu, J., Prieto, E., Martínez-Lage, P. et al. Automated analysis of FDG PET as a tool for single-subject probabilistic prediction and detection of Alzheimer’s disease dementia. Eur J Nucl Med Mol Imaging 40, 1394–1405 (2013). https://doi.org/10.1007/s00259-013-2458-z

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  • DOI: https://doi.org/10.1007/s00259-013-2458-z

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