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Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease

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

Abstract

Purpose

The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18F-fluorodeoxyglucose (FDG) PET data.

Methods

The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time.

Results

The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients.

Conclusions

The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease.

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Acknowledgements

Data collection and sharing for this project was partly funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research&Development, LLC.; Johnson & Johnson Pharmaceutical Research&Development LLC.; Lumosity; Lundbeck; Merck&Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. 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 is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

A substantial part of data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.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 analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

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Authors and Affiliations

  1. Institute of Molecular Bioimaging and Physiology, National Research Council, Largo Paolo Daneo, 3, 16132, Genoa, Italy

    Fabrizio De Carli

  2. Department of Neuroscience (DINOGMI), IRCCS Polyclinic San Martino-IST, University of Genoa, Genoa, Italy

    Flavio Nobili, Federico Massa, Matteo Grazzini & Dario Arnaldi

  3. Institute of Cognitive Sciences and Technologies, CNR, Rome, Italy

    Marco Pagani

  4. Medical Radiation Physics and Nuclear Medicine, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden

    Marco Pagani & Cathrine Jonsson

  5. Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

    Matteo Bauckneht & Silvia Morbelli

  6. Nuclear Medicine Unit, Polyclinic San Martino Hospital, Genoa, Italy

    Matteo Bauckneht & Silvia Morbelli

  7. National Institute of Nuclear Physics (INFN), Genoa, Italy

    Enrico Peira

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  1. Fabrizio De Carli
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  2. Flavio Nobili
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for the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Fabrizio De Carli.

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Conflict of interest

F.N.: received personal fees and nonfinancial support from GE Healthcare, non-financial support from Eli-Lilly and grants from Chiesi Farmaceutici; D.A. received speaking honoraria from Fidia s.p.a.; S.M. acted as a consultant for Eli Lilly in 2014 and for Avid Radiopharmaceuticals in 2016. All other authors have no potential conflicts to declare.

Ethical approval

The institutional review board of the University of Genoa approved the recording and data treatment procedures involving human participants in this study and all procedures were in accordance with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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De Carli, F., Nobili, F., Pagani, M. et al. Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease. Eur J Nucl Med Mol Imaging 46, 334–347 (2019). https://doi.org/10.1007/s00259-018-4197-7

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  • DOI: https://doi.org/10.1007/s00259-018-4197-7

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