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Classification of Alzheimer's and MCI patients from semantically parcelled PET images: A comparison between AV45 and FDG-PET

Seyed Hossein Nozadi, Samuel Kadoury

Article (2018)

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Early identification of dementia in the early or late stages of mild cognitive impairment (MCI) is crucial for a timely diagnosis and slowing down the progression of Alzheimer's disease (AD). Positron emission tomography (PET) is considered a highly powerful diagnostic biomarker, but few approaches investigated the efficacy of focusing on localized PET-active areas for classification purposes. In this work, we propose a pipeline using learned features from semantically labelled PET images to perform group classification. A deformable multimodal PET-MRI registration method is employed to fuse an annotated MNI template to each patient-specific PET scan, generating a fully labelled volume from which 10 common regions of interest used for AD diagnosis are extracted. The method was evaluated on 660 subjects from the ADNI database, yielding a classification accuracy of 91.2% for AD versus NC when using random forests combining features from cross-sectional and follow-up exams. A considerable improvement in the early versus late MCI classification accuracy was achieved using FDG-PET compared to the AV-45 compound, yielding a 72.5% rate. The pipeline demonstrates the potential of exploiting longitudinal multiregion PET features to improve cognitive assessment.

Uncontrolled Keywords

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

Department: Department of Computer Engineering and Software Engineering
Funders: Canadian Institutes of Health Research, Catalyst Grant, Alzheimer’s Disease Neuroimaging Initiative (ADNI), DOD ADNI, National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering, AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica, Biogen, Bristol-Myers Squibb Company, CereSpir, Cogstate, Eisai, Elan Pharmaceuticals, Eli Lilly and Company, EuroImmun, F. Hoffman--La Roche, Foundation for the National Institutes of Health, Northern California Institute for Research and Education, Genentech, Inc., Fujirebio, GE Healthcare, IXICO, Janssen Alzheimer Immunotherapy Research & Development, Johnson & Johnson Pharmaceutical Research & Development LLC, Lumosity, Lundbeck, Merck & Co, Meso Scale Diagnostics, NeuroRx, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc, Piramal Imaging, Servier, Takeda Pharmaceutical Company, Transition Therapeutics
Grant number: 313693, U01 AG024904, W81XWH-12-2-0012
PolyPublie URL: https://publications.polymtl.ca/5061/
Journal Title: International Journal of Biomedical Imaging (vol. 2018)
Publisher: Hindawi Publishing Corporation
DOI: 10.1155/2018/1247430
Official URL: https://doi.org/10.1155/2018%2f1247430
Date Deposited: 16 May 2022 17:07
Last Modified: 26 May 2023 03:09
Cite in APA 7: Nozadi, S. H., & Kadoury, S. (2018). Classification of Alzheimer's and MCI patients from semantically parcelled PET images: A comparison between AV45 and FDG-PET. International Journal of Biomedical Imaging, 2018, 1247430 (13 pages). https://doi.org/10.1155/2018%2f1247430


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