A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET
Etminani, Kobra (Center for Applied Intelligent Systems Research (CAISR). Halmstad University)
Soliman, Amira (Center for Applied Intelligent Systems Research (CAISR). Halmstad University)
Davidsson, Anette (Department of Clinical Physiology. Department of Health. Medicine and Caring Sciences. Linköping University)
Chang, Jose R. (National Cheng Kung University in Tainan)
Martínez-Sanchis, Begoña (Hospital Universitari i Politècnic La Fe (València))
Byttner, Stefan (Center for Applied Intelligent Systems Research (CAISR). Halmstad University)
Camacho, Valle (Institut d'Investigació Biomèdica Sant Pau)
Bauckneht, Matteo (Nuclear Medicine Unit. IRCCS Ospedale Policlinico San Martino)
Stegeran, Roxana (Department of Diagnostic Radiology. Linköping University Hospital)
Ressner, Marcus (Department of Medical Physics. Linköping University Hospital)
Agudelo-Cifuentes, Marc (Hospital Universitari i Politècnic La Fe (València))
Chincarini, Andrea (National Institute of Nuclear Physics (INFN))
Brendel, Matthias (Department of Nuclear Medicine. University Hospital. LMU Munich)
Rominger, Axel (Department of Nuclear Medicine. Inselspital. University Hospital Bern)
Bruffaerts, Rose (Biomedical Research Institute. Hasselt University)
Vandenberghe, Rik (Neurology Department. University Hospitals Leuven)
Kramberger, Milica G. (Department of Neurology. University Medical Centre)
Trost, Maja (Univerza V Ljubljani)
Nicastro, Nicolas (Department of Clinical Neurosciences. Geneva University Hospitals)
Frisoni, Giovanni B. (LANVIE (Laboratoire de Neuroimagerie du Vieillissement). Department of Psychiatry. University Hospitals)
Lemstra, Afina W. (Department of Neurology. Alzheimer Center)
van Berckel, Bart N.M. (Department of Radiology & Nuclear Medicine. Amsterdam UMC. location VUmc)
Pilotto, Andrea (Parkinson's Disease Rehabilitation Centre)
Padovani, Alessandro (Neurology Unit. Department of Clinical and Experimental Sciences. University of Brescia)
Morbelli, Silvia (Department of Health Sciences. University of Genoa)
Aarsland, Dag (Department of Old Age Psychiatry. Institute of Psychiatry. Psychology and Neuroscience. King's College London)
Nobili, Flavio (Clinical Neurology. IRCCS Ospedale Policlinico San Martino)
Garibotto, Valentina (Division of Nuclear Medicine and Molecular Imaging. University Hospitals of Geneva and NIMTLab. Faculty of Medicine. University of Geneva)
Ochoa-Figueroa, Miguel (Center for Medical Image Science and Visualization (CMIV). Linköping University)
Universitat Autònoma de Barcelona

Date:	2022
Abstract:	Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results: The proposed model achieved area under the ROC curve of 96. 2% (95% confidence interval: 90. 6-100) on predicting the final diagnosis of DLB in the independent test set, 96. 4% (92. 7-100) in AD, 71. 4% (51. 6-91. 2) in MCI-AD, and 94. 7% (90-99. 5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.
Note:	Altres ajuts: the Alzheimer's Disease Neuroimaging Initiative (ADNI); National Institutes of Health (Grant U01 AG024904); DOD ADNI - Department of Defense (award number W81XWH-12-2-0012; VINNOVA (Grant 2017-02447); Formas and the Swedish Energy Agency; the Swiss National Science Foundation (projects 320030_169876, 320030_185028); the Velux Foundation (project 1123); the Flanders Research Foundation (FWO 12I2121N).
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Artificial intelligence ; Deep learning ; FDG PET ; Alzheimer's disease ; Mild cognitive impairment ; Dementia with Lewy bodies
Published in:	European Journal of Nuclear Medicine and Molecular Imaging, Vol. 49 Núm. 2 (january 2022) , p. 563-584, ISSN 1619-7089

DOI: 10.1007/s00259-021-05483-0
PMID: 34328531

22 p, 3.7 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut de Recerca Sant Pau
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