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Rogeau A, Hives F, Bordier C, Lahousse H, Roca V, Lebouvier T, Pasquier F, Huglo D, Semah F, Lopes R. A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET. Neuroimage 2024; 288:120530. [PMID: 38311126 DOI: 10.1016/j.neuroimage.2024.120530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 02/06/2024] Open
Abstract
With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.
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Affiliation(s)
- Antoine Rogeau
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK.
| | - Florent Hives
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Cécile Bordier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Hélène Lahousse
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France
| | - Vincent Roca
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
| | - Thibaud Lebouvier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Florence Pasquier
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Memory Clinic, Lille University Hospitals, Lille, France
| | - Damien Huglo
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; Inserm, CHU Lille, University of Lille, U1189 OncoTHAI, Lille, France
| | - Franck Semah
- Department of Nuclear Medicine, Lille University Hospitals, Lille, France; University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France
| | - Renaud Lopes
- University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France; Institut Pasteur de Lille, University of Lille, CNRS, Inserm, CHU Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France
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Nguyen H, Clément M, Mansencal B, Coupé P. Brain structure ages-A new biomarker for multi-disease classification. Hum Brain Mapp 2024; 45:e26558. [PMID: 38224546 PMCID: PMC10785199 DOI: 10.1002/hbm.26558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/20/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024] Open
Abstract
Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.
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Affiliation(s)
- Huy‐Dung Nguyen
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Michaël Clément
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Boris Mansencal
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Pierrick Coupé
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
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