期刊
NEUROIMAGE-CLINICAL
卷 31, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2021.102712
关键词
Alzheimer's disease; Support vector machine; Convolutional Neural Network; External validation
类别
资金
- NVIDIA Corporation
- Dutch Heart Foundation [2018B011]
- Netherlands CardioVascular Research Initiative [CVON2012-06, CVON2018-28]
- Medical Delta Diagnostics 3.0: Dementia and Stroke
- Health Holland LSH-TKI project Beyond [LSHM18049]
- European Union [666992]
- Dutch Federation of University Medical Centers
- Dutch Gov-ernment - Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Alz-heimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Fujirebio
- Johnson & Johnson Pharmaceutical Research & Development LLC.
- Merck Co., Inc.
- Meso Scale Diagnostics
- NeuroRx Research
- Novartis Pharmaceuticals Corporation
- Pfizer Inc.
- Piramal Imaging
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- ADNI clinical sites in Canada
- Foundation for the National Institutes of Health
- Northern California Institute for Research and Education
- Laboratory for Neuro Imaging at the University of Southern California
This study validates the generalizability of MRI-based classification of AD patients and controls to an external dataset, and for predicting conversion to AD in individuals with MCI. Different preprocessing methods were compared, and classifiers based on modulated GM maps outperformed minimally processed images. The results indicate that machine learning algorithms show promise in clinical applications for neurodegenerative diseases.
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive preprocessing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p < 0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p = 0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
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