期刊
NEUROIMAGE-CLINICAL
卷 15, 期 -, 页码 653-658出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.nicl.2017.06.010
关键词
Magnetic resonance imaging; Neuroimaging; Diagnosis; Neurodegeneration; Amyotrophic lateral sclerosis; Motor neuron disease
类别
资金
- Health Research Board (HRB-Ireland)
- Irish Institute of Clinical Neuroscience IICN - Novartis Ireland Research Grant
- Iris O'Brien Foundation
- Perrigo Clinician-Scientist Research Fellowship
- Research Motor Neuron (RMN-Ireland) Foundation
- EU-Joint Programme for Neurodegeneration (JPND) SOPHIA project
Background: Diagnostic uncertainty in ALS has serious management implications and delays recruitment into clinical trials. Emerging evidence of presymptomatic disease-burden provides the rationale to develop diagnostic applications based on the evaluation of in-vivo pathological patterns early in the disease. Objectives: To outline and test a diagnostic classification approach based on an array of complementary imaging metrics in key disease-associated anatomical structures. Methods: Data from 75 ALS patients and 75 healthy controls were randomly allocated in a 'training' and 'validation' cohort. Spatial masks were created for anatomical foci which best discriminate patients from controls in the 'training sample'. In a virtual 'brain biopsy', data was then retrieved from these key disease-associated brain regions. White matter diffusivity indices, grey matter T1-signal intensity values and basal ganglia volumes were evaluated as predictor variables in a canonical discriminant function. Results: Following predictor variable selection, a classification specificity of 85.5% and sensitivity of 89.1% was achieved in the training sample and 90% specificity and 90% sensitivity in the validation sample. Discussion: This study evaluates disease-associated imaging measures in a dummy diagnostic application. Although larger samples will be required for robust validation, the study confirms the potential of multimodal quantitative imaging in future clinical applications.
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