1. Virtual brain biopsies in amyotrophic lateral sclerosis: Diagnostic classification based on in vivo pathological patterns
- Author
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Peter Bede, Parameswaran M. Iyer, Eoin Finegan, Taha Omer, and Orla Hardiman
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
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. Keywords: Magnetic resonance imaging, Neuroimaging, Diagnosis, Neurodegeneration, Amyotrophic lateral sclerosis, Motor neuron disease
- Published
- 2017
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