NeuroImage. Clinical · 2017
Virtual brain biopsies in amyotrophic lateral sclerosis: Diagnostic classification based on in vivo pathological patterns.
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Bede Peter, Iyer Parameswaran M, Finegan Eoin, Omer Taha, Hardiman Orla
Аннотация
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.
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.