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Abstract #0757

Automated Stroke Disability Prediction & Mismatch Analysis by Employing Lesion Topography & Statistical Models

Roland Bammer1, Matus Straka1, Gregory W. Albers2, 3

1Center for Quantitative Neuroimaging, Department of Radiology, Stanford University, Stanford, CA, United States; 2Stanford Stroke Center, Department of Neurology, Stanford University, Stanford, CA, United States; 3on behalf of the DEFUSE investigators

DWI/PWI have been demonstrated to be reliable surrogate imaging markers for infarct core and at-risk tissue in acute stroke. Thus far, imaging-based prediction of clinical outcome has been primarily relied on overall lesion size and/or volumetric mismatch between stroke core and at-risk tissue. Here, we use a novel approach that employs importance-weighting to the mismatch analysis, where brain voxels contribute more or less to the scoring metric, based on their location and relative contribution to disability-based population statistics. Using a statistical model (i.e. stroke atlas), weights were derived from an acute stroke patient population and it could been shown that this topographic method predicts stroke disability extremely well.

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Keywords

accounts acute affected applied aspect assigning asymmetry atlas automated automatic basis behalf brain built capacity certain circulation clinical coefficients combined composition comprising computed considerable considers contribute contributions core correlate correlation critically damage define defuse derived disability distributions dominant driven either employing enrolled existing favorable feasibility final flair focusing funded harmful hemisphere hemispheres hemispheric impact importance incidence included indicate individual infarct introduced investigators known leave lesion lesions likely linearly local localized location look malignant manually markers metric metrics mismatch model models moreover mostly much neglects neurology normalized note novel ordinal outcome overall patient patients pilot plasticity population posterior predetermined predicted prediction predicts primarily profile quantitative radiology reflecting regional regionally relationship relatively reliable reliably repository represent risk salvageable score scores scoring selected several severity since space sparse spatial statistical statistics striking stroke strokes subcategories subsequent substantial successfully suggest summation territories territory tissue topographically topography triage trial true variant vascular visual volume volumetric warrant