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    Statistical prediction of tissue fate in acute ischemic brain injury

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    Authors
    Shen, Qiang
    Ren, Hongxia
    Fisher, Marc
    Duong, Timothy Q.
    UMass Chan Affiliations
    Department of Neurology
    Document Type
    Journal Article
    Publication Date
    2005-04-15
    Keywords
    Algorithms
    Animals
    Brain Ischemia
    Cerebrovascular Circulation
    Cluster Analysis
    *Diffusion Magnetic Resonance Imaging
    Infarction, Middle Cerebral Artery
    Male
    *Models, Statistical
    Probability
    Prognosis
    Rats
    Rats, Sprague-Dawley
    Regional Blood Flow
    Stroke
    Neurology
    Statistics and Probability
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    Link to Full Text
    http://dx.doi.org/10.1038/sj.jcbfm.9600126
    Abstract
    An algorithm was developed to statistically predict ischemic tissue fate on a pixel-by-pixel basis. Quantitative high-resolution (200 x 200 microm) cerebral blood flow (CBF) and apparent diffusion coefficient (ADC) were measured on acute stroke rats subjected to permanent middle cerebral artery occlusion and an automated clustering (ISODATA) technique was used to classify ischemic tissue types. Probability and probability density profiles were derived from a training data set (n=6) and probability maps of risk of subsequent infarction were computed in another group of animals (n=6) as ischemia progressed. Predictions were applied to overall tissue fate. Performance measures (sensitivity, specificity, and receiver operating characteristic) showed that prediction made based on combined ADC+CBF data outperformed those based on ADC or CBF data alone. At the optimal operating points, combined ADC+CBF predicted tissue infarction with 86%+/-4% sensitivity and 89%+/-6% specificity. More importantly, probability of infarct (P(I)) for different ISODATA-derived ischemic tissue types were also computed: (1) For the 'normal' cluster in the ischemic right hemisphere, P(I) based on combined ADC+CBF data (P(I)[ADC+CBF]) accurately reflected tissue fate, whereas P(I)[ADC] and P(I)[CBF] overestimated infarct probability. (2) For the 'perfusion-diffusion mismatch' cluster, P(I)[ADC+CBF] accurately predicted tissue fate, whereas P(I)[ADC] underestimated and P(I)[CBF] overestimated infarct probability. (3) For the core cluster, P(I)[ADC+CBF], P(I)[ADC], and P(I)[CBF] prediction were high and similar ( approximately 90%). This study shows an algorithm to statistically predict overall, normal, ischemic core, and 'penumbral' tissue fate using early quantitative perfusion and diffusion information. It is suggested that this approach can be applied to stroke patients in a computationally inexpensive manner.
    Source
    J Cereb Blood Flow Metab. 2005 Oct;25(10):1336-45. Link to article on publisher's site
    DOI
    10.1038/sj.jcbfm.9600126
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/37630
    PubMed ID
    15829912
    Related Resources
    Link to article in PubMed
    ae974a485f413a2113503eed53cd6c53
    10.1038/sj.jcbfm.9600126
    Scopus Count
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