Linked Life Data

11711228

Source:http://linkedlifedata.com/resource/pubmed/id/11711228

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2001-11-16
pubmed:abstractText
In this study we present a novel automated strategy for predicting infarct evolution, based on MR diffusion and perfusion images acquired in the acute stage of stroke. The validity of this methodology was tested on novel patient data including data acquired from an independent stroke clinic. Regions-of-interest (ROIs) defining the initial diffusion lesion and tissue with abnormal hemodynamic function as defined by the mean transit time (MTT) abnormality were automatically extracted from DWI/PI maps. Quantitative measures of cerebral blood flow (CBF) and volume (CBV) along with ratio measures defined relative to the contralateral hemisphere (r(a)CBF and r(a)CBV) were calculated for the MTT ROIs. A parametric normal classifier algorithm incorporating these measures was used to predict infarct growth. The mean r(a)CBF and r(a)CBV values for eventually infarcted MTT tissue were 0.70 +/- 0.19 and 1.20 +/- 0.36. For recovered tissue the mean values were 0.99 +/- 0.25 and 1.87 +/- 0.71, respectively. There was a significant difference between these two regions for both measures (p < 0.003 and p < 0.001, respectively). Mean absolute measures of CBF (ml/100g/min) and CBV (ml/100g) for the total infarcted territory were 33.9 +/- 9.7 and 4.2 +/- 1.9. For recovered MTT tissue, the mean values were 41.5 +/- 7.2 and 5.3 +/- 1.2, respectively. A significant difference was also found for these regions (p < 0.009 and p < 0.036, respectively). The mean measures of sensitivity, specificity, positive and negative predictive values for modeling infarct evolution for the validation patient data were 0.72 +/- 0.05, 0.97 +/- 0.02, 0.68 +/- 0.07 and 0.97 +/- 0.02. We propose that this automated strategy may allow possible guided therapeutic intervention to stroke patients and evaluation of efficacy of novel stroke compounds in clinical drug trials.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0730-725X
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1043-53
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
MRI based diffusion and perfusion predictive model to estimate stroke evolution.
pubmed:affiliation
Centre For Magnetic Resonance, University of Queensland, Brisbane, Queensland 4072, Australia. Stephen.Rose@cmr.uq.edu.au
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't