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      Quantitative measurements of relative fluid-attenuated inversion recovery (FLAIR) signal intensities in acute stroke for the prediction of time from symptom onset.

      Journal of Cerebral Blood Flow & Metabolism
      Aged, Algorithms, Blood Volume, physiology, Brain, blood supply, Cerebrovascular Circulation, Data Interpretation, Statistical, Diffusion Magnetic Resonance Imaging, Female, Humans, Image Interpretation, Computer-Assisted, Male, Microvessels, Predictive Value of Tests, Sensitivity and Specificity, Stroke, diagnosis, Symptom Assessment, methods, Time Factors

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          Abstract

          In acute stroke magnetic resonance imaging, a 'mismatch' between visibility of an ischemic lesion on diffusion-weighted imaging (DWI) and missing corresponding parenchymal hyperintensities on fluid-attenuated inversion recovery (FLAIR) data sets was shown to identify patients with time from symptom onset ≤4.5 hours with high specificity. However, moderate sensitivity and suboptimal interpreter agreement are limitations of a visual rating of FLAIR lesion visibility. We tested refined image analysis methods in patients included in the previously published PREFLAIR study using refined visual analysis and quantitative measurements of relative FLAIR signal intensity (rSI) from a three-dimensional, segmented stroke lesion volume. A total of 399 patients were included. The rSI of FLAIR lesions showed a moderate correlation with time from symptom onset (r=0.382, P<0.001). A FLAIR rSI threshold of <1.0721 predicted symptom onset ≤4.5 hours with slightly increased specificity (0.85 versus 0.78) but also slightly decreased sensitivity (0.47 versus 0.58) as compared with visual analysis. Refined visual analysis differentiating between 'subtle' and 'obvious' FLAIR hyperintensities and classification and regression tree algorithms combining information from visual and quantitative analysis also did not improve diagnostic accuracy. Our results raise doubts whether the prediction of stroke onset time by visual image judgment can be improved by quantitative rSI measurements.

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