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Published on: November 26, 2013
Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours
Anke Wouters1, Bastian Cheng2, Soren Christensen2
1From the Departments of Neurosciences (A.W., R. Lemmens), Cognitive Neurology (P.D.), and Electrical Engineering (D.R.), KU Leuven, University of Leuven; VIB Center for Brain & Disease Research (A.W., R. Lemmens); Department of Neurology (A.W., R. Lemmens), University Hospitals Leuven, Leuven, Belgium; Department of Neurology (B.C., G.T.), University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Stanford Stroke Center (S.C., G.W.A.), Stanford University Medical Center, Palo Alto, CA; Department of Neurology (B.N.), Lund University, Sweden; Guided Development GmbH (R. Laage), Heidelberg, Germany; and Florey Institute of Neuroscience and Mental Health (V.N.T.), Heidelberg, Australia. anke.wouters@kuleuven.vib.be.
Objective:
To develop an automated model based on diffusion-weighted imaging (DWI) to detect patients within 4.5 hours after stroke onset and compare this method to the visual DWI-FLAIR (fluid-attenuated inversion recovery) mismatch.
Methods:
We performed a subanalysis of the "DWI-FLAIR mismatch for the identification of patients with acute ischemic stroke within 4.5 hours of symptom onset" (PRE-FLAIR) and the "AX200 for ischemic stroke" (AXIS 2) trials. We developed a prediction model with data from the PRE-FLAIR study by backward logistic regression with the 4.5-hour time window as dependent variable and the following explanatory variables: age and median relative DWI (rDWI) signal intensity, interquartile range (IQR) rDWI signal intensity, and volume of the core. We obtained the accuracy of the model to predict the 4.5-hour time window and validated our findings in an independent cohort from the AXIS 2 trial. We compared the receiver operating characteristic curve to the visual DWI-FLAIR mismatch.
Results:
In the derivation cohort of 118 patients, we retained the IQR rDWI as explanatory variable. A threshold of 0.39 was most optimal in selecting patients within 4.5 hours after stroke onset resulting in a sensitivity of 76% and specificity of 63%. The accuracy was validated in an independent cohort of 200 patients. The predictive value of the area under the curve of 0.72 (95% confidence interval 0.64-0.80) was similar to the visual DWI-FLAIR mismatch (area under the curve = 0.65; 95% confidence interval 0.58-0.72; p for difference = 0.18).
Conclusions:
An automated analysis of DWI performs at least as good as the visual DWI-FLAIR mismatch in selecting patients within the 4.5-hour time window.
Insights
An automated diffusion-weighted imaging (DWI) model effectively identifies acute ischemic stroke patients within 4.5 hours. This AI approach matches the accuracy of visual DWI-FLAIR mismatch analysis for time-sensitive stroke treatment decisions.
Area of Science:
- Neurology
- Radiology
- Medical Imaging Analysis
Background:
- Timely detection of acute ischemic stroke is critical for effective treatment.
- Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) are key imaging modalities.
- Visual assessment of DWI-FLAIR mismatch is a standard method for patient selection within a 4.5-hour window.
Purpose of the Study:
- To develop and validate an automated model using DWI for detecting patients within 4.5 hours of stroke onset.
- To compare the performance of the automated DWI model against the established visual DWI-FLAIR mismatch method.
Main Methods:
- A prediction model was developed using backward logistic regression on data from the PRE-FLAIR study.
- Key explanatory variables included age, median relative DWI (rDWI) signal intensity, IQR rDWI signal intensity, and core volume.
- The model's accuracy was validated in an independent cohort from the AXIS 2 trial, with performance compared via receiver operating characteristic curves.
Main Results:
- The optimal threshold for the interquartile range (IQR) of rDWI was 0.39, achieving 76% sensitivity and 63% specificity in the derivation cohort.
- Validation in an independent cohort showed an area under the curve (AUC) of 0.72 (95% CI 0.64-0.80) for the automated model.
- The automated model's predictive performance was comparable to the visual DWI-FLAIR mismatch (AUC = 0.65; 95% CI 0.58-0.72).
Conclusions:
- Automated analysis of DWI demonstrates comparable efficacy to visual DWI-FLAIR mismatch assessment.
- The automated DWI model reliably selects patients within the critical 4.5-hour time window for acute stroke intervention.
- This automated approach offers a potential advancement in streamlining stroke patient triage and treatment initiation.
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