| 11. |
- Wouters, Anke, et al.
(författare)
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Association between the perfusion/diffusion and diffusion/FLAIR mismatch: data from the AXIS2 trial.
- 2015
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Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : SAGE Publications. - 1559-7016 .- 0271-678X. ; 35:10, s. 1681-1686
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Tidskriftsartikel (refereegranskat)abstract
- The perfusion-/diffusion-weighted imaging (PWI/DWI) mismatch and the diffusion/fluid attenuated inversion recovery (DWI/FLAIR) mismatch are magnetic resonance imaging (MRI) markers of evolving brain ischemia. We examined whether the DWI/FLAIR mismatch was independently associated with the PWI/DWI mismatch. Furthermore, we determined whether the presence of the DWI/FLAIR mismatch in patients with the PWI/DWI mismatch would provide additional information regarding last seen normal time (LTM). We used data from the 'AX200 for ischemic stroke' trial (AXIS 2 study NCT00927836). We studied the association between the presence of the DWI/FLAIR and PWI/DWI mismatch, baseline National Institute of Health Stroke Scale (NIHSS), age, ischemic-core volume, gender, intravenous (IV) tissue plasminogen activator (tPA), and perfusion-mismatch volume in univariate analysis. Significant variables (P<0.05) were added into the final multivariate model. We analyzed 197 patients. Seventy-two (37%) had both the PWI/DWI and the DWI/FLAIR mismatch. Patients with the double mismatch pattern had a shorter LTM than patients with the PWI/DWI mismatch alone (Median difference 90 minutes, P<0.01). Multivariate analysis confirmed the independent association between the two mismatch patterns (odds ratio (OR) 2.6, 95% confidence interval (CI) 1.2 to 5.4). Our study implies that the DWI/FLAIR mismatch and PWI/DWI mismatch are strongly associated, independent from LTM. Furthermore, in the presence of the PWI/DWI mismatch, the DWI/FLAIR pattern indicates a shorter LTM. This could have implications in selecting patients for reperfusion therapy.Journal of Cerebral Blood Flow & Metabolism advance online publication, 3 June 2015; doi:10.1038/jcbfm.2015.108.
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| 12. |
- Wouters, Anke, et al.
(författare)
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Association Between Time From Stroke Onset and Fluid-Attenuated Inversion Recovery Lesion Intensity Is Modified by Status of Collateral Circulation.
- 2016
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Ingår i: Stroke: a journal of cerebral circulation. - 1524-4628. ; 47, s. 1018-1018
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE:In patients with acute stroke, the intensity of a fluid-attenuated inversion recovery (FLAIR) lesion in the region of diffusion restriction is associated with time from symptom onset. We hypothesized that collateral status as assessed by the hypoperfusion intensity ratio could modify the association between time from stroke onset and FLAIR lesion intensity.METHODS:From the AX200 for ischemic stroke trial, 141 patients had appropriate FLAIR, diffusion-weighted imaging, and perfusion-weighted imaging. In the region of nonreperfused core, we calculated voxel-based relative FLAIR (rFLAIR) signal intensity. The hypoperfusion intensity ratio was defined as the ratio of the Tmax >10 s lesion over the Tmax >6 s lesion volume. A hypoperfusion intensity ratio threshold of ≤0.4 was used to dichotomize good versus poor collaterals. We studied the interaction between collateral status on the association between time from symptom onset and FLAIR intensity.RESULTS:Time from symptom onset was associated with the rFLAIR intensity in the region of nonreperfused core (B=1.05; 95% confidence interval, 1.0-1.1). We identified an interaction between this association and collateral status; an association was present between time and rFLAIR intensity in patients with poor collaterals (r=0.53), but absent in patients with good collaterals (r=0.17; P=0.04).CONCLUSIONS:Our findings show that the relationship between time from symptom onset and rFLAIR lesion intensity depends on collateral status. In patients with good collaterals, the development of an rFLAIR-positive lesion is less dependent on time from symptom onset compared with patients with poor collaterals.
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| 13. |
- Wouters, Anke, et al.
(författare)
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Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions
- 2018
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Ingår i: European Stroke Journal. - : SAGE Publications. - 2396-9873 .- 2396-9881. ; 3:2, s. 185-192
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Mechanical thrombectomy within 6 h after stroke onset improves the outcome in patients with large vessel occlusions. The aim of our study was to establish a model based on diffusion weighted and perfusion weighted imaging to provide an accurate prediction for the 6 h time-window in patients with unknown time of stroke onset. Patients and methods: A predictive model was designed based on data from the DEFUSE 2 study and validated in a subgroup of patients with large vessel occlusions from the AXIS 2 trial. Results: We constructed the model in 91 patients from DEFUSE 2. The following parameters were independently associated with <6 h time-window and included in the model: interquartile range and median relative diffusion weighted imaging, hypoperfusion intensity ratio, core volume and the interaction between median relative diffusion weighted imaging and hypoperfusion intensity ratio as predictors of the 6 h time-window. The area under the curve was 0.80 with a positive predictive value of 0.90 (95%CI 0.79–0.96). In the validation cohort (N = 90), the area under the curve was 0.73 (P for difference = 0.4) with a positive predictive value of 0.85 (95%CI 0.69–0.95). Discussion: After validation in a larger independent dataset the model can be considered to select patients for endovascular treatment in whom stroke onset is unknown. Conclusion: In patients with large vessel occlusion and unknown time of stroke onset an automated multivariate imaging model is able to select patients who are likely within the 6 h time-window.
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| 14. |
- Wouters, Anke, et al.
(författare)
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Prediction of Stroke Onset is Improved by Relative Fluid-Attenuated Inversion Recovery and Perfusion Imaging Compared to the Visual Diffusion-Weighted Imaging/Fluid-Attenuated Inversion Recovery Mismatch
- 2016
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Ingår i: Stroke: a journal of cerebral circulation. - 0039-2499. ; 47:10, s. 2559-2564
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose - Acute stroke patients with unknown time of symptom onset are ineligible for thrombolysis. The diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR) mismatch is a reasonable predictor of stroke within 4.5 hours of symptom onset, and its clinical usefulness in selecting patients for thrombolysis is currently being investigated. The accuracy of the visual mismatch rating is moderate, and we hypothesized that the predictive value of stroke onset within 4.5 hours could be improved by including various clinical and imaging parameters. Methods - In this study, 141 patients in whom magnetic resonance imaging was obtained within 9 hours after symptom onset were included. Relative FLAIR signal intensity was calculated in the region of nonreperfused core. Mean T max was calculated in the total region with T max >6 s. Mean relative FLAIR, mean T max, lesion volume with T max >6 s, age, site of arterial stenosis, core volume, and location of infarct were analyzed by logistic regression to predict stroke onset time before or after 4.5 hours. Results - Receiver-operating characteristic curve analysis revealed an area under the curve of 0.68 (95% confidence interval 0.59-0.78) for the visual diffusion-weighted imaging/FLAIR mismatch, thereby correctly classifying 69% of patients with an onset time before or after 4.5 hours. Age, relative FLAIR, and T max increased the accuracy significantly (P<0.01) to an area under the curve of 0.82 (95% confidence interval 0.74-0.89). This new predictive model correctly categorized 77% of patients according to stroke onset before versus after 4.5 hours. Conclusions - In patients with unknown stroke onset, the accuracy of predicting time from symptom onset within 4.5 hours is improved by obtaining relative FLAIR and perfusion imaging.
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| 15. |
- Wu, Ona, et al.
(författare)
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Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data
- 2019
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Ingår i: Stroke. - 1524-4628. ; 50:7, s. 1734-1741
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets.
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