Diffusion-Weighted Imaging and Fluid-Attenuated Inversion Recovery Quantification to Predict Diffusion-Weighted Imaging-Fluid-Attenuated Inversion Recovery Mismatch Status in Ischemic Stroke With Unknown Onset

BACKGROUND

Visual rating of diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch can be challenging. We evaluated quantification of DWI and FLAIR to predict DWI-FLAIR mismatch status in ischemic stroke.

METHODS

In screened patients from the WAKE-UP trial (Efficacy and Safety of Magnetic Resonance Imaging-Based Thrombolysis in Wake-Up Stroke), we retrospectively studied relative DWI (rDWI SI) and FLAIR signal intensity (rFLAIR SI). We defined the optimal mean rFLAIR SI and interquartile range of the rDWI SI in the DWI lesion to predict DWI-FLAIR mismatch status. We investigated agreement between each quantitative parameter and the DWI-FLAIR mismatch and the association between both quantitative parameters. We evaluated the predictive value of the quantitative parameters for excellent functional outcome by logistic regression, adjusted for DWI lesion volume, treatment, age, and National Institutes of Health Stroke Scale score.

RESULTS

In the rFLAIR and rDWI SI analysis, 213/369 and 241/421 subjects respectively had a DWI-FLAIR mismatch. A mean rFLAIR SI cutoff of 1.09 and interquartile range rDWI SI cutoff of 0.47 were optimal to predict the DWI-FLAIR mismatch with a sensitivity and specificity of 77% (95% CI, 71%-83%) and 67% (95% CI, 59%-74%), and 76% (95% CI, 70%-81%) and 72% (95% CI, 65%-79%), respectively. For both quantitative parameters, agreement with the DWI-FLAIR mismatch was fair (73%, κ=0.44 [95% CI, 0.35-0.54] for rFLAIR and 74%, κ=0.48 [95% CI, 0.39-0.56] for rDWI). Both quantitative parameters correlated moderately (Pearson R=0.54 [95% CI, 0.46-0.61], P<0.001, n=367). The interquartile range rDWI SI (n=188), but not the mean rFLAIR SI (n=172), was an independent predictor of excellent functional outcome (odds ratio, 0.67 per 0.1 unit increase of interquartile range rDWI SI, 95% CI, 0.51-0.89, P=0.01).

CONCLUSIONS

Agreement between the quantitative and qualitative approach may be insufficient to advocate DWI or FLAIR quantification as alternative for visual rating.

Low relative diffusion weighted image signal intensity can predict good prognosis after endovascular thrombectomy in patients with acute ischemic stroke

BACKGROUND

It is vital to identify a surrogate last-known-well time to perform proper endovascular thrombectomy in acute ischemic stroke; however, no established imaging biomarker can easily and quickly identify eligibility for endovascular thrombectomy and predict good clinical prognosis.

OBJECTIVE

To investigate whether low relative diffusion-weighted imaging (DWI) signal intensity can be used as a predictor of good clinical outcome after endovascular thrombectomy in patients with acute ischemic stroke.

METHODS

We retrospectively identified consecutive patients with acute ischemic stroke who were treated with endovascular thrombectomy within 24 hours of the last-known-well time and achieved successful recanalization (modified Thrombolysis in Cerebral Infarction score ≥2b). Relative DWI signal intensity was calculated as DWI signal intensity in the infarcted area divided by DWI signal intensity in the contralateral hemisphere. Good prognosis was defined as a modified Rankin Scale score of 0-2 at 90 days after stroke onset (good prognosis group).

RESULTS

49 patients were included in the analysis. Relative DWI signal intensity was significantly lower in the group with good prognosis than in the those with poor prognosis (median (IQR) 1.32 (1.27-1.44) vs 1.56 (1.43-1.66); p<0.01), and the critical cut-off value for predicting good prognosis was 1.449 (area under the curve 0.78). Multiple logistic regression analysis revealed association of good prognosis after endovascular thrombectomy with low relative DWI signal intensity (OR=6.84; 95% CI 1.13 to 41.3; p=0.04).

CONCLUSIONS

Low relative DWI signal intensity was associated with good prognosis after endovascular thrombectomy. Its ability to predict good clinical outcome shows potential for determining patient suitability for endovascular thrombectomy.

A Comparison of T2 Relaxation-Based MRI Stroke Timing Methods in Hyperacute Ischemic Stroke Patients: A Pilot Study

Background:

T₂ relaxation-based magnetic resonance imaging (MRI) signals may provide onset time for acute ischemic strokes with an unknown onset. The ability of visual and quantitative MRI-based methods in a cohort of hyperacute ischemic stroke patients was studied.

Methods:

A total of 35 patients underwent 3T (3 Tesla) MRI (<9-hour symptom onset). Diffusion-weighted (DWI), apparent diffusion coefficient (ADC), T₁-weighted (T₁w), T₂-weighted (T₂w), and T₂ relaxation time (T₂) images were acquired. T₂-weighted fluid attenuation inversion recovery (FLAIR) images were acquired for 17 of these patients. Image intensity ratios of the average intensities in ischemic and non-ischemic reference regions were calculated for ADC, DWI, T₂w, T₂ relaxation, and FLAIR images, and optimal image intensity ratio cut-offs were determined. DWI and FLAIR images were assessed visually for DWI/FLAIR mismatch.

Results:

The T₂ relaxation time image intensity ratio was the only parameter with significant correlation with stroke duration (r = 0.49, P = .003), an area under the receiver operating characteristic curve (AUC = 0.77, P < .0001), and an optimal cut-off (T₂ ratio = 1.072) that accurately identified patients within the 4.5-hour thrombolysis treatment window with sensitivity of 0.74 and specificity of 0.74. In the patients with the additional FLAIR, areas under the precision-recall-gain curve (AUPRG) and F₁ scores showed that the T₂ relaxation time ratio (AUPRG = 0.60, F₁ = 0.73) performed considerably better than the FLAIR ratio (AUPRG = 0.39, F₁ = 0.57) and the visual DWI/FLAIR mismatch (F₁ = 0.25).

Conclusions:

Quantitative T₂ relaxation time is the preferred MRI parameter in the assessment of patients with unknown onset for treatment stratification.

Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions

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.

Thrombectomy or intravenous thrombolysis in patients with NIHSS of 5 or less?

BACKGROUND AND PURPOSE

To compare outcomes of minor stroke patients with intracranial vessel occlusions (IVO) underwent mechanical thrombectomy (MT) versus those treated with intravenous thrombolysis alone (IVT).

METHODS

We retrospectively reviewed two large prospective stroke databases from two European centers searching for patients admitted with minor stroke (i.e. NIHSS Score░≤░5), baseline mRS░=░0 and occlusion of the M1-M2 segment of the middle cerebral artery (MCA). Groups receiving (A) IVT alone and (B) MT+/-IVT were compared. Primary outcome measures were MT safety, successful recanalization rate (mTICI 2b-3) and NIHSS shift (discharge NIHSS minus admission NIHSS); secondary outcomes included discharge rates and excellent outcome (mRS 0-1) at 3 months. Univariate and multivariate analyses were performed.

RESULTS

Thirty-two patients were enrolled in Group B (19░MT alone; 13 MT░+░IVT) and 24 in Group A. Successful recanalization (mTICI 2b-3) was obtained in 100% of cases in Group B vs 38% in Group A. Symptomatic hemorrhagic transformation rate did not differ between the two groups. Multivariate analysis reported MT as the only predictor of early (<░12░h) favorable NIHSS shift and lower NIHSS at discharge. Moreover, discharge at home and excellent outcome at 3-month follow-up were statistically associated with MT.

CONCLUSIONS

MT in patients with minor strokes and intracranial vessel occlusion (IVO) is safe and can determine a rapid improvement of NIHSS Score. MT seems also associated with a higher rate of patients discharged at home after hospitalization and better clinical outcome at 3-month follow-up. Larger randomized trials are warranted to confirm these results.

Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours

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.

Determining Stroke Onset Time Using Quantitative MRI: High Accuracy, Sensitivity and Specificity Obtained from Magnetic Resonance Relaxation Times

Many ischaemic stroke patients are ineligible for thrombolytic therapy due to unknown onset time. Quantitative MRI (qMRI) is a potential surrogate for stroke timing. Rats were subjected to permanent middle cerebral artery occlusion and qMRI parameters including hemispheric differences in apparent diffusion coefficient, T₂-weighted signal intensities, T₁ and T₂ relaxation times (qT₁, qT₂) and f₁, f₂ and V_overlap were measured at hourly intervals at 4.7 or 9.4 T. Accuracy and sensitivity for identifying strokes scanned within and beyond 3 h of onset was determined. Accuracy for V_overlap, f₂ and qT₂ (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for V_overlap (0.90) and f₂ (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time.