Dataset Information

Can early ischemic lesion recurrence on diffusion-weighted MRI affect functional outcome after acute ischemic stroke?

ABSTRACT:

Background and purpose

Ischemic lesion recurrence on diffusion-weighted imaging (DWI-LR) is a frequently observed phenomenon after acute ischemic stroke. However, no study has elucidated the impact of DWI-LR on functional outcome.

Methods

Among a consecutive series of patients who presented with focal symptoms or signs compatible with stroke within 48 hours from the onset over a 50-month period, those who had relevant ischemic lesions on initial DWI and underwent follow-up DWI within 14 days after the onset were enrolled in this study. As outcome variables, the scores on the modified Rankin Disability Scale (mRDS) at 3 months and 1 year were measured prospectively and dichotomized into good (0-2) vs. poor (3-6). When calculating odds ratios (ORs), adjustment was performed for age, previous stroke, initial score on the NIH Stroke Scale, stroke subtype, and IV thrombolysis.

Results

Among those 786 patients finally enrolled in this study, 221 (28.1%) had DWI-LR. For a poor outcome at 3 months, the crude ORs of any, symptomatic, and asymptomatic DWI-LR were 2.70 [95% confidence interval (CI), 1.96 to 3.72], 10.03 (95% CI, 4.39 to 22.96), and 2.04 (95% CI, 1.44 to 2.88), respectively. With adjustment, the OR of symptomatic DWI-LR was 6.44 (95% CI, 2.50 to 16.57), whereas those of any and asymptomatic DWI-LR lost their statistical significance: 1.44 (95% CI, 0.94 to 2.20) and 1.04 (95% CI, 0.65 to 1.65), respectively. Analyzing with the 1-year outcome produced similar results.

Conclusions

This study shows that symptomatic early lesion recurrence can affect functional outcome after acute ischemic stroke, whereas an asymptomatic one may not.

SUBMITTER: Kim WJ

PROVIDER: S-EPMC2851294 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

Can early ischemic lesion recurrence on diffusion-weighted MRI affect functional outcome after acute ischemic stroke?

Kim Wook-Joo WJ Kim Jae Hyoung JH Ko Youngchai Y Park Jung Hyun JH Yang Mi Hwa MH Jang Myung Suk MS Han Moon-Ku MK Kim Sang Yun SY Park Seong Ho SH Bae Hee-Joon HJ

Journal of clinical neurology (Seoul, Korea) 20100326 1

<h4>Background and purpose</h4>Ischemic lesion recurrence on diffusion-weighted imaging (DWI-LR) is a frequently observed phenomenon after acute ischemic stroke. However, no study has elucidated the impact of DWI-LR on functional outcome.<h4>Methods</h4>Among a consecutive series of patients who presented with focal symptoms or signs compatible with stroke within 48 hours from the onset over a 50-month period, those who had relevant ischemic lesions on initial DWI and underwent follow-up DWI wit ...[more]

PMID: 20386639

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Project description:BackgroundAn accurate prediction of tissue outcome in acute ischemic stroke patients is of high interest for treatment decision making. To date, various machine learning models have been proposed that combine multi-parametric imaging data for this purpose. However, most of these machine learning models were trained using voxel information extracted from the whole brain, without taking differences in susceptibility to ischemia into account that exist between brain regions. The aim of this study was to develop and evaluate a local tissue outcome prediction approach, which makes predictions using locally trained machine learning models and thus accounts for regional differences.Material and methodsMulti-parametric MRI data from 99 acute ischemic stroke patients were used for the development and evaluation of the local tissue outcome prediction approach. Diffusion (ADC) and perfusion parameter maps (CBF, CBV, MTT, Tmax) and corresponding follow-up lesion masks for each patient were registered to the MNI brain atlas. Logistic regression (LR) and random forest (RF) models were trained employing a local approach, which makes predictions using models individually trained for each specific voxel position using the corresponding local data. A global approach, which uses a single model trained using all voxels of the brain, was used for comparison. Tissue outcome predictions resulting from the global and local RF and LR models, as well as a combined (hybrid) approach were quantitatively evaluated and compared using the area under the receiver operating characteristic curve (ROC AUC), the Dice coefficient, and the sensitivity and specificity metrics.ResultsStatistical analysis revealed the highest ROC AUC and Dice values for the hybrid approach. With 0.872 (ROC AUC; LR) and 0.353 (Dice; RF), these values were significantly higher (p < 0.01) than the values of the two other approaches. In addition, the local approach achieved the highest sensitivity of 0.448 (LR). Overall, the hybrid approach was only outperformed in sensitivity (LR) by the local approach and in specificity by both other approaches. However, in these cases the effect sizes were comparatively small.ConclusionThe results of this study suggest that using locally trained machine learning models can lead to better lesion outcome prediction results compared to a single global machine learning model trained using all voxel information independent of the location in the brain.

Project description:PurposeTo investigate radiomics based on DWI (diffusion-weighted imaging) for predicting 1-year ischemic stroke recurrence.MethodsA total of 1,580 ischemic stroke patients were enrolled in this retrospective study conducted from January 2018 to April 2021. Demographic and clinical characteristics were compared between recurrence and non-recurrence groups. On DWI, lesions were segmented using a 2D U-Net automatic segmentation network. Further, radiomics feature extraction was done using the segmented mask matrix on DWI and the corresponding ADC map. Additionally, radiomics features were extracted. The study participants were divided into a training cohort (n = 157, 57 recurrence patients, and 100 non-recurrence patients) and a test cohort (n = 846, 28 recurrence patients, 818 non-recurrence patients). A sparse representation feature selection model was performed to select features. Further classification was accomplished using a recurrent neural network (RNN). The area under the receiver operating characteristic curve values was obtained for model performance.ResultsA total of 1,003 ischemic stroke patients (682 men and 321 women; mean age: 65.90 ± 12.44 years) were included in the final analysis. About 85 patients (8.5%) recurred in 1 year, and patients in the recurrence group were older than the non-recurrence group (P = 0.003). The stroke subtype was significantly different between recurrence and non-recurrence groups, and cardioembolic stroke (11.3%) and large artery atherosclerosis patients (10.3%) showed a higher recurrence percentage (P = 0.005). Secondary prevention after discharge (statins, antiplatelets, and anticoagulants) was found significantly different between the two groups (P = 0.004). The area under the curve (AUC) of clinical-based model and radiomics-based model were 0.675 (95% CI: 0.643-0.707) and 0.779 (95% CI: 0.750-0.807), respectively. With an AUC of 0.847 (95% CI: 0.821-0.870), the model that combined clinical and radiomic characteristics performed better.ConclusionDWI-based radiomics could help to predict 1-year ischemic stroke recurrence.

Project description:Background and aims:Assessment of ischemic lesions on computed tomography or MRI diffusion-weighted imaging (DWI) using the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is widely used to guide acute stroke treatment. However, it has never been defined how many voxels need to be affected to label a DWI-ASPECTS region ischemic. We aimed to assess the effect of various lesion load thresholds on DWI-ASPECTS and compare this automated analysis with visual rating. Materials and methods:We analyzed overlap of individual DWI lesions of 315 patients from the previously published predictive value of fluid-attenuated inversion recovery study with a probabilistic ASPECTS template derived from 221 CT images. We applied multiple lesion load thresholds per DWI-ASPECTS region (>0, >1, >10, and >20% in each DWI-ASPECTS region) to compute DWI-ASPECTS for each patient and compared the results to visual reading by an experienced stroke neurologist. Results:By visual rating, median ASPECTS was 9, 84 patients had a DWI-ASPECTS score ?7. Mean DWI lesion volume was 22.1 (±35) ml. In contrast, by use of >0, >1-, >10-, and >20%-thresholds, median DWI-ASPECTS was 1, 5, 8, and 10; 97.1% (306), 72.7% (229), 41% (129), and 25.7% (81) had DWI-ASPECTS ?7, respectively. Overall agreement between automated assessment and visual rating was low for every threshold used (>0%: ?w?=?0.020 1%: ?w?=?0.151; 10%: ?w?=?0.386; 20% ?w?=?0.381). Agreement for dichotomized DWI-ASPECTS ranged from fair to substantial (?7: >10% ??=?0.48; >20% ??=?0.45; ?5: >10% ??=?0.528; and >20% ??=?0.695). Conclusion:Overall agreement between automated and the standard used visual scoring is low regardless of the lesion load threshold used. However, dichotomized scoring achieved more comparable results. Varying lesion load thresholds had a critical impact on patient selection by ASPECTS. Of note, the relatively low lesion volume and lack of patients with large artery occlusion in our cohort may limit generalizability of these findings.

Project description:Background and purposeLesions on diffusion-weighted imaging (DWI) occasionally appear on follow-up magnetic resonance imaging (MRI) among initially DWI-negative but clinically suspicious stroke patients. We established the prevalence of positive conversion in DWI-negative stroke and determined the clinical factors associated with it.MethodsThis retrospective, observational, single-center study included 5,271 patients hospitalized due to stroke/transient ischemic attack (TIA) in a single university hospital during 2010 to 2017. Patients without initial DWI lesions underwent follow-up DWI imaging as a routine practice. Adjusted hazard ratios (aHRs) for recurrent stroke risk according to positive conversion were determined using Cox proportional hazard regression. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for positive conversion among initially DWI-negative patients were estimated.ResultsIn total, 694 (13.2%) patients (mean±standard deviation age, 62.9±13.7 years; male, 404 [58.2%]) were initially DWI-negative. Among them, 22.5% had positive-conversion on follow-up DWI. Positive conversion was associated with a higher risk of recurrent stroke (aHR, 3.12; 95% CI, 1.56 to 6.26). Early neurologic deterioration (aOR, 15.1; 95% CI, 5.71 to 47.66), atrial fibrillation (aOR, 6.17; 95% CI, 3.23 to 12.01), smoking (aOR, 3.76; 95% CI, 2.19 to 6.63), pre-stroke dependency (aOR, 1.62; 95% CI, 1.15 to 2.27), objective hemiparesis (aOR, 4.39; 95% CI, 1.90 to 10.32), longer symptom duration (aOR, 2.17; 95% CI, 1.57 to 3.08), high cholesterol (aOR, 4.70; 95% CI, 1.78 to 12.77), National Institutes of Health Stroke Scale score (aOR, 1.44; 95% CI, 1.08 to 1.91), and high systolic blood pressure (aOR, 1.01; 95% CI, 1.00 to 1.02) were associated with a higher incidence of lesions with delayed appearance. Regarding the location of lesions on follow-up DWI, 34.6% and 21.2% were in the cortex and brainstem, respectively.ConclusionsIn DWI-negative stroke/TIA, positive conversion is associated with a higher risk of recurrent stroke. DWI-negative stroke with factors related to positive conversion may require follow-up MRI for a definitive diagnosis.

Dataset Information

Can early ischemic lesion recurrence on diffusion-weighted MRI affect functional outcome after acute ischemic stroke?

Background and purpose

Methods

Results

Conclusions

Publications

Can early ischemic lesion recurrence on diffusion-weighted MRI affect functional outcome after acute ischemic stroke?

Similar Datasets

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