Early ADC changes in motor structures predict outcome of acute stroke better than lesion volume

Preserved Corticospinal Tract Revealed by Acute Perfusion Imaging Relates to Better Outcome After Thrombectomy in Stroke

BACKGROUND

The indication for mechanical thrombectomy (MT) in stroke patients with large vessel occlusion has been constantly expanded over the past years. Despite remarkable treatment effects at the group level in clinical trials, many patients remain severely disabled even after successful recanalization. A better understanding of this outcome variability will help to improve clinical decision-making on MT in the acute stage. Here, we test whether current outcome models can be refined by integrating information on the preservation of the corticospinal tract as a functionally crucial white matter tract derived from acute perfusion imaging.

METHODS

We retrospectively analyzed 162 patients with stroke and large vessel occlusion of the anterior circulation who were admitted to the University Medical Center Lübeck between 2014 and 2020 and underwent MT. The ischemic core was defined as fully automatized based on the acute computed tomography perfusion with cerebral blood volume data using outlier detection and clustering algorithms. Normative whole-brain structural connectivity data were used to infer whether the corticospinal tract was affected by the ischemic core or preserved. Ordinal logistic regression models were used to correlate this information with the modified Rankin Scale after 90 days.

RESULTS

The preservation of the corticospinal tract was associated with a reduced risk of a worse functional outcome in large vessel occlusion-stroke patients undergoing MT, with an odds ratio of 0.28 (95% CI, 0.15-0.53). This association was still significant after adjusting for multiple confounding covariables, such as age, lesion load, initial symptom severity, sex, stroke side, and recanalization status.

CONCLUSIONS

A preinterventional computed tomography perfusion-based surrogate of corticospinal tract preservation or disconnectivity is strongly associated with functional outcomes after MT. If validated in independent samples this concept could serve as a novel tool to improve current outcome models to better understand intersubject variability after MT in large vessel occlusion stroke.

Texture analysis of apparent diffusion coefficient maps in predicting the clinical functional outcomes of acute ischemic stroke

Purpose

To investigate texture analysis (TA) based on apparent diffusion coefficient (ADC) map in predicting acute ischemic stroke (AIS) prognosis and discriminating TA features in stroke subtypes.

Methods

This retrospective study included patients with AIS between January 2018 and April 2021. The patients were assigned to the favorable [modified Rankin Scale (mRS) score ≤ 2] and unfavorable (mRS score > 2) outcome groups. All patients underwent stroke subtyping according to the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification. The TA features were extracted from infarction lesions on the ADC map. The demographic characteristics, clinical characteristics, and texture features were used to construct prediction models with recurrent neural network (RNN). The receiver operating characteristic (ROC) curves were implemented to evaluate the performance of the predictive models.

Results

A total of 1,003 patients (682 male; mean age 65.90 ± 12.44) with AIS having documented the 90-day mRS score were identified, including 840 with favorable outcomes. In the validation set, the area under the curve (AUC) of the predictive model using only clinical characteristics achieved an AUC of 0.56, texture model 0.77, the model combining both clinical and texture features showed better with an AUC of 0.78. The texture feature profiles differed between large artery atherosclerosis (LAA) and small artery occlusion (SAO) subtypes (all p < 0.05). The AUC of combined prediction models for LAA and SAO subtypes was 0.80 and 0.81.

Conclusion

Texture analysis based on ADC map could be useful as an adjunctive tool for predicting ischemic stroke prognosis.

Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging

Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionally used biomarkers. We aimed to evaluate the performance and interpretability of a deep learning model based on convolutional neural networks (CNN) in predicting long-term functional outcome with diffusion-weighted imaging (DWI) acquired at day 1 post-stroke. Ischemic stroke patients (n = 322) were included from the ASTER and INSULINFARCT trials as well as the Pitié-Salpêtrière registry. We trained a CNN to predict long-term functional outcome assessed at 3 months with the modified Rankin Scale (dichotomized as good [mRS ≤ 2] vs. poor [mRS ≥ 3]) and compared its performance to two logistic regression models using lesion volume and ASPECTS. The CNN contained an attention mechanism, which allowed to visualize the areas of the brain that drove prediction. The deep learning model yielded a significantly higher area under the curve (0.83 95%CI [0.78-0.87]) than lesion volume (0.78 [0.73-0.83]) and ASPECTS (0.77 [0.71-0.83]) (p < 0.05). Setting all classifiers to the specificity as the deep learning model (i.e., 0.87 [0.82-0.92]), the CNN yielded a significantly higher sensitivity (0.67 [0.59-0.73]) than lesion volume (0.48 [0.40-0.56]) and ASPECTS (0.50 [0.41-0.58]) (p = 0.002). The attention mechanism revealed that the network learned to naturally attend to the lesion to predict outcome.

Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

Objective

To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes.

Materials and Methods

Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses.

Results

Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness.

Conclusion

The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Apparent diffusion coefficient signature of ischemic tissue predicts neurological progression in isolated pontine infarcts

BACKGROUND

Early neurological deterioration is encountered in up to a third of patients with isolated pontine infarcts. A limited number of clinical and imaging features have been suggested as predictors of neurological progression in this setting. In this study, we assessed whether quantitative apparent diffusion coefficient (ADC) measurements within the ischemic pontine region could be used as a radiomic feature to forecast clinical deterioration.

METHODS

We calculated the mean ADC value of ischemic voxels within the ischemic region and normalized them to the contralateral non-ischemic tissue (relative ADC, rADC) in patients with isolated pontine infarcts. This imaging signature was then compared among patients with neurological progression (n = 21) and a propensity matched cohort of non-progressors (n = 42), together with other clinical and imaging features in bivariate and multivariate statistical models.

RESULTS

The rADCmean was significantly lower among patients with progression (p = 0.008). Female gender and extension of the ischemic lesion to the ventral pontine surface were other features significantly associated with progression. The association between rADCmean and progression persisted in multivariate models with an odds ratio of 13.7 (95% CI 2.6-72.8; p = 0.002) for progression among patients with rADCmean ≤ 0.67 in their ischemic tissue. The probability for worsening was 80% among patients who had an ischemic lesion extending to the ventral pontine surface with a mean rADC ≤ 0.67.

CONCLUSION

The mean rADC value within the ischemic lesion is closely related with early neurological deterioration in patients with isolated pontine infarcts.

Longitudinal MRI dynamics of recent small subcortical infarcts and possible predictors

We aimed to explore the morphological evolution of recent small subcortical infarcts (RSSIs) over 15 months. Moreover, we hypothesized that quantitative lesion apparent diffusion coefficient (ADC) values and serum neurofilament light (NfL) levels predict subsequent lacunar cavitation. We prospectively studied 78 RSSI patients, who underwent pre-defined follow-up investigations three and 15 months poststroke using 3 T MRI including high-resolution T1 sequences. To identify potential predictors of cavitation, we determined RSSI size and quantitative ADC values, and serum NfL using the SIMOA technique. The majority of RSSIs showed cavitation at three months (n = 61, 78%) with only minimal changes regarding cavitation status thereafter. The maximum axial lacunar diameter decreased from 8 mm at three to 7 mm at 15 months (p < 0.05). RSSIs which cavitated had lower lesional ADC values and were associated with higher baseline NfL levels compared to those without cavitation, but did not differ regarding lesion size. In logistic regression analysis, only baseline NfL levels predicted cavitation (p = 0.017). In this prospective study using predefined high-resolution MRI protocols, the majority of RSSIs evolved into lacunes during the first three months poststroke with not much change thereafter. Serum NfL seems to be a promising biomarker for more advanced subsequent tissue destruction in RSSIs.

Impact of Ischemic Lesion Location on the mRS Score in Patients with Ischemic Stroke: A Voxel-Based Approach

BACKGROUND AND PURPOSE

Previous studies indicated that ischemic lesion volume might be a useful surrogate marker for functional outcome in ischemic stroke but should be considered in the context of lesion location. In contrast to previous studies using the ROI approach, which has several drawbacks, the present study aimed to measure the impact of ischemic lesion location on functional outcome using a more precise voxelwise approach.

MATERIALS AND METHODS

Datasets of patients with acute ischemic strokes from the Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands (MR CLEAN) were used. Primary outcome was functional outcome as assessed by the modified Rankin Scale 3 months after stroke. Ischemic lesion volume was determined on CT scans 3-9 days after stroke. Voxel-based lesion-symptom mapping techniques, including covariates that are known to be associated with functional outcome, were used to determine the impact of ischemic lesion location for outcome.

RESULTS

Of the 500 patients in the MR CLEAN trial, 216 were included for analysis. The mean age was 63 years. Lesion-symptom mapping with inclusion of covariates revealed that especially left-hemispheric lesions in the deep periventricular white matter and adjacent internal capsule showed a great influence on functional outcome.

CONCLUSIONS

Our study confirms that infarct location has an important impact on functional outcome of patients with stroke and should be considered in prediction models. After we adjusted for covariates, the left-hemispheric corticosubcortical fiber tracts seemed to be of higher functional importance compared with cortical lesions.

Development and validation of a penumbra-based predictive model for thrombolysis outcome in acute ischemic stroke patients

The use of thrombolysis in acute ischemic stroke is restricted to a small proportion of patients because of the rigid 4·5-h window. With advanced imaging-based patient selection strategy, rescuing penumbra is critical to improving clinical outcomes. In this study, we included 155 acute ischemic stroke patients (84 patients in training dataset, age from 43 to 80, 59 males; 71 patients in validation dataset, age from 36 to 80, 45 males) who underwent MR scan within the first 9-h after onset, from 7 independent centers. Based on the mismatch concept, penumbra and core area were identified and quantitatively analyzed. Moreover, predictive models were developed and validated to provide an approach for identifying patients who may benefit from thrombolytic therapy. Predictive models were constructed, and corresponding areas under the curve (AUC) were calculated to explore their performances in predicting clinical outcomes. Additionally, the models were validated using an independent dataset both on Day-7 and Day-90. Significant correlations were detected between the mismatch ratio and clinical assessments in both the training and validation datasets. Treatment option, baseline systolic blood pressure, National Institutes of Health Stroke Scale score, mismatch ratio, and three regional radiological parameters were selected as biomarkers in the combined model to predict clinical outcomes of acute ischemic stroke patients. With the external validation, this predictive model reached AUCs of 0·863 as short-term validation and 0·778 as long-term validation. This model has the potential to provide quantitative biomarkers that aid patient selection for thrombolysis either within or beyond the current time window.

Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review

Background There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked "high" in overall evidence quality. Conclusions There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.

Hyperglycaemia, insulin therapy and critical penumbral regions for prognosis in acute stroke: further insights from the INSULINFARCT trial

BACKGROUND

Recently, the concept of 'clinically relevant penumbra' was defined as an area saved by arterial recanalization and correlated with stroke outcome. This clinically relevant penumbra was located in the subcortical structures, especially the periventricular white matter. Our aims were to confirm this hypothesis, to investigate the impact of admission hyperglycemia and of insulin treatment on the severity of ischemic damages in this area and to study the respective contributions of infarct volume and ischemic damage severity of the clinically relevant penumbra on 3-month outcome.

METHODS

We included 99 patients from the INSULINFARCT trial. Voxel-Based Analysis was carried on the Apparent Diffusion Coefficient (ADC) maps obtained at day one to localize the regions, which were more damaged in patients i) with poor clinical outcomes at three months and ii) without arterial recanalization. We determined the intersection of the detected areas, which represents the clinically relevant penumbra and investigated whether hyperglycemic status and insulin regimen affected the severity of ischemic damages in this area. We performed logistic regression to examine the contribution of infarct volume or early ADC decrease in this strategic area on 3-month outcome.

FINDINGS

Lower ADC values were found in the corona radiata in patients with poor prognosis (p< 0.0001) and in those without arterial recanalization (p< 0.0001). The tracking analysis showed that lesions in this area interrupted many important pathways. ADC values in this area were lower in hyperglycemic than in normoglycemic patients (average decrease of 41.6 ± 20.8 x10(-6) mm2/s) and unaffected by the insulin regimen (p: 0.10). ADC values in the clinically relevant penumbra, but not infarct volumes, were significant predictors of 3-month outcome.

CONCLUSION

These results confirm that the deep hemispheric white matter is part of the clinically relevant penumbra and show that hyperglycaemia exacerbates the apparition of irreversible ischemic damage within 24 hours in this area. However, early intensive insulin therapy fails to protect this area from infarction.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00472381.

Infarct volume prediction using apparent diffusion coefficient maps during middle cerebral artery occlusion and soon after reperfusion in the rat

Middle cerebral artery occlusion (MCAO) in rodents causes brain infarctions of variable sizes that depend on multiple factors, particularly in models of ischemia/reperfusion. This is a major problem for infarct volume comparisons between different experimental groups since unavoidable variability can induce biases in the results and imposes the use of large number of subjects. MRI can help to minimize these difficulties by ensuring that the severity of ischemia is comparable between groups. Furthermore, several studies showed that infarct volumes can be predicted with MRI data obtained soon after ischemia onset. However, such predictive studies require multiparametric MRI acquisitions that cannot be routinely performed, and data processing using complex algorithms that are often not available. The aim here was to provide a simplified method for infarct volume prediction using apparent diffusion coefficient (ADC) data in a model of transient MCAO in rats. ADC images were obtained before, during MCAO and after 60 min of reperfusion. Probability histograms were generated using ADC data obtained either during MCAO, after reperfusion, or both combined. The results were compared to real infarct volumes, i.e.T2 maps obtained at day 7. Assessment of the performance of the estimations showed better results combining ADC data obtained during occlusion and at reperfusion. Therefore, ADC data alone can provide sufficient information for a reasonable prediction of infarct volume if the MRI information is obtained both during the occlusion and soon after reperfusion. This approach can be used to check whether drug administration after MRI acquisition can change infarct volume prediction.

The ischemic penumbra: the location rather than the volume of recovery determines outcome

PURPOSE OF REVIEW

The clinical efficiency of thrombolysis is explained by the rescue of ischemic penumbra areas resulting from early arterial recanalization. The perfusion-diffusion weighted imaging mismatch is a commonly used MRI surrogate of the ischemic penumbra. However, the randomized trials testing the mismatch hypothesis have been negative. We will review the 'mismatch concept' and the recent studies that aim to localize the clinically eloquent areas of penumbra in middle cerebral artery (MCA) infarcts.

RECENT FINDINGS

New methods of image analysis have shown that poor outcomes after MCA stroke are related to infarction of an extremely well localized area of the periventricular white matter and adjacent internal capsule, where projections and association tracts are crossing and converging. This area almost colocalizes with the area salvaged by early arterial recanalization and is located extremely close to the initial ischemic core.

SUMMARY

The location of the area that correlates with disability in MCA stroke patients and that is salvaged by early arterial recanalization is in the same specific region of the deep white matter, close to the initial ischemic core. These findings may have important implications for designing new recanalization trials and support the importance of basic research on white-matter neuroprotection.

Pediatric brain injury: can DTI scalars predict functional outcome?

Diffusion imaging has made significant inroads into the clinical diagnosis of a variety of diseases by inferring changes in microstructure, namely cell membranes, myelin sheath and other structures that inhibit water diffusion. This review discusses recent progress in the use of diffusion parameters in predicting functional outcome. Studies in the literature using only scalar parameters from diffusion measurements, such as apparent diffusion coefficient (ADC) and fractional anisotropy (FA), are summarized. Other more complex mathematical models and post-processing uses are also discussed briefly.

[Diffusion-Weighted Imaging infarct volume and neurologic outcomes after ischemic stroke]

BACKGROUND

This study's purpose was to demonstrate a relationship between growth of stroke volume measured on Diffusion-Weighted MRI (DWI) at admission (baseline) and 24 hours later (follow-up) and functional outcome at 90 days evaluated by the modified Rankin Scale (mRS).

METHODS

DWI infarct volumes were calculated, using an Analyze Software. Clinical outcomes were assessed at 90 days by the mRS. Univariate regression analysis was performed to assess the relationship between changes in DWI lesion volume and mRS less or equal to 1.

RESULTS

Sixty-nine cases had serial DWI scans with a measurable lesion at baseline and follow-up. The median baseline National Institutes of Health Stroke Scale (NIHSS) was 10, 5 and 7 at 24 hours. At 90 days, the proportion of patients with Rankin less or equal to 1 was 51.7%. The average baseline volume was 19.7 cm(3) and average follow-up volume was 46.1 cm(3). For each 10 cm(3) of growth in DWI infarct volume, the odds ratio for a mRS less or equal to 1 was 10,1 (IC 95%, 3-33.9).

CONCLUSION

The results of this study provide evidence of a significant inverse relationship between infarct growth measured by DWI and good functional clinical outcome at 90 days.

Quantification of brain edema and hemorrhage by MRI after experimental traumatic brain injury in rabbits predicts subsequent functional outcome

We use multiple MRI modalities to measure cerebral edema and intracerebral hemorrhage quantitatively after TBI in rabbits and to acquire the early prognostic MRI information. Multiple MRI modalities (DCE-MRI, DWI and SWI) were used to assess cerebral edema and intracerebral hemorrhage quantitatively at different time points within a month after TBI in 15 rabbits. The functional outcomes were evaluated at 1 and 30 days after TBI. The relationships between the quantitative MRI information at different time points and functional outcome at 30 days were analyzed. The volume transfer coefficient (K (trans)) in the focal lesion area and the perifocal lesion area at the acute phase correlated with the functional outcome at 30 days (p < 0.05). The apparent diffusion coefficient (ADC) value at 7 days in the focal lesion area correlated with the functional outcome at 30 days (p < 0.01) and had a trend to correlate at 3 days (p = 0.08). In the perifocal lesion area, the ADC values at both acute and subacute phase correlated with the functional outcome at 30 days (p < 0.05). The volume of hemorrhage correlated with functional outcome at 30 days (p < 0.05). The cerebral edema assessed by DCE-MRI (K (trans)) and DWI (ADC) and intracerebral hemorrhage assessed by SWI may have predictive values.