Advanced imaging in acute ischemic stroke of unknown onset

Stroke of unknown onset accounts for up to 20% of all acute ischemic stroke. Prior to the successful completion of the Efficacy and Safety of MRI-Based Thrombolysis in WAKE UP Stroke (WAKE UP) trial, these patients were typically excluded from treatment with IV tPA as this therapy was only approved for cases within 4.5 hours of known symptom onset. WAKE UP utilized a novel imaging biomarker of lesion age, the DWI-FLAIR mismatch (acute stroke visible on DWI but not yet visible on FLAIR), to allocate patients into the early time window for which thrombolysis has been proven safe and efficient; a concept which became known as “tissue clocking”. As a multicenter and imaging-heavy trial, WAKE UP relied upon a homogeneous understanding and interpretation of its imaging criteria by all of its many investigators, a process that was safeguarded by dedicated training developed especially for the study’s purposes. The study was successful and, upon its completion in 2017, together with two smaller and similar trials that were completed at comparable time points, WAKE UP generated enough high quality evidence to influence a change in official guidelines, now recommending thrombolysis for patients with stroke of unknown onset who satisfy WAKE UP criteria. Various sub-analyses conducted since on the WAKE UP cohort further cemented the credibility of tissue clocking as a patient selection paradigm. But it is not the only such model. In addition to tissue clocking another concept, dubbed penumbral imaging and used as a biomarker of tissue at risk of infarction, has also been investigated in large clinical trials such as EXTEND and ECASS-4, as a way to offer treatment to patients with unknown symptom onset. Both of these methods fall under the umbrella of advanced imaging because they necessitate hardware and/or software as well as expertise in image interpretation that is not routinely available in the majority of the world’s hospitals. Tissue clocking (using magnetic resonance imaging and the DWI-FLAIR mismatch) as well as penumbral imaging (using MR or CT based perfusion imaging) offer a lot of additional information, and through it, assurance to the treating physician that potential risks have been minimized and possible benefits of therapy enhanced. In this sense, advanced brain imaging should definitely be considered as part of state of the art, evidence based stroke treatment. Especially in the unknown time window, and due to its ability to perform both tissue clocking and penumbral imaging, MRI as a modality has been proposed as the most inclusive approach to screening ischemic stroke patients in hopes of identifying those still eligible for thrombolytic treatment. However, this approach clearly suffers the drawback of limited availability in everyday clinical practice. Further, well-designed and well-conducted prospective, randomized, controlled trials should be performed to evaluate the exact scope of (advanced) imaging needed for an as-inclusive-as-possible and successful patient selection in the unknown time window

Total perfusion-diffusion mismatch detected using resting-state functional MRI

Total perfusion-diffusion mismatch is a well-recognised phenomenon in patients with acute ischaemic stroke. We describe a case of total perfusion-diffusion mismatch detected using an emerging contrast-agent-free perfusion imaging technique in a young patient with acute cerebellar stroke

The biennial cycle of respiratory syncytial virus outbreaks in Croatia

The paper analyses the epidemic pattern of respiratory syncytial virus (RSV) outbreaks in children in Croatia. Over a period of 11 consecutive winter seasons (1994–2005) 3,435 inpatients from Zagreb County aged from infancy to 10 years who were hospitalised with acute respiratory tract infections were tested for RSV-infection. RSV was identified in nasopharyngeal secretions of patients by virus isolation in cell culture and by detection of viral antigen with monoclonal antibodies

The ratio between cerebral blood flow and Tmax predicts the quality of collaterals in acute ischemic stroke

Background In acute ischemic stroke the status of collateral circulation is a critical factor in determining outcome. We propose a less invasive alternative to digital subtraction angiography for evaluating collaterals based on dynamic-susceptibility contrast magnetic resonance imaging. Methods Perfusion maps of Tmax and cerebral blood flow (CBF) were created for 35 patients with baseline occlusion of a major cerebral artery. Volumes of hypoperfusion were defined as having a Tmax delay of > 4 seconds (Tmax4s) and > 6 seconds (Tmax6s) and a CBF drop below 80% of healthy, contralateral tissue. For each patient a ratio between the volume of the CBF and the Tmax based perfusion deficit was calculated. Associations with collateral status and radiological outcome were assessed with the Mann-Whitney-U test, uni- and multivariable logistic regression analyses as well as area under the receiver-operator- characteristic (ROC) curve. Results The CBF/Tmax volume ratios were significantly associated with bad collateral status in crude logistic regression analysis as well as with adjustment for NIHSS at admission and baseline infarct volume (OR = 2.5 95% CI[1.2–5.4] p = 0.020 for CBF/Tmax 4s volume ratio and OR = 1.6 95% CI[1.0–2.6] p = 0.031 for CBF/Tmax6s volume ratio). Moreover, the ratios were significantly correlated to final infarct size (Spearman’s rho = 0.711 and 0.619, respectively for the CBF/Tmax4s volume ratio and CBF/Tmax6s volume ration, all p<0.001). The ratios also had a high area under the ROC curve of 0.93 95%CI[0.86–1.00]) and 0.90 95%CI[0.80–1.00]respectively for predicting poor radiological outcome. Conclusions In the setting of acute ischemic stroke the CBF/Tmax volume ratio can be used to differentiate between good and insufficient collateral circulation without the need for invasive procedures like conventional angiography

The Association Between Recanalization, Collateral Flow, and Reperfusion in Acute Stroke Patients: A Dynamic Susceptibility Contrast MRI Study

Background: Collateral circulation in ischemic stroke patients plays an important role in infarct evolution und assessing patients' eligibility for endovascular treatment. By means of dynamic susceptibility contrast MRI, we aimed to investigate the effects of reperfusion, recanalization, and collateral flow on clinical and imaging outcomes after stroke. Methods: Retrospective analysis of 184 patients enrolled into the prospective observational 1000Plus study (clinicaltrials.org NCT00715533). Inclusion criteria were vessel occlusion on baseline MR-angiography, imaging within 24 h after stroke onset and follow-up perfusion imaging. Baseline Higashida score using subtracted dynamic MR perfusion source images was used to quantify collateral flow. The influence of these variables, and their interaction with vessel recanalization, on clinical and imaging outcomes was assessed using robust linear regression. Results: Ninety-eight patients (53.3%) showed vessel recanalization. Higashida score (p = 0.002), and recanalization (p = 0.0004) were independently associated with reperfusion. However, we found no evidence that the association between Higashida score and reperfusion relied on recanalization status (p = 0.2). NIHSS on admission (p < 0.0001) and recanalization (p = 0.001) were independently associated with long-term outcome at 3 months, however, Higashida score (p = 0.228) was not. Conclusion: Higashida score and recanalization were independently associated with reperfusion, but the association between recanalization and reperfusion was similar regardless of collateral flow quality. Recanalization was associated with long-term outcome. DSC-based measures of collateral flow were not associated with long-term outcome, possibly due to the complex dynamic nature of collateral recruitment, timing of imaging and the employed post-processing

Automated acute ischemic stroke lesion delineation based on apparent diffusion coefficient thresholds

Purpose: Automated lesion segmentation is increasingly used in acute ischemic stroke magnetic resonance imaging (MRI). We explored in detail the performance of apparent diffusion coefficient (ADC) thresholding for delineating baseline diffusion-weighted imaging (DWI) lesions. Methods: Retrospective, exploratory analysis of the prospective observational single-center 1000Plus study from September 2008 to June 2013 (clinicaltrials.org; NCT00715533). We built a fully automated lesion segmentation algorithm using a fixed ADC threshold (≤620 × 10–6 mm2/s) to delineate the baseline DWI lesion and analyzed its performance compared to manual assessments. Diagnostic capabilities of best possible ADC thresholds were investigated using receiver operating characteristic curves. Influential patient factors on ADC thresholding techniques’ performance were studied by conducting multiple linear regression. Results: 108 acute ischemic stroke patients were selected for analysis. The median Dice coefficient for the algorithm was 0.43 (IQR 0.20–0.64). Mean ADC values in the DWI lesion (β = −0.68, p < 0.001) and DWI lesion volumes (β = 0.29, p < 0.001) predicted performance. Optimal individual ADC thresholds differed between subjects with a median of ≤691 × 10−6 mm2/s (IQR ≤660–750 × 10−6 mm2/s). Mean ADC values in the DWI lesion (β = −0.96, p < 0.001) and mean ADC values in the brain parenchyma (β = 0.24, p < 0.001) were associated with the performance of individual thresholds. Conclusion: The performance of ADC thresholds for delineating acute stroke lesions varies substantially between patients. It is influenced by factors such as lesion size as well as lesion and parenchymal ADC values. Considering the inherent noisiness of ADC maps, ADC threshold-based automated delineation of very small lesions is not reliable

Multimodal Fusion Strategies for Outcome Prediction in Stroke

Data driven methods are increasingly being adopted in the medical domain for clinical predictive modeling. Prediction of stroke outcome using machine learning could provide a decision support system for physicians to assist them in patient-oriented diagnosis and treatment. While patient-specific clinical parameters play an important role in outcome prediction, a multimodal fusion approach that integrates neuroimaging with clinical data has the potential to improve accuracy. This paper addresses two research questions: (a) does multimodal fusion aid in the prediction of stroke outcome, and (b) what fusion strategy is more suitable for the task at hand. The baselines for our experimental work are two unimodal neural architectures: a 3D Convolutional Neural Network for processing neuroimaging data, and a Multilayer Perceptron for processing clinical data. Using these unimodal architectures as building blocks we propose two feature-level multimodal fusion strategies: 1) extracted features , where the unimodal architectures are trained separately and then fused, and 2) end-to-end, where the unimodal architectures are trained together. We show that integration of neuroimaging information with clinical metadata can potentially improve stroke outcome prediction. Additionally, experimental results indicate that the end-to-end fusion approach proves to be more robust

Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome

State-of-the-art machine learning (ML) artificial intelligence methods are increasingly leveraged in clinical predictive modeling to provide clinical decision support systems to physicians. Modern ML approaches such as artificial neural networks (ANNs) and tree boosting often perform better than more traditional methods like logistic regression. On the other hand, these modern methods yield a limited understanding of the resulting predictions. However, in the medical domain, understanding of applied models is essential, in particular, when informing clinical decision support. Thus, in recent years, interpretability methods for modern ML methods have emerged to potentially allow explainable predictions paired with high performance. To our knowledge, we present in this work the first explainability comparison of two modern ML methods, tree boosting and multilayer perceptrons (MLPs), to traditional logistic regression methods using a stroke outcome prediction paradigm. Here, we used clinical features to predict a dichotomized 90 days post-stroke modified Rankin Scale (mRS) score. For interpretability, we evaluated clinical features' importance with regard to predictions using deep Taylor decomposition for MLP, Shapley values for tree boosting and model coefficients for logistic regression. With regard to performance as measured by Area under the Curve (AUC) values on the test dataset, all models performed comparably: Logistic regression AUCs were 0.83, 0.83, 0.81 for three different regularization schemes; tree boosting AUC was 0.81; MLP AUC was 0.83. Importantly, the interpretability analysis demonstrated consistent results across models by rating age and stroke severity consecutively amongst the most important predictive features. For less important features, some differences were observed between the methods. Our analysis suggests that modern machine learning methods can provide explainability which is compatible with domain knowledge interpretation and traditional method rankings. Future work should focus on replication of these findings in other datasets and further testing of different explainability methods

The Effect of Scan Length on the Assessment of BOLD Delay in Ischemic Stroke

Objectives: To evaluate the impact of resting-state functional MRI scan length on the diagnostic accuracy, image quality and lesion volume estimation of BOLD delay maps used for brain perfusion assessment in acute ischemic stroke. Methods: Sixty-three acute ischemic stroke patients received a 340 s resting-state functional MRI within 24 h of stroke symptom onset. BOLD delay maps were calculated from the full scan and four shortened versions (68 s, 136 s, 204 s, 272 s). The BOLD delay lesions on these maps were compared in terms of spatial overlap and volumetric agreement with the lesions derived from the full scans and with time-to-maximum (Tmax) lesions derived from DSC-MRI in a subset of patients (n = 10). In addition, the interpretability and quality of these maps were compared across different scan lengths using mixed models. Results: Shortened BOLD delay scans showed a small volumetric bias (ranging from 0.05 to 5.3mL; between a 0.13%volumetric underestimation and a 7.7%overestimation relative to the mean of the volumes, depending on scan length) compared to the full scan. Decreased scan length was associated with decreased spatial overlap with both the BOLD delay lesions derived from the full scans and with Tmax lesions. Only the two shortest scan lengths (68 and 136 s) were associated with substantially decreased interpretability, decreased structure clarity, and increased noisiness of BOLD delay maps. Conclusions: BOLD delay maps derived from resting-state fMRI scans lasting 272 and 204 s provide sufficient diagnostic quality and adequate assessment of perfusion lesion volumes. Such shortened scans may be helpful in situations where quick clinical decisions need to be made