Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi

Value of thrombus CT Characteristics in Patients with Acute Ischemic Stroke

BACKGROUND AND PURPOSE: Thrombus CT characteristics might be useful for patient selection for intra-arterial treatment. Our objective was to study the association of thrombus CT characteristics with outcome and treatment effect in patients with acute ischemic stroke. MATERIALS AND METHODS: We included 199 patients for whom thin-section NCCT and CTA within 30 minutes from each other were available in the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute ischemic stroke in the Netherlands (MR CLEAN) study. We assessed the following thrombus characteristics: location, distance from ICA terminus to thrombus, length, volume, absolute and relative density

Current and potentially novel antithrombotic treatment in acute ischemic stroke

Acute ischemic stroke (AIS) is the most common type of stroke and requires immediate reperfusion. Current acute reperfusion therapies comprise the administration of intravenous thrombolysis and/or endovascular thrombectomy. Although these acute reperfusion therapies are increasingly successful, optimized secondary antithrombotic treatment remains warranted, specifically to reduce the risk of major bleeding complications. In the development of AIS, coagulation and platelet activation play crucial roles by driving occlusive clot formation. Recent studies implicated that the intrinsic route of coagulation plays a more prominent role in this development, however, this is not fully understood yet. Next to the acute treatments, antithrombotic therapy, consisting of anticoagulants and/or antiplatelet therapy, is successfully used for primary and secondary prevention of AIS but at the cost of increased bleeding complications. Therefore, better understanding the interplay between the different pathways involved in the pathophysiology of AIS might provide new insights that could lead to novel treatment strategies. This narrative review focuses on the processes of platelet activation and coagulation in AIS, and the most common antithrombotic agents in primary and secondary prevention of AIS. Furthermore, we provide an overview of promising novel antithrombotic agents that could be used to improve in both acute treatment and stroke prevention

Follow-up infarct volume as a mediator of endovascular treatment effect on functional outcome in ischaemic stroke

Objective: The putative mechanism for the favourable effect of endovascular treatment (EVT) on functional outcome after acute ischaemic stroke is preventing follow-up infarct volume (FIV) progression. We aimed to assess to what extent difference in FIV explains the effect of EVT on functional outcome in a randomised trial of EVT versus no EVT (MR CLEAN). Methods: FIV was assessed on non-contrast CT scan 5–7 days after stroke. Functional outcome was the score on the modified Rankin Scale at 3 months. We tested the causal pathway from intervention, via FIV to functional outcome with a mediation model, using linear and ordinal regression, adjusted for relevant baseline covariates, including stroke severity. Explained effect was assessed by taking the ratio of the log odds ratios of treatment with and without adjustment for FIV. Results: Of the 500 patients included in MR CLEAN, 60 died and four patients underwent hemicraniectomy before FIV was assessed, leaving 436 patients for analysis. Patients in the intervention group had better functional outcomes (adjusted common odds ratio (acOR) 2.30 (95% CI 1.62–3.26) than controls and smaller FIV (median 53 vs. 81 ml) (difference 28 ml; 95% CI 13–41). Smaller FIV was associated with better outcome (acOR per 10 ml 0.60, 95% CI 0.52–0.68). After adjustment for FIV the effect of intervention on functional outcome decreased but remained substantial (acOR 2.05, 95% CI 1.44–2.91). This implies that preventing FIV progression explains 14% (95% CI 0–34) of the beneficial effect of EVT on outcome. Conclusion: The effect of EVT on FIV explains only part of the treatment effect on functional outcome. Key Points: • Endovascular treatment in acute ischaemic stroke patients prevents progression of follow-up infarct volume on non-contrast CT at 5–7 days.• Follow-up infarct volume was related to functional outcome, but only explained a modest part of the effect of intervention on functional outcome.• A large proportion of treatment effect on functional outcome remains unexplained, suggesting FIV alone cannot be used as an early surrogate imaging marker of functional outcome

Automated image registration of cerebral digital subtraction angiography

Purpose: Our aim is to automatically align digital subtraction angiography (DSA) series, recorded before and after endovascular thrombectomy. Such alignment may enable quantification of procedural success. Methods: Firstly, we examine the inherent limitations for image registration, caused by the projective characteristics of DSA imaging, in a representative set of image pairs from thrombectomy procedures. Secondly, we develop and assess various image registration methods (SIFT, ORB). We assess these methods using manually annotated point correspondences for thrombectomy image pairs. Results: Linear transformations that account for scale differences are effective in aligning DSA sequences. Two anatomical landmarks can be reliably identified for registration using a U-net. Point-based registration using SIFT and ORB proves to be most effective for DSA registration and are applicable to recordings for all patient sub-types. Image-based techniques are less effective and did not refine the results of the best point-based registration method. Conclusion: We developed and assessed an automated image registration approach for cerebral DSA sequences, recorded before and after endovascular thrombectomy. Accurate results were obtained for approximately 85% of our image pairs.</p

Functional Outcome Prediction in Acute Ischemic Stroke

BACKGROUND Stroke is one of the most prevalent neurological diseases and causes of disability worldwide. Functional outcome prediction models can assist the treatment decision process and optimize acute ischemic stroke health care. Current models often use a limited set of input features to predict functional outcome, although combining various types of features could improve model performance. Furthermore, they often incorporate follow-up information, while prediction models applicable in the acute setting are desirable. METHODS We trained an ensemble model consisting of five machine learning models with leave-one-out cross-validation to predict the binarized modified Rankin Scale score three months after stroke onset in patients with acute ischemic stroke caused by a large vessel occlusion who received endovascular treatment. We used clinical variables, treatment variables and lesion loads derived from registration of a stroke population-specific neuroanatomical CT brain atlas with the follow-up non-contrast enhanced CT scan as input features. RESULTS Taking into account five performance metrics (accuracy, AUC, sensitivity, specificity and F1-score), the ensemble model and support vector machine (SVM) seemed to achieve the best performances out of the six models (ensemble model and the five individual machine learning models), with AUC values up to 0.76 and 0.77 respectively. The highest accuracy obtained with the ensemble model was 0.69, and with the SVM 0.72. Little variance in performance was found between the various sets of input features. CONCLUSION Although similar performances compared to current literature were obtained, conventional machine learning models might not be sophisticated enough to capture the complex interactions between input features for functional outcome prediction in acute ischemic stroke

Endovascular treatment in comatose patients with anterior circulation ischemic stroke

BACKGROUND: Coma in the first hours after anterior circulation ischemic stroke is rare. We aimed to assess the causes of coma and outcomes after endovascular thrombectomy (EVT) in this relatively unexplored subgroup of patients.MATERIALS AND METHODS: We used data from the MR CLEAN Registry, a prospective, multicenter, observational cohort study of patients treated with EVT in the Netherlands between March 2014, and December 2018. We included patients with anterior circulation ischemic stroke treated within 6.5 h of symptom onset and assessed frequency and causes of coma, defined as a score of 8 or lower on the Glasgow Coma Scale. Patients with a posterior circulation stroke were excluded. The primary outcome was the score on the modified Rankin Scale at 90 days. We compared outcomes of comatose and non-comatose patients with logistic regression.RESULTS: Fifty-two (1%) of 4,869 patients were comatose. The main causes of coma were bilateral ischemia, a post-ictal state after an epileptic seizure, and respiratory insufficiency. Comatose patients were less likely to receive intravenous thrombolysis (54% vs. 73%; p = 0.004) and onset-to-groin times were longer (226 vs. 199 min; p = 0.012). Patients with coma had poorer functional outcomes (adjusted common odds ratio (OR), 2.73; 95%CI: 1.45-5.13) and more frequently died within 90 days (adjusted OR, 2.95; 95%CI: 1.47-5.90). CONCLUSION: Bilateral ischemia, a post-ictal state after an epileptic seizure and respiratory insufficiency are common causes of coma in patients with anterior circulation ischemic stroke treated with EVT. These patients have a high risk of death or dependency at 90 days.</p