No abstract

BACKGROUND AND PURPOSE:Incomplete occlusion and recanalization of large and wide-neck brain aneurysms treated by endovascular therapy remains a challenge. We present preliminary clinical and angiographic results of an experimentally optimized Surpass flow diverter for treatment of intracranial aneurysms in a prospective, multicenter, nonrandomized, single-arm study.

Background and Purpose— The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods— The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0–2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results— The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions— Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

Background and Purpose— For patients with large vessel occlusion, neuroimaging biomarkers that evaluate the changes in brain tissue are important for determining the indications for mechanical thrombectomy. In this study, we applied deep learning to derive imaging features from pretreatment diffusion-weighted image data and evaluated the ability of these features in predicting clinical outcomes for patients with large vessel occlusion. Methods— This multicenter retrospective study included patients with anterior circulation large vessel occlusion treated with mechanical thrombectomy between 2013 and 2018. We designed a 2-output deep learning model based on convolutional neural networks (the convolutional neural network model). This model employed encoder-decoder architecture for the ischemic lesion segmentation, which automatically extracted high-level feature maps in its middle layers, and used its information to predict the clinical outcome. Its performance was internally validated with 5-fold cross-validation, externally validated, and the results compared with those from the standard neuroimaging biomarkers Alberta Stroke Program Early CT Score and ischemic core volume. The prediction target was a good clinical outcome, defined as a modified Rankin Scale score at 90-day follow-up of 0 to 2. Results— The derivation cohort included 250 patients, and the validation cohort included 74 patients. The convolutional neural network model showed the highest area under the receiver operating characteristic curve: 0.81±0.06 compared with 0.63±0.05 and 0.64±0.05 for the Alberta Stroke Program Early CT Score and ischemic core volume models, respectively. In the external validation, the area under the curve for the convolutional neural network model was significantly superior to those for the other 2 models. Conclusions— Compared with the standard neuroimaging biomarkers, our deep learning model derived a greater amount of prognostic information from pretreatment neuroimaging data. Although a confirmatory prospective evaluation is needed, the high-level imaging features derived by deep learning may offer an effective prognostic imaging biomarker.

Administration of ex vivo-expanded bone marrow-derived EPCs reduced infarct volume and neurological deficits in acute focal brain ischemia-reperfusion injury caused, at least in part, by attenuation of endothelial dysfunction.

Our results indicate that the MCA specimens from MMD patients had thicker intimal walls than the specimens from control patients. In addition, hypoxia-inducing factor-1alpha and endoglin were overexpressed in the intima of the MCA of MMD patients.

BACKGROUND AND PURPOSE: Paraclinoid aneurysms have been increasingly treated endovascularly. The natural history of these aneurysms has gradually been elucidated. The purpose of this study was to assess the safety and efficacy of endovascular treatment for these aneurysms.

Background and Purpose— National registration studies (the Japanese Registry of Neuroendovascular Therapy [JR-NET] and JR-NET2) have determined the current status and outcomes of neuroendovascular therapy (neuro-EVT). We analyzed short-term outcomes of EVT for asymptomatic unruptured intracranial aneurysms (UIAs). Methods— We extracted periprocedural information about EVT for 4767 asymptomatic UIAs from 31 968 registered procedural records of all EVT in the JR-NET and JR-NET2 databases. We assessed the features of the aneurysms and procedures, immediate radiographic findings, procedure-related complications, and clinical outcomes at 30 days after the procedures. Results— We located 80.0% of UIAs in the anterior circulation, and the most frequent were paraclinoid. The diameter of 2.5%, 32.9%, 51.9%, 12.0%, and 0.7% of the UIAs was <3, 3 to 4, 5 to 9, 10 to 19, and >20 mm, respectively. EVT failed in only 2.1%. Adjunctive techniques were applied in 54.8% of procedures. Pre- and postprocedural antiplatelet agents were prescribed in 85.6% and 84.0%, respectively, of the procedures. The immediate radiographic outcomes of 57.7%, 31.9%, and 10.0% of the UIAs comprised complete occlusion, residual necks, and residual aneurysms, respectively. Complications that were associated with 9.1% of procedures comprised 2.0% hemorrhagic and 4.6% ischemic, and the 30-day morbidity and mortality rates were 2.12% and 0.31%, respectively. Conclusions— The radiographic results of EVT for asymptomatic UIAs in Japan were acceptable, with low mortality and morbidity rates.