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.

The recent development of revascularization devices, including stent retrievers, has enabled increasingly higher revascularization rates for arterial occlusions in acute ischemic stroke. Patient-specific factors such as anatomy, however, may occasionally limit endovascular deployment of these new devices via the conventional transfemoral approach. We report three cases of acute ischemic stroke where a transbrachial endovascular approach to revascularization was used, resulting in successful recanalization. These examples suggest that a transbrachial approach may be considered as an alternative in the endovascular treatment of acute ischemic stroke.

We present a case of a 53-year-old HIV negative man with a 2-month history of progressive recent memory disturbance, gait disturbance and urinary incontinence. On MRI, an infiltrative tumor in the brain and spinal cord was noted. Subsequent positron emission tomography studies along with bone marrow biopsy and serum protein electrophoresis showed no evidence of systemic disease. Open brain biopsy results revealed a small lymphocytic infiltrate with scattered plasma cells in a predominantly perivascular growth pattern. The morphology was consistent with involvement by a low-grade B-cell lymphoma. Immunohistochemical findings showed CD20+, CD10-, CD5-, TdT-, EBV-encoded RNA in situ- and IgM-. The above findings were consistent with involvement by a non-dural extranodal marginal zone B-cell lymphoma (MZBCL) primary to the brain and spinal cord. This is a case report of a CNS MZBCL of mucosa-associated lymphoid tissue type involving the brain and spinal cord parenchyma.

BackgroundHerein, we report an in vivo study of a biodegradable flow diverter (BDFD) for aneurysm occlusion. Conceptually, BDFDs induce a temporal flow‐diverting effect and provide a vascular scaffold for neointimal formation at the neck of the aneurysm until occlusion. This offers several potential advantages, including a reduced risk of remote ischemic complications and more treatment options in case of device failure to occlude the aneurysm.Methods and ResultsA BDFD consisting of 48 poly‐l‐lactic acid wires with radiopaque markers at both ends was prepared. An in vitro degradation test of the BDFD was performed. Thirty‐six BDFDs were implanted in a rabbit aneurysm model. Digital angiography, optical coherence tomography, histopathology, and scanning electron microscopy were performed after 1, 3, and 6 months, and 1 year. The in vitro degradation test showed that the BDFD was almost degraded in 1.5 years. In the in vivo experiment, aneurysm occlusion rates were 0% at 1 month, 20% at 3 months, 50% at 6 months, and 33% at 1 year. Optical coherence tomography showed that luminal area stenosis was the highest at 3 months (16%) and decreased afterward. Immunohistochemical analysis showed that more than half of the luminal surface area was covered by endothelial cells at 1 month. Device fragmentation was not observed in any lesions.ConclusionsThis first in vivo study of a BDFD shows the feasibility of using BDFDs for treating aneurysms; however, a longer follow‐up is required for comprehensive evaluation of the biological and mechanical behavior peculiar to biodegradable devices.