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Purpose: This study aimed to evaluate the effectiveness and safety of the NeVaTM stent retriever as first- and second-line device for mechanical thrombectomy in acute ischemic stroke. Methods: In this retrospective single-center study, all consecutive patients that underwent mechanical thrombectomy with NeVaTM stent retriever as first- or second-line device due to intracranial vessel occlusion with acute ischemic stroke between March and November 2022 were included. Results: Thirty-nine patients (m=18, f=21) with a mean age of 69.9 ± 13.3 years were treated with the NeVaTM stent retriever. NeVaTM stent retriever was used as first-line device in 24 (61.5%) of patients and in 15 (38.5%) as second-line device. First-pass rate (≥mTICI 2c) of NeVaTM stent retriever was both 66.7% when used as first- or second-line device. Final recanalization rate including rescue strategies was 92.3% for ≥mTICI2c and 94.9% for ≥mTICI2b. No device-related minor or major adverse events were observed. A hemorrhage was detected in 33.3% of patients at 24h post-thrombectomy dual-energy CT, of which none was classified as symptomatic intracerebral hemorrhage. NIHSS and mRS improved significantly at discharge compared to admission (p<0.05). Conclusion: The NeVaTM stent retriever has a high effectivity and good safety profile as first- and second-line device for mechanical thrombectomy in acute ischemic stroke. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms. [ABSTRACT FROM AUTHOR]

Our aim is to predict possible gross total and subtotal resections of skull meningiomas from pre-treatment T1 post contrast MR-images using radiomics and machine learning in a representative patient cohort. We analyse the accuracy of our model predictions depending on the tumor location within the skull and the postoperative tumor volume. In this retrospective, IRB-approved study, image segmentation of the contrast enhancing parts of the tumor was semi-automatically performed using the 3D Slicer open-source software platform. Imaging data were split into training data and independent test data at random. We extracted a total of 107 radiomic features by hand-delineated regions of interest on T1 post contrast MR images. Feature preselection and model construction were performed with eight different machine learning algorithms. Each model was estimated 100 times on new training data and then tested on a previously unknown, independent test data set to avoid possible overfitting. Our cohort included 138 patients. A gross total resection of the meningioma was performed in 107 cases and a subtotal resection in the remaining 31 cases. Using the training data, the mean area under the curve (AUC), mean accuracy, mean kappa, mean sensitivity and mean specificity were 0.901, 0.875, 0.629, 0.675 and 0.933 respectively. We obtained very similar results with the independent test data: mean AUC = 0.900, mean accuracy = 0.881, mean kappa = 0.644, mean sensitivity = 0.692 and mean specificity = 0.936. Thus, our model exposes good and stable predictive performance with both training and test data. Our radiomics approach shows that with machine learning algorithms and comparatively few explanatory factors such as the location of the tumor within the skull as well as its shape, it is possible to make accurate predictions about whether a meningioma can be completely resected by surgery. Complete resections and resections with larger postoperative tumor volumes can be predicted with very high accuracy. However, cases with very small postoperative tumor volumes are comparatively difficult to predict correctly. [ABSTRACT FROM AUTHOR]

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology. [ABSTRACT FROM AUTHOR]

Background Thrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition. Methods The analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation. Results Test set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity. Conclusions Machine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) imaging in acute stroke is an established and fairly widespread approach, but there is no data on applicability of intraosseous (IO) contrast administration in the case of failed intravenous (IV) cannula placement. Here, we present the first case of IO contrast administration for CT imaging in suspected acute stroke providing a dedicated CT examination protocol and analysis of achieved image quality as well as a review of available literature. [ABSTRACT FROM AUTHOR]

Background Different imaging characteristics such as clot burden score, collaterals, and pre- interventional thrombus migration are associated with functional outcome in patients with acute ischemic stroke. Moreover, histological thrombus composition is associated with pre- interventional thrombus migration. We hypothesized that smaller clots may more likely migrate and that collateral status in ischemic stroke patients may mediate this tendency of the clot to migrate. Methods In this prospective cohort of consecutive ischemic stroke patients, clot burden scores and collateral scores were rated and the retrieved thrombi were histologically analyzed. We then investigated the relationship between clot burden score, probability for thrombus migration, and collateral scores using mediation analysis. Results 163 patients are included of which 36 (22.1%) had a clot migration. Probability of thrombus migration was significantly associated with lower collateral scores (P<0.01), higher clot burden scores (P<0.01), shorter thrombi (P<0.01), and higher RBC count (P<0.01). In the mediator pathway, higher collateral scores were significantly associated with higher clot burden scores (P<0.01) and younger age (P=0.029). The total effect of an increase in clot burden score by one grade on thrombus migration is composed of the direct effect (+18%, P<0.01) and the collateral score- mediated indirect effect (--5%, P<0.01). Conclusions Smaller, erythrocyte- rich thrombi tend to migrate more often. Good collaterals seem to have a considerable effect on limiting migration. This supports the hypothesis that larger clots have stronger adherence with the vessel wall and that good collaterals increase the counter pressure distal of the clot. [ABSTRACT FROM AUTHOR]

The glycocalyx consists of proteoglycans, glycoproteins, glycosaminoglycans, associated plasma proteins, and soluble glycosaminoglycans and covers the surface of all eukaryotic cells. It mediates specific recognition events, modulates biological processes such as ligand–receptor interactions, and has been proposed to affect tumor metastasis. Here, we studied whether the glycocalyx is required for melanoma cell migration. We diminished the glycocalyx of human melanoma cells by inhibiting posttranslational N-glycosylation or by enzymatic digestion of the N-glycosides. This partial destruction of the glycocalyx reduced melanoma cell migration by up to 60%. It was accompanied by the disintegration of a characteristic pH nanoenvironment typically surrounding migrating cells. Restoring this pH profile by stimulating the activity of the Na+/H+ exchanger NHE1 rescued cell migration even in the absence of an intact glycocalyx. The effects of partially removing the glycocalyx compared to those of knocking down β1-integrin expression points to a close functional correlation between glycocalyx, integrins, and cell surface pH nanoenvironment. We conclude that the glycocalyx is required for tumor cell migration. It stabilizes the cell surface pH nanoenvironment allowing a concerted pH-dependent interaction of adhesion receptors and extracellular matrix. [ABSTRACT FROM AUTHOR]