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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]

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]

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]

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]

Abstract: Many physiological and pathophysiological processes, such as embryogenesis, immune defense, wound healing, or metastasis, are based on cell migration and invasion. The activity of the ubiquitously expressed NHE1 isoform of the plasma membrane Na+/H+ exchanger is one of the requirements for directed locomotion of migrating cells. The mechanisms by which NHE1 is involved in cell migration are multiple. NHE1 contributes to cell migration by affecting the cell volume, by regulating the intracellular pH and thereby the assembly and activity of cytoskeletal elements, by anchoring the cytoskeleton to the plasma membrane, by the organization of signal transduction and by regulating gene expression. The present review focuses on two additional, extracellular mechanisms by which NHE1 activity contributes to cell migration and invasion. Protons extruded by the NHE1 lead to local, extracellular acidification which, on the one hand, can create pH optima needed for the activity of proteinases at invadopodia/podosomes necessary for extracellular matrix digestion and, on the other hand, facilitates cell/matrix interaction and adhesion at the cell front. [Copyright &y& Elsevier]