Glioma grading based on histogram analysis of normalized CBV heterogeneity is an alternative to the established hot-spot method, as it offers increased diagnostic accuracy and interobserver agreement.

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In patients with acromegaly, T2 signal intensity at diagnosis correlates with histological features and predicts biochemical outcome of first-line SA treatment.

Background Among asymptomatic patients with severe carotid artery stenosis but no recent stroke or transient cerebral ischaemia, either carotid artery stenting (CAS) or carotid endarterectomy (CEA) can restore patency and reduce long-term stroke risks. However, from recent national registry data, each option causes about 1% procedural risk of disabling stroke or death. Comparison of their long-term protective effects requires large-scale randomised evidence.Methods ACST-2 is an international multicentre randomised trial of CAS versus CEA among asymptomatic patients with severe stenosis thought to require intervention, interpreted with all other relevant trials. Patients were eligible if they had severe unilateral or bilateral carotid artery stenosis and both doctor and patient agreed that a carotid procedure should be undertaken, but they were substantially uncertain which one to choose. Patients were randomly allocated to CAS or CEA and followed up at 1 month and then annually, for a mean 5 years. Procedural events were those within 30 days of the intervention. Intention-to-treat analyses are provided. Analyses including procedural hazards use tabular methods. Analyses and meta-analyses of non-procedural strokes use Kaplan-Meier and log-rank methods. The trial is registered with the ISRCTN registry, ISRCTN21144362.

Flow diverter stents are new important tools in the treatment of large, giant, or wide-necked aneurysms. Their delivery and positioning may be difficult due to vessel tortuosity. Common adverse events include intracranial hemorrhage and ischemic stroke, which usually occurs within the same day, or the next few days after the procedure. We present a case where we encountered an unusual intracerebral complication several months after endovascular treatment of a large left internal carotid artery aneurysm, and where brain biopsy revealed foreign body reaction to hydrophilic polymer fragments distally to the stent site. Although previously described, embolization of polymer material from intravascular equipment is rare. We could not identify any other biopsy verified case in the literature, with this particular presentation of intracerebral polymer embolization – a multifocal inflammation spread out through the white matter of one hemisphere without hemorrhage or ischemic changes.

BACKGROUND AND PURPOSE:Inclusion of oligodendroglial tumors may confound the utility of MR based glioma grading. Our aim was, first, to assess retrospectively whether a histogram-analysis method of MR perfusion images may both grade gliomas and differentiate between low-grade oligodendroglial tumors with or without loss of heterozygosity (LOH) on 1p/19q and, second, to assess retrospectively whether low-grade oligodendroglial subtypes can be identified in a population of patients with high-grade and low-grade astrocytic and oligodendroglial tumors.

Purpose:To assess whether glioma volumes from knowledge-based fuzzy c-means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging. Materials and Methods:Fifty patients with newly diagnosed gliomas were imaged using DSC MR imaging at 1.5 Tesla. To compare our results with manual tumor definitions, glioma volumes were also defined independently by four neuroradiologists. Using a histogram analysis method, diagnostic efficacy values for glioma grade and expected patient survival were assessed. Results:The areas under the receiver operator characteristics curves were similar when using manual and automated tumor volumes to grade gliomas (P ϭ 0.576 -0.970). When identifying a high-risk patient group (expected survival Ͻ2 years) and a low-risk patient group (expected survival Ͼ2 years), a higher log-rank value from Kaplan-Meier survival analysis was observed when using automatic tumor volumes (14.403; P Ͻ 0.001) compared with the manual volumes (10.650 -12.761; P ϭ 0.001-0.002). Conclusion:Our results suggest that knowledge-based FCM clustering of multiple MR image classes provides a completely automatic, user-independent approach to selecting the target region for presurgical glioma characterization

The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived from MR perfusion images and to assess the diagnostic accuracy of the model and the sensitivity to sample size. A total of 86 patients with histologically-confirmed gliomas were imaged using dynamic susceptibility contrast (DSC) MRI at 1.5T. Histogram signatures from 53 of the 86 patients were analyzed independently by four neuroradiologists and used as a basis for the predictive SVM model. The resulting SVM model was tested on the remaining 33 patients and analyzed by a fifth neuroradiologist. At optimal SVM parameters, the true positive rate (TPR) and true negative rate ( Several studies have shown that cerebral blood volume (CBV) maps derived from dynamic susceptibility contrast (DSC) MRI can improve differentiation between highgrade (grades III-IV) and low-grade (grades I-II) gliomas, using the World Health Organization (WHO) classification system (1-4). Based on normalized CBV (nCBV) maps, viable malignant tumor tissue can be identified as regions of elevated microvascular blood volume (maximum nCBV; "hotspot method") compared to unaffected tissue (5). However, applying these grading methods prospectively requires some prior knowledge about the appropriate threshold values that provide optimal differentiation between high-and low-grade gliomas. In studies using the hotspot method on gradient-echo perfusion images, the reported maximum nCBV threshold for optimal differentiation show large variations (6 -8). This suggests that the optimal nCBV threshold depends on several method-specific parameters, including contrast agent properties and dose, imaging technique, and postprocessing routines. This method-dependency on the critical nCBV threshold means that the threshold value must be determined specifically at each site, which complicates the comparison of data between sites and also places restrictions on modification of any of the model-sensitive parameters in a given institution.Although common in the literature on tumor growth and invasion (9 -11), application of predictive modeling to MR perfusion images with the aim of predicting glioma grade is, to our knowledge, not reported in the literature. One possible reason for this is that current nCBV threshold values are difficult to generalize into a useful model. A predictive model based on one single value per subject will inherently lack sufficient robustness. An alternative histogram-based method for analysis of nCBV maps has recently been proposed, which provides a measure of the distribution of nCBV values in the entire volume affected by the tumor; it has been shown that this approach may improve differentiation between low-and high-grade gliomas compared to the hotspot method (8,12). Histogram analysis also provides a more attractive starting point for predictive modelin...