Safety and Efficacy of Surgical and Endovascular Treatment for Distal Anterior Cerebral Artery Aneurysms: A Systematic Review and Meta-Analysis.

BACKGROUND: Aneurysms of the distal anterior cerebral artery (DACA) are rare, representing between 1% and 9% of all intracranial aneurysms. The best treatment strategy for these aneurysms continues to be debated. OBJECTIVE: We conducted a systematic review of the literature to evaluate the safety and efficacy of treatment strategies of DACA aneurysms. METHODS: A systematic search of Medline, Embase, Scopus, and Web of Science was performed for studies published from January 2000 to August 2015. We included studies describing treatment of DACA aneurysms with ≥10 patients. Random effects meta-analysis was used to pool the following outcomes: complete occlusion, technical success, periprocedural morbidity/mortality and stroke rates, aneurysm recurrence/rebleed, and long-term neurologic morbidity/mortality. RESULTS: Thirty studies with 1329 DACA aneurysms were included. Complete occlusion was 95% (95% confidence interval [CI], 91.0%-97.0%) in the surgical group and 68% (95% CI, 56.0%-78.0%) in the endovascular group (P < 0.0001). Aneurysm recurrence occurred in 3% (95% CI, 2.0%-4.0%) after surgery and in 19.1% (95% CI, 12.0%-27.0%) after endovascular treatment (P < 0.0001). Overall neurologic morbidity and mortality were 15% (95% CI, 11.0%-21.0%) and 9% (95% CI, 7.0%-11.0%) after surgery and 14% (95% CI, 10.0%-19.0%) (P = 0.725) and 7% (95% CI, 5.0%-10.0%) (P = 0.422) after endovascular treatment, respectively. Overall long-term favorable neurologic outcome was 80% and it was equal in both groups (80%; 95% CI, 73.0%-85.0% in the surgical group and 80%; 95% CI, 72.0%-87.0% in the endovascular group) (P = 0.892). CONCLUSIONS: Our meta-analysis showed that both treatment modalities are technically feasible and effective with sufficient long-term aneurysm occlusion and acceptable recurrence/rebleed rates. Surgical treatment is associated with superior angiographic outcomes. There were no substantial differences in procedure-related morbidity and mortality. These findings are important because they suggest that therapy of DACA aneurysms should be performed on a selective, case-by-case basis to maximize patient benefits.

Detection of carotid artery stenosis using histological specimens: a comparison of CT angiography, magnetic resonance angiography, digital subtraction angiography and Doppler ultrasonography.

BACKGROUND: Carotid endarterectomy (CEA) is accepted as a primary modality to treat carotid stenosis. The accuracy of measuring carotid stenosis is important for indication of the CEA procedure. Different diagnostic tools have been developed and used in the past 2 decades for the diagnosis of carotid stenosis. Only a few studies, however, have focused on the comparison of different diagnostic tools to histological findings of carotid plaque. METHOD: Patients with internal carotid artery (ICA) stenosis were investigated primarily by computed tomography angiography (CTA). Digital subtraction angiography (DSA), Doppler ultrasonography (DUS) and magnetic resonance angiography (MRA) were performed as well. Atherosclerotic plaque specimens were transversally cut into smaller segments and histologically processed. The slides were scanned and specimens showing maximal stenosis were determined; the minimal diameter and the diameter of the whole plaque were measured. High quality histological specimen and histological measurement was considered to be the prerequisite for inclusion into the analysis. The preoperative findings were compared with histological measurement. CTA and histological measurements were obtained from 152 patients. DSA measurements were available in 138 of these cases, MRA in 107 and DUS in 88. A comparison between preoperative and histological findings was performed. In addition, correlation coefficients were computed and tested. RESULTS: A significant correlation was found for each of the diagnostic procedures. The strongest correlation coefficient and the best allocation of stenosis into clinical significant groups (<50 %, 50-69 %, ≥70 %) was observed for CTA. Mean differences in the whole cohort between preoperative and histological measurements were as follows: CTA underestimated histological measurement by 2.4 % (based on European Carotid Surgery Trial [ECST] methodology) and 11.9 % (based on North American Symptomatic Carotid Endarterectomy Trial [NASCET] methodology). DSA underestimated the histological measurement by 7 % (ECST) and 12.2 % (NASCET). MRA overestimated the histological measurement by 2.6 % (ECST) and underestimated by 0.6 % (NASCET). DUS overestimated the stenosis by 1.8 %. CONCLUSIONS: CTA yields the best accuracy in detection of carotid stenosis, provided that all axial slices of the stenosis are checked and carefully analysed. DSA underestimates moderate and mild ICA stenosis, whereas DUS overestimates high-grade ICA stenosis. For MRA, a relatively low correlation coefficient was observed with histological findings. We conclude that CTA-ecst technique is the most reliable technique for carotid stenosis measurement.

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas.

PURPOSE: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O(6)-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. METHODS: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. RESULTS: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. CONCLUSIONS: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.

Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging.

BACKGROUND: Segmentation of pre-operative low-grade gliomas (LGGs) from magnetic resonance imaging is a crucial step for studying imaging biomarkers. However, segmentation of LGGs is particularly challenging because they rarely enhance after gadolinium administration. Like other gliomas, they have irregular tumor shape, heterogeneous composition, ill-defined tumor boundaries, and limited number of image types. To overcome these challenges we propose a semi-automated segmentation method that relies only on T2-weighted (T2W) and optionally post-contrast T1-weighted (T1W) images. METHODS: First, the user draws a region-of-interest (ROI) that completely encloses the tumor and some normal tissue. Second, a normal brain atlas and post-contrast T1W images are registered to T2W images. Third, the posterior probability of each pixel/voxel belonging to normal and abnormal tissues is calculated based on information derived from the atlas and ROI. Finally, geodesic active contours use the probability map of the tumor to shrink the ROI until optimal tumor boundaries are found. This method was validated against the true segmentation (TS) of 30 LGG patients for both 2D (1 slice) and 3D. The TS was obtained from manual segmentations of three experts using the Simultaneous Truth and Performance Level Estimation (STAPLE) software. Dice and Jaccard indices and other descriptive statistics were computed for the proposed method, as well as the experts' segmentation versus the TS. We also tested the method with the BraTS datasets, which supply expert segmentations. RESULTS AND DISCUSSION: For 2D segmentation vs. TS, the mean Dice index was 0.90 ± 0.06 (standard deviation), sensitivity was 0.92, and specificity was 0.99. For 3D segmentation vs. TS, the mean Dice index was 0.89 ± 0.06, sensitivity was 0.91, and specificity was 0.99. The automated results are comparable with the experts' manual segmentation results. CONCLUSIONS: We present an accurate, robust, efficient, and reproducible segmentation method for pre-operative LGGs.

Innovative technique for the direct determination of proteins in calcified aortic valves.

Aortal valve mineralization very frequently causes a genesis of aortic stenosis, which is the most often surgically treated heart disease. Hydroxyapatite deposits have been identified as one of the causes leading to the loss of elasticity of the aortic valves. It is known that phosphates/calcium is accumulated in valve tissues during mineralization, but the mechanism of this process remains unclear. The work is focused mainly on the study of protein composition of mineralized aortic valves by nano-liquid chromatography electrospray ionization in a quadrupole orthogonal acceleration time-of-flight mass spectrometry. New methodological approach based on direct enzymatic digestion of proteins contained in hydroxyapatite deposits was developed for the study of pathological processes connected with osteogenesis. Our objectives were to simplify the traditional analytical protocols of sample preparation and to analyze the organic components of the explanted aortic valves for significant degenerative aortic stenosis. The study of aortic valve mineralization on the molecular level should contribute to understanding this process, which should consequently lead to effective prevention as well as to new ways of treatment of this grave disease.