Eftychia Kapsalaki

Obstructive sleep apnea hypopnea syndrome (OSAHS) is associated with increased risk of cerebrovascular disease. The aim of the present study was to investigate the association between the presence of the small vessel disease (SVD) of the brain in patients with OSAHS. The study included 24 patients with moderate to severe OSAHS and 34 healthy volunteers. All the subjects underwent magnetic resonance imaging (MRI) of the brain, in order to sought periventricular white matter (PVWM), deep white matter (DWM) and brainstem SVD. Among patients with OSAHS, 79.1% had SVD (grade 1–3, Fazekas score) in DWM and 91.7% in PVWM while 22.4% had brainstem—white matter hyperintensities (B-WMH). Patients with OSAHS had a much higher degree of SVD in the DWM and PVWM compared to the control group (p < 0.001). The multivariate analysis showed an independent significant association of OSAHS with SVD (DWM and PVWM) (p = 0.033, OR 95% CI: 8.66 (1.19–63.08) and: p = 0.002, OR 95% CI: 104.98 (5.15–2141)). The same analysis showed a moderate association of OSAHS with B-WMH (p = 0.050, OR 15.07 (0.97–234.65)). Our study demonstrated an independent significant association of OSAHS with SVD and a moderate association of OSAHS with B-WMH.

Our study evaluated the role of the T2–fluid-attenuated inversion recovery (FLAIR) mismatch sign in detecting isocitrate dehydrogenase (IDH) mutations based on a mixed sample of 24 patients with low- and high- grade gliomas. The association between the two was realized using univariate and multivariate logistic regression analysis. There was a substantial agreement between the two raters for the detection of the T2–FLAIR mismatch sign (Cohen’s kappa coefficient was 0.647). The T2–FLAIR mismatch sign when co-registered with the degree of tumor homogeneity were significant predictors of the IDH status (OR 29.642; 95% CI 1.73–509.15, p = 0.019). The probability of being IDH mutant in the presence of T2–FLAIR mismatch sign was as high as 92.9% (95% CI 63–99%). The sensitivity and specificity of T2–FLAIR mismatch sign in the detection of the IDH mutation was 88.9% and 86.7%, respectively. The T2–FLAIR mismatch sign may be an easy to use and helpful tool in recognizing IDH mutant patients, particularly if formal IDH testing is not available. We suggest that the adoption of a protocol based on imaging and histological data for optimal glioma characterization could be very helpful.

To present the anatomical variations of vidian canal (VC) and sphenoid sinus (SS), relative to other anatomical landmarks of skull base area, which may be helpful for safer surgical approach to this area.

Background: Machine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. Methods: 333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. Results: Features from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1. Conclusion: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading. Key points: - On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% <1 grade difference) - Shape features distinguish best grade II from grade III gliomas. - Texture and distribution features distinguish best grade III from grade IV tumours. Importance of study: This work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% <1 grade difference) and 71% across mutation status) was obtained. Therefore, our results provide a proof-of-concept for this emerging precision medicine field that has good generalisability and scalability properties. Introspection on the classification errors highlighted mostly borderline cases and helped underline the challenges of a categorical classification in a pathological continuum. With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

The role of conventional Magnetic Resonance Imaging (MRI) in the detection of cerebral tumors has been well established. However its excellent soft tissue visualization and variety of imaging sequences are in many cases non-specific for the assessment of brain tumor grading. Hence, advanced MRI techniques, like Diffusion-Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI) and Dynamic-Susceptibility Contrast Imaging (DSCI), which are based on different contrast principles, have been used in the clinical routine to improve diagnostic accuracy. The variety of quantitative information derived from these techniques provides significant structural and functional information in a cellular level, highlighting aspects of the underlying brain pathophysiology. The present work, reviews physical principles and recent results obtained using DWI/DTI and DSCI, in tumor characterization and grading of the most common cerebral neoplasms, and discusses how the available MR quantitative data can be utilized through advanced methods of analysis, in order to optimize clinical decision making.

Introduction: We report on a very rare case of a cervical ligamentum flavum cyst, which presented with progressive myelopathy and radiculopathy. The cyst was radically extirpated and our patient showed significant recovery. A review of the relevant literature yielded seven cases. Case presentation: An 82-year-old Greek woman presented with progressive bilateral weakness of her upper extremities and causalgia, cervical pain, episodes of upper extremity numbness and significant walking difficulties. Her neurological examination showed diffusely decreased motor strength in both her upper and lower extremities. Magnetic resonance imaging of her cervical spine demonstrated a large, well-demarcated cystic lesion on the dorsal aspect of her spinal cord at the C3 to C4 level, significantly compressing the spinal cord at this level, in close proximity to the yellow ligament and the C3 left lamina. The largest diameter of this lesion was 1.4 cm, and there was no lesion enhancement after the intravenous administration of a paramagnetic contrast. The lesion was surgically removed after a bilateral C3 laminectomy. The thick cystic wall was yellow and fibro-elastic in consistency, while its content was gelatinous and yellow-brownish. A postoperative cervical-spine magnetic resonance image was obtained before her discharge, demonstrating decompression of her spinal cord and dural expansion. Her six-month follow-up evaluation revealed complete resolution of her walking difficulties, improvement in the muscle strength of her arms (4+/5 in all the affected muscle groups), no causalgia and a significant decrease in her preoperative upper extremity numbness. Conclusion: Cervical ligamentum flavum cysts are rare benign lesions, which should be included in the list of differential diagnosis of spinal cystic lesions. They can be differentiated from other intracanalicular lesions by their hypointense appearance on T₁-weighted and hyperintense appearance on T₂-weighted magnetic resonance images, with contrast enhancement of the cystic wall. Surgical extirpation of the cyst is required for symptom alleviation and decompression of the spinal cord. The outcome of these cysts is excellent with no risk of recurrence.

Aneurysmal subarachnoid hemorrhage constitutes a clinical entity associated with high mortality and morbidity. It is widely accepted that improper clip placement may have as a result of incomplete aneurysm occlusion and/or partial or complete obstruction of an adjacent vessel. Various modalities, including intraoperative or postoperative digital subtracting angiography, near-infrared indocyanine green angiography, micro-Doppler ultrasonography (MDU), and neurophysiological studies, have been utilized for verifying proper clip placement. The aim of our study was to review the role of MDU during aneurysmal surgery.

Open access peer-reviewed chapter

Introduction: Quantification of cerebrospinal fluid (CSF) flow through the cerebral aqueduct is of paramount importance in patients with hydrocephalus. The purpose of this study was to evaluate the normal CSF flow measurements at three different anatomical levels of the aqueduct utilizing 3-Tesla (3 T) magnetic resonance imaging. Materials and methods: The CSF hydrodynamics in 22 healthy volunteers were evaluated. Phase-contrast cine MRI was performed on a 3 T General Electric MR system (GE Medical Systems, Milwaukee, WI, USA). A cardiac-gated, flow-compensated GRE sequence with flow encoding was used, and the aqueduct was visualized using a sagittal T1 FLAIR sequence. Velocity maps were acquired at three different anatomical levels. Region-of-interest (ROI) analysis was performed. Results: CSF flow velocities were slightly increased at the upper in comparison with the lower part of the aqueduct. The mean values for the peak positive and negative velocity and the mean average flow were calculated for both ROIs. Discussion/Conclusions: CSF peak positive velocity, peak negative velocity, and mean flow through the aqueduct were calculated in 22 young healthy volunteers performed at 3 T. Our measurements did not show significant difference compared with the reported measurements obtained at 1.5 T. Slight differences were observed in the CSF hydrodynamic measurements, depending on the anatomical level of the aqueduct; however, they did not vary significantly.