Dynamic contrast-enhanced magnetic resonance imaging perfusion characteristics in meningiomas treated with resection and adjuvant radiosurgery

Increased blood-brain barrier permeability of the thalamus and the correlation with symptom severity and brain volume alterations in schizophrenia patients

BACKGROUND

Cumulative evidence of microvascular dysfunction has suggested the blood-brain barrier (BBB) disruption in schizophrenia, while the direct in vivo evidence from patients is inadequate. In this study, using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods, we tried to test the hypothesis that there was increased BBB permeability in schizophrenia patients, and correlated with the clinical characters, and brain volumetric alterations.

METHODS

Structural MRI and DCE-MRI data from 29 schizophrenia patients and 18 age- and sex- matched controls (HC) were obtained. We calculated the volume transfer constant (K^trans) value and compared the difference between two groups. The regions with the abnormal K^trans value were extracted as ROIs (thalamus), and the correlation with the clinical characters and grey matter volume were analysed.

RESULTS

The results revealed that, compared with the HC, the volume transfer constant (K^trans) value of the bilateral thalamus in the schizophrenia group was increased (p < 0.001). There were significant positive correlations between thalamic mean K^trans value with disease duration (p < 0.05) and symptom severity (p < 0.001). Analysis of the thalamic subregions revealed that the BBB disruption was significant in pulvinar, especially the medial pulvinar nucleus (PuM) and lateral pulvinar nucleus (PuL) (p < 0.001). The K^trans value of the whole brain, thalamus, and thalamic subregions was negatively correlated with their volume separately.

CONCLUSION

These results provided the first in vivo evidence of BBB disruption of thalamus in schizophrenia patients, and the BBB dysfunction might contribute to the pathological brain structural alterations in schizophrenia.

PET/MR Imaging of Somatostatin Receptor Expression and Tumor Vascularity in Meningioma: Implications for Pathophysiology and Tumor Outcomes

Background and Purpose

Meningiomas, the most common primary intracranial tumor, are vascular neoplasms that express somatostatin receptor-2 (SSTR2). The purpose of this investigation was to evaluate if a relationship exists between tumor vascularity and SSTR2 expression, which may play a role in meningioma prognostication and clinical management.

Materials and Methods

Gallium-68-DOTATATE PET/MRI with dynamic contrast-enhanced (DCE) perfusion was prospectively performed. Clinical and demographic patient characteristics were recorded. Tumor volumes were segmented and superimposed onto parametric DCE maps including flux rate constant (Kep), transfer constant (Ktrans), extravascular volume fraction (Ve), and plasma volume fraction (Vp). Meningioma PET standardized uptake value (SUV) and SUV ratio to superior sagittal sinus (SUVR_SSS) were recorded. Pearson correlation analyses were performed. In a random subset, analysis was repeated by a second investigator, and intraclass correlation coefficients (ICCs) were determined.

Results

Thirty-six patients with 60 meningiomas (20 WHO-1, 27 WHO-2, and 13 WHO-3) were included. Mean Kep demonstrated a strong significant positive correlation with SUV (r = 0.84, p < 0.0001) and SUVR_SSS (r = 0.81, p < 0.0001). When stratifying by WHO grade, this correlation persisted in WHO-2 (r = 0.91, p < 0.0001) and WHO-3 (r = 0.92, p = 0.0029) but not WHO-1 (r = 0.26, p = 0.4, SUVR_SSS). ICC was excellent (0.97–0.99).

Conclusion

DOTATATE PET/MRI demonstrated a strong significant correlation between tumor vascularity and SSTR2 expression in WHO-2 and WHO-3, but not WHO-1 meningiomas, suggesting biological differences in the relationship between tumor vascularity and SSTR2 expression in higher-grade meningiomas, the predictive value of which will be tested in future work.

MR diffusion and dynamic-contrast enhanced imaging to distinguish meningioma, paraganglioma, and schwannoma in the cerebellopontine angle and jugular foramen

BACKGROUND AND PURPOSE

Differentiation of meningiomas, paragangliomas, and schwannomas in the cerebellopontine angle and jugular foramen remains challenging when conventional MRI findings are inconclusive. This study aimed to assess the clinical utility of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) findings for tumor type differentiation and to identify the most significant diagnostic parameters.

METHODS

This retrospective study included 57 patients with pathologically confirmed meningiomas, paragangliomas, and schwannomas, diagnosed between January 2018 and August 2021. DWI and DCE-MRI were obtained before surgery. The apparent diffusion coefficient (ADC) and DCE-MRI parameters were calculated. The Kruskal-Wallis H test and post hoc test with Bonferroni correction and receiver operating characteristic curve were used for statistical analysis.

RESULTS

There were 20 meningiomas (6 men; 62.3 ± 17.8 years), 23 paragangliomas (3 men; 51.6 ± 17.0 years), and 14 schwannomas (7 men; 37.7 ± 20.0 years). V_p showed a significant difference in each comparison (p < .001, <.001, and <.001, respectively), V_e showed significant differences both in meningiomas and paragangliomas, and paragangliomas and schwannomas (p < .001 and .017, respectively), and K_trans showed significant differences both in meningiomas and paragangliomas, and meningiomas and schwannomas (p = .0018 and <.001, respectively), though there was no significant difference in ADC. V_p diagnostic performance values for each pair of tumors were area under the curve of 0.89-1.00, with cutoff values of 0.14-0.27.

CONCLUSION

DCE-MRI can provide promising parameters to differentiate meningiomas, paragangliomas, and schwannomas in the cerebellopontine angle and jugular foramen.

New and Advanced Magnetic Resonance Imaging Diagnostic Imaging Techniques in the Evaluation of Cranial Nerves and the Skull Base

The skull base and cranial nerves are technically challenging to evaluate using magnetic resonance (MR) imaging, owing to a combination of anatomic complexity and artifacts. However, improvements in hardware, software and sequence development seek to address these challenges. This section will discuss cranial nerve imaging, with particular attention to the techniques, applications and limitations of MR neurography, diffusion tensor imaging and tractography. Advanced MR imaging techniques for skull base pathology will also be discussed, including diffusion-weighted imaging, perfusion and permeability imaging, with a particular focus on practical applications.

Outcomes following upfront radiation versus monitoring in atypical meningiomas: 16-year experience at a tertiary medical center

Background

The role of postoperative upfront radiotherapy (RT) in the management of gross totally resected atypical meningiomas remains unclear. This single-center retrospective review of newly diagnosed histologically confirmed cases of World Health Organization (WHO) Grade II atypical meningioma at Weill Cornell Medicine from 2004 to 2020 aims to compare overall survival (OS) and progression-free survival (PFS) of postoperative upfront RT versus observation, stratified by resection status (gross total resection [GTR] vs subtotal resection [STR]).

Methods

Ninety cases of atypical meningioma were reviewed (56% women; median age 61 years; median follow-up 41 months).

Results

In patients with GTR, hazard ratio (HR) of PFS was 0.09 for postoperative upfront RT versus observation alone (95% confidence interval [CI] 0.01–0.68; P = .02), though HR for OS was not significant (HR 0.46; 95% CI 0.05–4.45; P = .5). With RT, PFS was 100% at 12 and 36 months (compared to 84% and 63%, respectively, with observation); OS at 36 months (OS36) was 100% (compared to 94% with observation). In patients with STR, though PFS at 36 months was higher for RT arm versus observation (84% vs 74%), OS36 was 100% in both arms. HR was not significant (HR 0.76; 95% CI 0.16–3.5; P = .73).

Conclusions

This retrospective study suggests postoperative upfront RT following GTR of atypical meningioma is associated with improved PFS compared to observation. Further studies are required to draw conclusions about OS.

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.

Brain tumor segmentation using 3D Mask R-CNN for dynamic susceptibility contrast enhanced perfusion imaging

The segmentation of neoplasms is an important part of radiotherapy treatment planning, monitoring disease progression, and predicting patient outcome. In the brain, functional magnetic resonance imaging (MRI) like dynamic susceptibility contrast enhanced (DSCE) or T1-weighted dynamic contrast enhanced (DCE) perfusion MRI are important tools for diagnosis. They play a crucial role in providing pre-operative assessment of tumor histology, grading, and biopsy guidance. However, the manual contouring of these neoplasms is tedious, expensive, time-consuming, and vulnerable to inter-observer variability. In this work, we propose a 3D mask region-based convolutional neural network (R-CNN) method to automatically segment brain tumors in DSCE MRI perfusion images. As our goal is to simultaneously localize and segment the tumor, our training process contained both a region-of-interest (ROI) localization and regression with voxel-wise segmentation. The combination of classification loss, ROI location and size regression loss, and segmentation loss were used to supervise the proposed network. We retrospectively investigated 21 patients' perfusion images, with between 50 and 70 perfusion time point volumes, a total of 1260 3D volumes. Tumor contours were automatically segmented by our proposed method and compared against other state-of-the-art methods and those delineated by physicians as the ground truth. The results of our method demonstrated good agreement with the ground truth contours. The average DSC, precision, recall, Hausdorff distance, mean surface distance (MSD), root MSD, and center of mass distance were 0.90 ± 0.04, 0.91 ± 0.04, 0.90 ± 0.06, 7.16 ± 5.78 mm, 0.45 ± 0.34 mm, 1.03 ± 0.72 mm, and 0.86 ± 0.91 mm, respectively. These results support the feasibility of our method in accurately localizing and segmenting brain tumors in DSCE perfusion MRI. Our 3D Mask R-CNN segmentation method in DSCE perfusion imaging has great promise for future clinical use.