Dynamic susceptibility contrast and dynamic contrast-enhanced MRI characteristics to distinguish microcystic meningiomas from traditional Grade I meningiomas and high-grade gliomas

Development of a clinical-radiological nomogram for predicting severe postoperative peritumoral brain edema following intracranial meningioma resection

Objective

The goal of this study was to develop a nomogram that integrates clinical data to predict the likelihood of severe postoperative peritumoral brain edema (PTBE) following the surgical removal of intracranial meningioma.

Method

We included 152 patients diagnosed with meningioma who were admitted to the Department of Neurosurgery at the Affiliated People's Hospital of Jiangsu University between January 2016 and March 2023. Clinical characteristics were collected from the hospital's medical record system. Factors associated with severe postoperative PTBE were identified through univariate and LASSO regression analyses of clinical, pathological, and radiological features. A multivariate logistic regression analysis was then performed incorporating all features. Based on these analyses, we developed five predictive models using R software: conventional logistic regression, XGBoost, random forest, support vector machine (SVM), and k-nearest neighbors (KNN). Model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and conducting decision curve analysis (DCA). The most optimal model was used to create a nomogram for visualization. The nomogram was validated using both a validation set and clinical impact curve analysis. Calibration curves assessed the accuracy of the clinical-radiomics nomogram in predicting outcomes, with Brier scores used as an indicator of concordance. DCA was employed to determine the clinical utility of the models by estimating net benefits at various threshold probabilities for both training and testing groups.

Results

The study involved 151 patients, with a prevalence of severe postoperative PTBE at 35.1%. Univariate logistic regression identified four potential risk factors, and LASSO regression identified four significant risk factors associated with severe postoperative PTBE. Multivariate logistic regression revealed three independent predictors: preoperative edema index, tumor enhancement intensity on MRI, and the number of large blood vessels supplying the tumor. Among all models, the conventional logistic model showed the best performance, with AUCs of 0.897 (95% CI: 0.829-0.965) and DCA scores of 0.719 (95% CI: 0.563-0.876) for each cohort, respectively. We developed a nomogram based on this model to predict severe postoperative PTBE in both training and testing cohorts. Calibration curves and Hosmer-Lemeshow tests indicated excellent agreement between predicted probabilities and observed outcomes. The Brier scores were 10.7% (95% CI: 6.7-14.7) for the training group and 25% (95% CI: 15.2-34.8) for the testing group. DCA confirmed that the nomogram provided superior net benefit across various risk thresholds for predicting severe postoperative PTBE, with a threshold probability range from 0 to 81%.

Conclusion

Utilizing conventional logistic regression within machine learning frameworks, we developed a robust prediction model. The clinical-radiological nomogram, based on conventional logistic regression, integrated clinical characteristics to enhance the prediction accuracy for severe PTBE in patients following intracranial meningioma resection. This nomogram showed promise in aiding clinicians to create personalized and optimal treatment plans by providing precise forecasts of severe PTBE.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

The Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the Differentiation of Pseudoprogression and Recurrence of Intracranial Gliomas

Objective

The objective of this study was to determine the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing postoperative changes in intracranial gliomas.

Method

A total of fifty-one patients who had new enhanced lesions after surgical resection followed by standard radiotherapy and chemotherapy were collected retrospectively from October 2014 to June 2021. The patients were divided into a pseudoprogression group (15 cases) and a recurrence group (36 cases) according to the pathological results of the second operation or a follow-up of more than six months. The follow-up data of all patients were complete, and DCE-MRI was performed. The images were processed to obtain the quantitative parameters K^trans, Ve, and Kep and the semiquantitative parameter iAUC, which were analysed with relevant statistical software.

Results

First, the difference in K^trans and iAUC values between the two groups was statistically significant (P < 0.05), and the difference in Ve and Kep values was not statistically significant (P > 0.05). Second, by comparing the area under the curve, threshold, sensitivity and specificity of K^trans, and iAUC, it was found that the iAUC threshold value was slightly higher than that of K^trans, and the specificity of K^trans was equal to that of iAUC, while the area under the curve and sensitivity of K^trans were higher than those of iAUC. Third, K^trans and iAUC had high accuracy in diagnosing recurrence and pseudoprogression, and K^trans had higher accuracy than iAUC.

Conclusion

In this study, DCE-MRI has a certain diagnostic value in the early differentiation of recurrence and pseudoprogression, offering a new method for the diagnosis and assessment of gliomas after surgery.

Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network

The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural network (CNN) architecture was constructed with two encoding paths based on T1-CE and T2-FLAIR. Each path used the same 3 × 3 × 3 kernel with different filters to weigh the spatial features of each sequence separately. Final model performance was assessed by tenfold cross-validation. Of the 96 patients, 55 (57%) were pathologically classified as Grade I and 41 (43%) as Grade II meningiomas. Optimization of our model led to a filter weighting of 18:2 between the T1-CE and T2-FLAIR MR image paths. 86 (90%) patients were classified correctly, and 10 (10%) were misclassified based on their pre-operative MRs with a model sensitivity of 0.85 and specificity of 0.93. Among the misclassified, 4 were Grade I, and 6 were Grade II. The model is robust to tumor locations and sizes. A novel asymmetric CNN with two differently weighted encoding paths was developed for successful automated meningioma grade classification. Our model outperforms CNN using a single path for single or multimodal MR-based classification.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Analysis of Microvascular Permeability in Peritumor Brain Edema of Fibrous Meningiomas

INTRODUCTION

This study aims to analyze the permeability of intra- and peri-meningiomas regions and compare the microvascular permeability between peritumoral brain edema (PTBE) and non-PTBE using DCE-MRI.

METHODS

This was a retrospective of patients with meningioma who underwent surgery. The patients were grouped as PTBE and non-PTBE. The DCE-MRI quantitative parameters, including volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp), obtained using the extended Tofts-Kety 2-compartment model. Logistic regression analysis was conducted to explore the risk factor of PTBE.

RESULTS

Sixty-three patients, diagnosed as fibrous meningioma, were included in this study. They were 17 males and 46 females, aged from 32 to 88 years old. Kep and Vp were significantly lower in patients with PTBE compared with those without (Kep: 0.1852 ± 0.0369 vs. 0.5087 ± 0.1590, p = 0.010; Vp: 0.0090 ± 0.0020 vs. 0.0521 ± 0.0262, p = 0.007), while there were no differences regarding Ktrans and Ve (both p > 0.05). The multivariable analysis showed that tumor size ≥10 cm3 (OR = 4.457, 95% CI: 1.322-15.031, p = 0.016) and Vp (OR = 0.572, 95%CI: 0.333-0.981, p = 0.044) were independently associated with PTBE in patients with meningiomas.

CONCLUSION

DCE-magnetic resonance imaging·Meningioma·Blood vessel MRI can be used to quantify the microvascular permeability of PTBE in patients with meningioma.

Neuroimaging in the Era of the Evolving WHO Classification of Brain Tumors, From the AJR Special Series on Cancer Staging

The inclusion of molecular and genetic information with histopathologic information defines the framework for brain tumor classification and grading. This framework is reflected in the major restructuring of the WHO brain tumor classification system in 2016 and in numerous subsequent proposed updates reflecting ongoing developments in understanding the impact of tumor genotype on classification and grading. This incorporation of molecular and genetic features improves tumor diagnosis and prediction of tumor behavior and response to treatment. Neuroimaging is essential for the noninvasive assessment of pretreatment tumor grading and for identification and determination of therapeutic efficacy. Use of conventional neuroimaging and physiologic imaging techniques, such as diffusion- and perfusion-weighted MRI, can increase diagnostic confidence before and after treatment. Although the use of neuroimaging to consistently determine tumor genetics is not yet robust, promising developments are on the horizon. Given the complexity of the brain tumor microenvironment, the development and implementation of a standardized reporting system can aid in conveying to radiologists, referring providers, and patients important information about brain tumor response to treatment. The purpose of this article is to review the current state and role of neuroimaging in this continuously evolving field.

Microcystic Meningiomas: MRI-Pathologic Correlation

BACKGROUND AND PURPOSE

Microcystic meningiomas (MM) are a distinctive, rare subtype of Grade I meningiomas with limited radiological descriptions. We intend to identify unique imaging phenotypes and seek radiopathological correlations.

METHODS

Retrospective analysis of histopathologically proven MM was undertaken. Clinicodemographic profiles, imaging, and histopathological characteristics were recorded. Spearman rank correlations among radiological and pathological attributes were performed.

RESULTS

Twenty-eight cases were analyzed (mean age = 45.5 years; M:F = 1:1.54; mean volume = 50.1 mL; supratentorial n = 27). Most lesions were markedly T2 hyperintense (higher than peritumoral brain edema-a unique finding) (89.3%) and showed invariable diffusion restriction, severe peritumoral brain edema (edema index >2 in 64.3%), a "storiform" pattern on T2-weighted images (T2WI) (75%), reticular pattern on postcontrast T1 (78.6%)/diffusion-weighted images (DWI) (65.4%), hyperperfusion, T1 hypointensity (84.6%), and absence of blooming on susceptibility-weighted image (80.9%). Storiform/reticular morphology correlated with large cysts on histopathology (ρ = .56; P = .005753).  Lesion dimension positively correlated with reticular morphology on imaging (ρ = .59; P = .001173), higher flow voids (ρ = .65; P = .00027), and greater microcystic changes on histopathology (ρ = .51; P = .006778). Peritumoral brain edema was higher for lesions demonstrating greater angiomatous component (ρ = .46; P = .014451).

CONCLUSIONS

We have elucidated varied neuroimaging features and highlighted pathological substrates of crucial imaging findings of MM. MM ought to be considered as an imaging possibility in an extra-axial lesion with a marked hypodensity on noncontrast computed tomography, markedly T2-hyperintense/T1-hypointense signal, and a storiform/reticular pattern on T2W/GdT1w//DWI.

Modern day imaging of meningiomas

Meningiomas are the most common primary tumors of the central nervous system and as such they are often encountered at neuroimaging. Fortunately, meningiomas are readily diagnosed with anatomic computed tomography and magnetic resonance imaging. While conventional imaging is the mainstay for initial diagnosis and delineating tumor for treatment planning and posttreatment follow-up, the last couple of decades have given rise to advanced physiologic and metabolic imaging techniques that serve as powerful tools in the management of meningioma. These modern approaches are allowing imaging to expand its utility to include extraction of biologic and potentially prognostic information that will ultimately improve care for meningioma patients.

Can we predict intraoperative blood loss in meningioma patients? Application of dynamic susceptibility contrast-enhanced magnetic resonance imaging

PURPOSE

To evaluate the potential of quantitative dynamic susceptibility contrast (DSC) perfusion MR imaging parameters as imaging biomarkers for predicting intraoperative blood loss in meningioma.

METHODS

Fifty-one non-embolized meningioma patients who had undergone preoperative DSC perfusion MR imaging were retrospectively included. The corrected relative cerebral blood volume (rCBV) and leakage coefficient (K₂) of the entire enhanced tumor were obtained using leakage correction. Tumor volume, location, grade, and other clinical variables, were also analyzed. To investigate the vascularity and vascular permeability of meningiomas, and their correlation with predicting estimated blood loss (EBL) using preoperative DSC perfusion MR imaging, the authors proposed an index reflecting the inherent tendency of meningiomas to bleed after controlling volume (i.e., EBL/cm³). Simple regression was performed to identify predictors of EBL/cm³; subsequently, the relevant variables included in the stepwise multiple linear regression.

RESULTS

On univariate analysis, EBL/cm³ was correlated with rCBV (r=0.677; P<0.001), K₂ (r=0.294; P=0.036), and tumor volume (r=-0.312, P=0.026). EBL/cm³ was not correlated with age (P=0.873), sex (P=0.404), tumor location (P=0.327), or histological grade (P=0.230). On multiple linear regression, rCBV (β=0.663 [0.463-0.864], B=1.293 [0.903-1.684; P<0.001) and K₂ (β=0.260 [0.060-0.460], B=2.277 [0.523-4.031], P=0.012), were the only independent predictors of EBL/cm³.

CONCLUSION

The rCBV and K₂ derived from DSC perfusion MR imaging in meningiomas may serve as feasible tools for clinicians to predict intraoperative blood loss and facilitate surgical planning.

Can dynamic contrast-enhanced MRI evaluate VEGF expression in brain glioma? An MRI-guided stereotactic biopsy study

PURPOSE

To investigate whether pharmacokinetic parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to evaluate vascular endothelial growth factor (VEGF) expression in brain glioma based on a point-to-point basis.

MATERIALS AND METHODS

Forty-seven patients with treatment-naïve glioma received preoperative DCE-MRI before stereotactic biopsy. We histologically quantified VEGF from section of stereotactic biopsies, and co-registered biopsy locations with localized measurements of DCE-MRI parameters including volume transfer coefficient (K^trans), reverse reflux rate constant (Kep), extracellular extravascular volume fraction (Ve) and blood plasma volume (Vp). The correlations between DCE-MRI parameters (K^trans, Kep, Ve and Vp) and VEGF were determined using Spearman correlation coefficient. P≤.05 was considered statistically significant.

RESULTS

Seventy-nine biopsy samples were obtained and graded into 45 high-grade gliomas (HGGs) and 34 low-grade gliomas (LGGs). K^trans showed a significant positive correlation with VEGF expression in HGGs group (ρ=0.505, P<0.001) and in combined group (LGGs+HGGs) (ρ=0.549, P<0.001), but not in LGGs group (P>0.05). Kep, Ve or Vp was not correlated with VEGF even though a positive trend showed (P>0.05).

CONCLUSIONS

DCE-MRI is a useful, non-invasive imaging technique for quantitative evaluation of VEGF, and its parameter K^trans other than Kep, Ve or Vp may be used as a surrogate for VEGF expression in brain gliomas.