Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

Histogram Analysis of Advanced Diffusion-weighted MRI Models for Evaluating the Grade and Proliferative Activity of Meningiomas

RATIONALE AND OBJECTIVES

To explore the value of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) magnetic resonance imaging histogram analysis in evaluating the grade and proliferative activity of meningiomas.

MATERIALS AND METHODS

A total of 134 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumor histogram parameters were extracted from multiple functional maps. Mann-Whitney U test was used to compare the histogram parameters of high- and low-grade meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analysis were used to evaluate the diagnostic efficacy. The correlation between histogram parameters and the Ki-67 index was analyzed. The diffusion model was further validated with an independently validation set (n = 33).

RESULTS

Among single histogram parameters, the variance of NODDI-ISOVF (isotropic volume fraction) showed the highest AUC of 0.829 in grading meningiomas. For the combined models, the DKI model had the best performance in the diagnosis (AUC=0.925). Delong test showed the DKI combined model showed superior diagnostic performance to those of DTI, NODDI and MAP models (P < 0.05 for all). Moreover, moderate to weak correlations were found between various diffusion parameters and the Ki-67 labeling index (rho=0.20-0.45, P < 0.05 for all). In the validation set, the DKI model still showed higher performance (AUC, 0.85) than other diffusion models, thus demonstrating robustness.

CONCLUSIONS

Whole-tumor histogram analyses of DTI, DKI, NODDI, and MAP are useful for evaluating the grade and cellular proliferation of meningiomas. DKI combined model has higher diagnostic accuracy than DTI, NODDI and MAP in meningioma grading.

Malignant Meningiomas: From Diagnostics to Treatment

Meningiomas account for approximately 40% of all primary brain tumors, of which 1.5% are classified as grade 3. Whilst meningiomas are discovered on imaging with high-grade meningiomas being associated with certain imaging features, the final diagnosis is based on histopathology in combination with molecular markers. According to the latest World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS), grade 3 should be assigned based on criteria for anaplastic meningiomas, which comprise malignant cytomorphology (anaplasia) that resembles carcinoma, high-grade sarcoma or melanoma; elevated mitotic activity; a TERT promoter mutation and/or a homozygous CDKN2A and/or CDKN2B deletion. Surgery remains the mainstay treatment modality for grade 3 meningiomas, followed by radiotherapy. Limited data are available on the effect of stereotactic radiosurgery and systemic therapy for grade 3 meningiomas; however, studies are underway. Despite optimal treatment, the estimated recurrence rate ranges between 50% and 95% with a 5-year survival rate of 66% and a 10-year estimated survival rate of 14% to 24%.

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

Arterial spin labeled perfusion MRI for the assessment of radiation-treated meningiomas

Purpose

Conventional contrast-enhanced MRI is currently the primary imaging technique used to evaluate radiation treatment response in meningiomas. However, newer perfusion-weighted MRI techniques, such as 3D pseudocontinuous arterial spin labeling (3D pCASL) MRI, capture physiologic information beyond the structural information provided by conventional MRI and may provide additional complementary treatment response information. The purpose of this study is to assess 3D pCASL for the evaluation of radiation-treated meningiomas.

Methods

Twenty patients with meningioma treated with surgical resection followed by radiation, or by radiation alone, were included in this retrospective single-institution study. Patients were evaluated with 3D pCASL and conventional contrast-enhanced MRI before and after radiation (median follow up 6.5 months). Maximum pre- and post-radiation ASL normalized cerebral blood flow (ASL-nCBF) was measured within each meningioma and radiation-treated meningioma (or residual resected and radiated meningioma), and the contrast-enhancing area was measured for each meningioma. Wilcoxon signed-rank tests were used to compare pre- and post-radiation ASL-nCBF and pre- and post-radiation area.

Results

All treated meningiomas demonstrated decreased ASL-nCBF following radiation (p < 0.001). Meningioma contrast-enhancing area also decreased after radiation (p = 0.008) but only for approximately half of the meningiomas (9), while half (10) remained stable. A larger effect size (Wilcoxon signed-rank effect size) was seen for ASL-nCBF measurements (r = 0.877) compared to contrast-enhanced area measurements (r = 0.597).

Conclusions

ASL perfusion may provide complementary treatment response information in radiation-treated meningiomas. This complementary information could aid clinical decision-making and provide an additional endpoint for clinical trials.

Correlation of the apparent diffusion coefficient with the standardized uptake value in meningioma of the skull plane using [68]Ga-DOTATOC PET/MRI

PURPOSE

To evaluate a correlation between an MRI-specific marker for cellular density [apparent diffusion coefficient (ADC)] and the expression of Somatostatin Receptors (SSTR) in patients with meningioma of the skull plane and orbital space.

METHODS

68 Ga-DOTATOC PET/MR imaging was performed in 60 Patients with suspected or diagnosed meningiomas of the skull base and eye socket. Analysis of ADC values succeeded in 32 patients. ADC values (ADC mean and ADC min ) were analyzed using a polygonal region of interest. Tracer-uptake of target lesions was assessed according to corresponding maximal (SUV max ) and mean (SUV mean ) values. Correlations between assessed parameters were evaluated using the Pearson correlation coefficient.

RESULTS

One out of 32 patients (3%) was diagnosed with lymphoma by histopathological examination and therefore excluded from further analysis. Median ADC mean amounted to 822 × 10 -5  mm²/s -1 (95% CI: 570-1497) and median ADC min was 493 × 10 -5 mm 2 /s -1 (95% CI: 162-783). There were no significant correlations between SUV max and ADC min (r = 0.60; P  = 0.76) or ADC mean (r = -0.52; P  = 0.79), respectively. However, Pearson's test showed a weak, inverse but insignificant correlation between ADC mean and SUV mean (r = -0.33; P  = 0.07).

CONCLUSION

The presented data displays no relevant correlations between increased SSTR expression and cellularity in patients with meningioma of the skull base. SSTR-PET and DWI thus may offer complementary information on tumor characteristics of meningioma.

Advanced Meningioma Imaging

Noninvasive imaging methods are used to accurately diagnose meningiomas and track their growth and location. These techniques, including computed tomography, MRI, and nuclear medicine, are also being used to gather more information about the biology of the tumors and potentially predict their grade and impact on prognosis. In this article, we will discuss the current and developing uses of these imaging techniques including additional analysis using radiomics in the diagnosis and treatment of meningiomas, including treatment planning and prediction of tumor behavior.

Insights Into Meningioma Visibility on Arterial Spin Labeling MRI: Location Outweighs Size

Background Arterial Spin Labeling (ASL) MRI is a non-invasive imaging technique with potential applications for assessing meningiomas. This retrospective study aimed to investigate the impact of tumor location, size, age, and sex on the ASL visibility of meningiomas. Methods We retrospectively analysed 40 patients with meningiomas, who underwent 3 Tesla MRI examinations using a three-dimensional (3D) pulsed ASL technique. Tumor location was categorized as around the skull base or elsewhere, and size was determined by the area in the transverse plane. Results Our findings revealed that meningiomas around the skull base were significantly more likely to be ASL-visible compared to those located elsewhere (p < 0.001), whereas tumor size, age, and sex did not show a significant correlation with ASL visibility. This observation suggests that tumor location is a critical factor in determining the visibility of meningiomas on ASL MRI. Conclusion The results contribute to a better understanding of ASL visibility in meningiomas, highlighting the importance of tumor location over size. Further research, including larger cohorts and additional factors, such as histological variants, is needed to expand upon these findings and explore their clinical implications.

Arterial spin labeling clinical applications for brain tumors and tumor treatment complications: A comprehensive case-based review

Arterial spin labeling (ASL) is a noninvasive neuroimaging technique that allows for quantifying cerebral blood flow without intravenous contrast. Various neurovascular disorders and tumors have cerebral blood flow alterations. Identifying these perfusion changes through ASL can aid in the diagnosis, especially in entities with normal structural imaging. In addition, complications of tumor treatment and tumor progression can also be monitored using ASL. In this case-based review, we demonstrate the clinical applications of ASL in diagnosing and monitoring brain tumors and treatment complications.

Effects of Post-Labeling Delay on Magnetic Resonance Evaluation of Brain Tumor Blood Flow Using Arterial Spin Labeling

We investigated the effect of post-labeling delay (PLD) on the evaluation of brain tumor blood flow using arterial spin labeling (ASL) magnetic resonance (MR) imaging to assess the need for imaging with two PLDs. Retrospective analysis was conducted on 63 adult patients with brain tumors who underwent contrast-enhanced MR imaging including ASL imaging with PLDs of both 1525 and 2525 ms on a 1.5 T or 3 T MR unit. Blood flow was estimated in the tumors and normal-appearing brain parenchyma, and tumor blood flow was normalized by parenchymal flow. Estimates of tumor blood flow, parenchymal flow, and normalized tumor flow showed no statistically significant differences between PLDs of 1525 and 2525 ms. Close correlations between different PLDs were found, with the closest correlation for normalized tumor flow. These results were similarly observed for the 1.5 T and 3 T units. The blood flow estimates obtained using ASL MR imaging in patients with brain tumors were highly concordant between PLDs of 1525 and 2525 ms, irrespective of the magnetic field strength. It is indicated that imaging with a single, standard PLD is acceptable for ASL assessment of brain tumor perfusion and that additional imaging with a long PLD is not required.

Imaging Characteristics of Meningiomas

Contemporary neuroimaging of meningiomas has largely relied on computed tomography, and more recently magnetic resonance imaging. While these modalities are frequently used in nearly all clinical settings where meningiomas are treated for the routine diagnosis and follow-up of these tumors, advances in neuroimaging have provided novel opportunities for prognostication and treatment planning (including both surgical planning and radiotherapy planning). These include perfusion MRIs, and positron emission tomography (PET) imaging modalities. Here we will summarize the contemporary uses for neuroimaging in meningiomas, and future applications of novel, cutting edge imaging techniques that may be routinely implemented in the future to enable more precise treatment of these challenging tumors.

Can magnetic resonance imaging predict preoperative consistency and vascularity of intracranial meningioma?

Background

Meningiomas are considered the most common primary intracranial neoplasms. The surgical resection is the main curative therapy. Evaluation of meningioma consistency and vascularity is important before surgery to be aware about the difficulties that neurosurgeon will face during resection, the possibility of total resection and to determine which equipment will be suitable for surgery. The purpose of this study was to identify the relationship between the MRI predictors of meningioma consistency [utilizing tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios] as well as tumor vascularity (utilizing arterial spin labeling perfusion) in correlation with intraoperative findings. The study was carried out on 40 patients with MRI features of intracranial meningiomas. Non-contrast conventional MRI followed by arterial spin labeling MR perfusion and post contrast sequences were done for all cases. Final diagnosis of the cases was established by histopathological data while consistency and vascularity was confirmed by operative findings.

Results

According to surgical data, the studied cases of intracranial meningiomas were classified according to tumor consistency into 19 cases (47.5%) showing soft consistency, 14 cases (35%) showing intermediate consistency and 7 cases (17.5%) showing firm/hard consistency. TCTI ratio was the most significant MRI parameter in correlation with operative consistency of meningiomas, with soft lesions showing TCTI ranging from 1.75 to 2.87, intermediate consistency lesions TCTI ranging from 1.3 to 1.6, and firm lesions TCTI ranging from 0.9 to 1.2. According to intraoperative vascularity, cases were classified into 27 cases (67.5%) showing hypervascularity, 6 cases (15%) showing intermediate vascularity and 7 cases (17.5%) showing hypovascularity. Arterial spin labeling (ASL) was the most significant MRI parameter in correlation with operative vascularity of meningiomas, with hypervascular lesions showing normalized cerebral blood flow (n-CBF) ranging from 2.10 to 14.20, intermediately vascular lesions ranging from 1.50 to 1.60, and hypovascular lesions ranging from 0.70 to 0.90.

Conclusions

TCTI ratio showed good correlation with intraoperative meningioma consistency. ASL MR perfusion as a noninvasive technique is a reliable method to predict vascularity of meningioma in cases where IV contrast is contraindicated.

Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study

OBJECTIVES

Develop and evaluate a deep learning-based automatic meningioma segmentation method for preoperative meningioma differentiation using radiomic features.

METHODS

A retrospective multicentre inclusion of MR examinations (T1/T2-weighted and contrast-enhanced T1-weighted imaging) was conducted. Data from centre 1 were allocated to training (n = 307, age = 50.94 ± 11.51) and internal testing (n = 238, age = 50.70 ± 12.72) cohorts, and data from centre 2 external testing cohort (n = 64, age = 48.45 ± 13.59). A modified attention U-Net was trained for meningioma segmentation. Segmentation accuracy was evaluated by five quantitative metrics. The agreement between radiomic features from manual and automatic segmentations was assessed using intra class correlation coefficient (ICC). After univariate and minimum-redundancy-maximum-relevance feature selection, L1-regularized logistic regression models for differentiating between low-grade (I) and high-grade (II and III) meningiomas were separately constructed using manual and automatic segmentations; their performances were evaluated using ROC analysis.

RESULTS

Dice of meningioma segmentation for the internal testing cohort were 0.94 ± 0.04 and 0.91 ± 0.05 for tumour volumes in contrast-enhanced T1-weighted and T2-weighted images, respectively; those for the external testing cohort were 0.90 ± 0.07 and 0.88 ± 0.07. Features extracted using manual and automatic segmentations agreed well, for both the internal (ICC = 0.94, interquartile range: 0.88-0.97) and external (ICC = 0.90, interquartile range: 0.78-70.96) testing cohorts. AUC of radiomic model with automatic segmentation was comparable with that of the model with manual segmentation for both the internal (0.95 vs. 0.93, p = 0.176) and external (0.88 vs. 0.91, p = 0.419) testing cohorts.

CONCLUSIONS

The developed deep learning-based segmentation method enables automatic and accurate extraction of meningioma from multiparametric MR images and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

KEY POINTS

• A deep learning-based method was developed for automatic segmentation of meningioma from multiparametric MR images. • The automatic segmentation method enabled accurate extraction of meningiomas and yielded radiomic features that were highly consistent with those that were obtained using manual segmentation. • High-grade meningiomas were preoperatively differentiated from low-grade meningiomas using a radiomic model constructed on features from automatic segmentation.

Use of advanced neuroimaging and artificial intelligence in meningiomas

Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.

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.

Tumor volume and the dural tail sign enable the differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from high-grade meningioma

OBJECTIVES

Intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) is a rare mesenchymal neoplasm with imaging features mimicking high-grade meningioma (HGM) and can easily be misdiagnosed. We sought to determine the value of routine preoperative data in differentiating these tumors.

PATIENTS AND METHODS

Patients with confirmed SFT/HPC or HGM between January 2012 and June 2020 were identified. A total of 28 preoperative variables (including age, sex, tumor location, tumor volume, 10 traditional MRI features, and 14 peripheral blood indices) were collected for each patient. The top features were selected sequentially based on the least absolute shrinkage and selection operator (LASSO) and support vector machines-recursive feature elimination (SVM-RFE) methods. Differentiation and calibration of the classifiers were assessed by receiver operating characteristic (ROC) curves and calibration curves, respectively. Nomograms were constructed based on multivariate analysis.

RESULTS

A total of 127 patients, including 29 with SFT/HPC and 98 with HGM, were analyzed. Three features were first selected using the LASSO and SVM-RFE methods, and corresponding models were developed. Although the area under the curve (AUC) of model 1 was the highest, a comprehensive analysis suggested the superiority of model 2, which consisted only of the features tumor volume (TV) and dural tail sign (DTS) (AUC: 0.942, sensitivity: 93.10%, p-value of H-L test: 0.734, Brier score: 0.07). A risk score formula and a nomogram were constructed.

CONCLUSIONS

TV can be used to effectively identify SFT/HPC and HGM, whereas adding DTS can improve the overall prediction accuracy. As these two variables are routinely available and are easy for clinicians to master, they can provide a powerful reference for clinical decision-making.

Giant Intraparenchymal Meningioma in a Female Child: Case Report and Literature Review

BACKGROUND

Intraparenchymal meningiomas without dural attachment are extremely rare, especially in female children. To our knowledge, fibrous intraparenchymal meningioma located in the temporal lobe has never been reported in female children. The significance in the differential diagnosis of lesions in the temporal lobe should be emphasized.

CASE PRESENTATION

A 12-year-old girl was admitted to our hospital, complaining of recurrent generalized seizures for 2 months. Magnetic resonance imaging demonstrated a solid lesion located in the temporal lobe. The lesion underwent gross total resection. Histopathological examination indicated that the lesion was a fibrous meningioma. Postoperative rehabilitation was uneventful.

CONCLUSION

This case report presents an extremely unusual intraparenchymal fibrous meningioma of the temporal lobe with peritumoral edema and reviewed 21 intraparenchymal meningioma cases in children and to discuss the clinical presentation and treatment, differential diagnosis, and radiological features.

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Clinical utility of arterial spin labeling imaging in disorders of the nervous system

Neuroimaging is an indispensable tool in the workup and management of patients with neurological disorders. Arterial spin labeling (ASL) is an imaging modality that permits the examination of blood flow and perfusion without the need for contrast injection. Noninvasive in nature, ASL provides a feasible alternative to existing vascular imaging techniques, including angiography and perfusion imaging. While promising, ASL has yet to be fully incorporated into the diagnosis and management of neurological disorders. This article presents a review of the most recent literature on ASL, with a special focus on its use in moyamoya disease, brain neoplasms, seizures, and migraines and a commentary on recent advances in ASL that make the imaging technique more attractive as a clinically useful tool.

Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI

PURPOSE

To investigate the prediction performance of radiomic models based on multiparametric MRI in predicting the meningioma grade.

METHOD

In all, 229 low-grade [Grade I] and 87 high-grade [Grade II/III] patients with pathologically diagnosed meningiomas were enrolled. Radiomic features from conventional MRI (cMRI), ADC maps and SWI were extracted based on the volume of entire tumor. Classification performance of different radiomic models (cMRI, ADC, SWI, cMRI + ADC, cMRI + SWI, ADC + SWI, and cMRI + ADC + SWI models) was evaluated by a nested LOOCV approach, combining the LASSO feature selection and RF classifier that was trained (1) without subsampling, and (2) with the synthetic minority over-sampling technique (SMOTE). The prediction performance of radiomic models was assessed using ROC curve and AUC of them was compared using Delong's test.

RESULTS

The cMRI + ADC + SWI model demonstrated the best performance without or with subsampling, which AUCs were 0.84 and 0.81, respectively. Following the cMRI + ADC + SWI model, the AUC range of the other models was 0.75-0.80 without subsampling, and was 0.71-0.79 with subsampling. Although the cMRI + ADC model and cMRI + SWI model showed higher AUCs than the cMRI model without subsampling (0.77 vs 0.80, P = 0.037 and 0.77 vs 0.80, P = 0.009, respectively), there was no significant difference among these models with subsampling (0.78 vs 0.77, P = 0.552 and 0.78 vs 0.79, P = 0.246, respectively).

CONCLUSIONS

Multiparametric radiomic model based on cMRI, ADC map and SWI yielded the best prediction performance in predicting the meningioma grade, which might offer potential guidance in clinical decision-making.

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine.

Utility of Arterial Spin Labeling Magnetic Resonance Imaging in Differentiating Sellar Region Meningiomas from Pituitary Adenomas

BACKGROUND

Differentiating sellar region meningiomas from pituitary adenomas on standard magnetic resonance imaging (MRI) sequences can be difficult. Arterial spin labeling (ASL) is a noninvasive technique of magnetic resonance perfusion imaging. The range of applications of ASL in neurosurgery has increased, and the information provided can be unique and complementary to other MRI sequences. Here we investigate the utility of ASL MRI in differentiating between sellar region meningiomas and pituitary adenomas.

METHODS

This was a retrospective comparison of quantitative assessments on absolute and normalized tumor blood flow in histologically proven meningiomas versus pituitary adenomas.

RESULTS

A total of 15 patients with sellar region lesions were identified, including 9 meningiomas and 6 pituitary adenomas. Mean absolute tumor blood flow and normalized tumor blood flow were significantly higher in meningiomas (131 mL/100 g/min and 2.22) than adenomas (47 mL/100 g/min and 0.92; P < 0.05).

CONCLUSIONS

ASL MRI is a useful adjunct sequence in differentiating sellar region meningiomas, which exhibit high perfusion, from pituitary adenomas, which exhibit relatively low perfusion.

Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas

PURPOSE

Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.

METHODS

Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.

RESULTS

Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.

CONCLUSION

Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.

Histological Grade of Meningioma: Prediction by Intravoxel Incoherent Motion Histogram Parameters

RATIONALE AND OBJECTIVES

To evaluate the usefulness of intravoxel incoherent motion (IVIM) histogram analysis for differentiating low-grade meningiomas (LGMs) and high-grade meningiomas (HGMs).

MATERIALS AND METHODS

Fifty-nine patients with pathologically confirmed meningiomas (45 LGMs and 14 HGMs) underwent IVIM MR imaging. Maps of IVIM parameters (perfusion fraction, f; true diffusion coefficient, D; and pseudo diffusion coefficient, D*), as well as of the apparent diffusion coefficient (ADC), were generated. Histogram analysis was performed using parametric values from all voxels in regions-of-interest manually drawn to encompass the whole tumor. The histogram results of ADC and IVIM parameters were compared using the Mann-Whitney U test. Area under the receiver operating characteristic curve (AUC) values were generated to evaluate how well each parameter could differentiate LGMs from HGMs. Spearman's rank correlation coefficients were used to evaluate correlations between histogram parameters and Ki-67 expression.

RESULTS

Compared to LGM, HGM showed significantly higher standard deviation (SD), variance, and coefficient of variation (CV) of ADC (p< 0.006-0.028; AUC, 0.693-0.748), D (p< 0.004-0.032; AUC, 0.670-0.752), and significantly higher CV of f (p< 0.005-0.024; AUC = 0.737). Means and percentiles of ADC and IVIM parameters did not differ significantly between LGM and HGM. Significant positive correlations were identified between Ki-67 and histogram parameters of ADC (SD, variance, kurtosis, skewness, and CV) and D (SD, variance, kurtosis, and CV), whereas no significant correlation with Ki-67 was shown for mean or percentiles of ADC and IVIM parameters.

CONCLUSION

Heterogeneity histogram parameters of ADC, D, and f may be useful for differentiating LGMs from HGMs.

Pseudo-continuous arterial spin labelling shows high diagnostic performance in the detection of postoperative residual lesion in hyper-vascularised adult brain tumours

OBJECTIVES

Our aim was to evaluate the contribution of pseudo-continuous arterial spin labelling (pCASL) in the detection of a postoperative residual lesion in adult brain tumours.

METHODS

Seventy-five patients were prospectively included. Following the results of preoperative DSC-PWI assessment, intra-axial lesions, including high-grade gliomas (n = 43) and certain metastases (n = 14), were classified as hyper-vascular (HV+ group, n = 57); other lesions, including low-grade gliomas and certain metastases, were classified as non-hyper-vascular (HV- group, n = 18). To confirm the absence/presence of a residual lesion or disease progression, postoperative MRI including pCASL sequence and follow-up-MRI were performed within 72 h and 1-6 months after the resection, respectively. Two raters evaluated the images. Mean and maximal ASL cerebral blood flow (CBF) values were measured in the perioperative region and normalised to the contralateral tissue. The pCASL-CBF maps and post-contrast T1WI were visually assessed for residual lesion. Quantitative data were analysed with unpaired Student t and Mann-Whitney U tests and the visual diagnostic performance with the McNemar test.

RESULTS

In the HV+ group, the mean normalised CBF was 1.97 ± 0.59 and 0.97 ± 0.29 (p < 0.0001, AUC = 0.964, cut-off = 1.27) for patients with or without residual tumours, respectively. The mean normalised CBF was not discriminative for assessing residual tumours in the HV- group (p = 0.454). Visual CBF evaluation allowed 92.98% patients belonging to the HV+ group to be correctly classified (sensitivity 93.02%, specificity 92.86%, p < 0.001). Visual evaluation was correlated with contrast enhancement evaluation and with the mean normalised CBF values (r = 0.505, p < 0.0001 and 0.838, p < 0.0001, respectively).

CONCLUSION

Qualitative and quantitative ASL evaluation shows high diagnostic performance in postoperative assessment of hyper-perfused tumours. In this case, postoperative pCASL may be useful, especially if contrast injection cannot be performed or when contrast enhancement is doubtful.

KEY POINTS

• Evaluation of postoperative residual lesion in the case of brain tumours is an imaging challenge. • This prospective monocentric study showed that increased normalised cerebral blood flow assessed by pseudo-continuous arterial spin labelling (pCASL) correlates well with the presence of a residual tumour in the case of hyper-vascular tumour diagnosed on preoperative MRI. • Qualitative and quantitative pCASL is an informative sequence for hyper-vascular residual tumour, especially if acquired more than 48 h after brain tumour surgery, when contrast enhancement can give ambiguous results due to blood-brain barrier disruption.

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.

Diagnostic performance of diffusion and perfusion MRI in differentiating high from low-grade meningiomas: A systematic review and meta-analysis

OBJECTIVES

The purpose of the present meta-analysis and systematic review was to evaluate the currently published data on the potential role of perfusion (PWI) and diffusion (DWI) weighted imaging for the assessment of meningioma grade.

PATIENTS AND METHODS

A search of MEDLINE and relative reference lists was conducted to identify all the eligible studies assessing the diagnostic performance of DWI and PWI in grading meningiomas. Meta-Disc and Rev-Man were used for the statistical analysis. Methodological quality and risk of bias were assessed with the use of the updated Quality assessment of the diagnostic accuracy (QUADAS-2) tool. Pooled sensitivity, specificity and area under the summary receiver operating characteristic curve were calculated individually for DWI and PWI to demonstrate the diagnostic performance of each modality.

RESULTS

Fourteen studies with 1063 patients were included. The 8 studies evaluating DWI showed a pooled sensitivity of 80% (95% CI, 74%-86%) and a pooled specificity of 76% (95% CI, 72%-79%). As for the 6 remaining studies concerning PWI, the pooled sensitivity and specificity were found 80% (95% CI, 71%-88%) and 91% (95% CI, 87%-94%), respectively. The area under the SROC curve was 0.94 (95% CI) for PWI and 0.91 (95% CI) for DWI. The comparison of the two AUCs showed that neither technique was superior with regards to the diagnostic performance.

CONCLUSIONS

The current evidence proves that both techniques are efficient at differentiating high from low-grade meningiomas.

Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI

BACKGROUND

It is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas.

PURPOSE

To evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort.

FIELD STRENGTH/SEQUENCE

T₁ -weighted, T₂ -weighted, and contrast-enhanced T₁ -weighted imaging were performed on 1.5 or 3.0T MR systems from two centers.

ASSESSMENT

Tumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two-step feature selection. Three single-sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort.

STATISTICAL TESTS

Area under receiver operating characteristic curve (AUC), accuracy (Acc), f1-score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models.

RESULTS

Among the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort).

DATA CONCLUSION

Nonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1810-1820.

Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping

BACKGROUND AND PURPOSE

The ability to predict high-grade meningioma preoperatively is important for clinical surgical planning. The purpose of this study is to evaluate the performance of comprehensive multiparametric MRI, including susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) in predicting high-grade meningioma both qualitatively and quantitatively.

METHODS

Ninety-two low-grade and 37 higher grade meningiomas in 129 patients were included in this study. Morphological characteristics, quantitative histogram analysis of QSM and ADC images, and tumor size were evaluated to predict high-grade meningioma using univariate and multivariate analyses. Receiver operating characteristic (ROC) analyses were performed on the morphological characteristics. Associations between Ki-67 proliferative index (PI) and quantitative parameters were calculated using Pearson correlation analyses.

RESULTS

For predicting high-grade meningiomas, the best predictive model in multivariate logistic regression analyses included calcification (β=0.874, P=0.110), peritumoral edema (β=0.554, P=0.042), tumor border (β=0.862, P=0.024), tumor location (β=0.545, P=0.039) for morphological characteristics, and tumor size (β=4×10^-5, P=0.004), QSM kurtosis (β=-5×10^-3, P=0.058), QSM entropy (β=-0.067, P=0.054), maximum ADC (β=-1.6×10^-3, P=0.003), ADC kurtosis (β=-0.013, P=0.014) for quantitative characteristics. ROC analyses on morphological characteristics resulted in an area under the curve (AUC) of 0.71 (0.61-0.81) for a combination of them. There were significant correlations between Ki-67 PI and mean ADC (r=-0.277, P=0.031), 25^th percentile of ADC (r=-0.275, P=0.032), and 50^th percentile of ADC (r=-0.268, P=0.037).

CONCLUSIONS

Although SWI and QSM did not improve differentiation between low and high-grade meningiomas, combining morphological characteristics and quantitative metrics can help predict high-grade meningioma.

Non-invasive qualitative and semiquantitative presurgical investigation of the feeding vasculature to intracranial meningiomas using superselective arterial spin labeling

BACKGROUND

Intracranial meningiomas may be amenable to presurgical embolization to reduce bleeding complications. Detailed information usually obtained by digital subtraction angiography (DSA) on the contribution of blood supply from internal and external carotid artery branches is required to prevent non-target embolization and is helpful for pre-surgical planning.

PURPOSE

To investigate the contribution of the feeding vasculature to intracranial meningiomas with superselective arterial spin labelling (sASL) as an alternative to DSA.

MATERIAL AND METHODS

Consecutive patients presenting for meningioma resection were prospectively included. sASL perfusion images acquired on a clinical 3T MRI scanner were independently rated by two readers. Contribution of the external carotid artery (ECA), internal carotid artery (ICA) and vertebral/basilar artery (VA/BA) was rated as none, <50% or >50%. Correlation of sASL was performed in two patients undergoing DSA.

RESULTS

32 patients (61 ± 13 years) harboring 42 meningiomas could be included. sASL was technically successful in all patients. 19 meningiomas had ICA dominant supply, 19 had ECA dominant supply. One meningioma had mixed supply and in three meningiomas a perfusion signal could not be detected. While exclusive unilateral ECA supply was common (n = 14) and exclusive unilateral ICA was rare (n = 4), mixed supply from multiple vessels (n = 20) was a frequent finding. Interrater agreement was substantial (κ = 0.73). Agreement with DSA was perfect within our predefined categories.

CONCLUSION

sASL is able to identify the presence and extent of the feeding vasculature in intracranial meningiomas.

Arterial Spin Labeling for Glioma Grade Discrimination: Correlations with IDH1 Genotype and 1p/19q Status

Since accurate grading of gliomas has important clinical value, the aim of this study is to evaluate the diagnostic efficacy of perfusion values derived from arterial spin labeling (ASL) to grade gliomas. In addition, the correlation between perfusion and isocitrate dehydrogenase 1 (IDH1) genotypes and chromosome arms 1p and 19q (1p/19q) status of gliomas was assessed. A total of 52 cases of supratentorial gliomas in adults who received ASL imaging were enrolled in this retrospective study. The cerebral blood flow (CBF) images derived from ASL and anatomical maps were normalized to the Montreal Neurological Institute coordinate system and matched. The mean CBF (meanCBF), the maximum CBF (maxCBF), and their relative values (rmeanCBF and rmaxCBF, respectively) were assessed in each case. The tumor grades, IDH1 genotypes, and 1p/19q status were diagnosed according to the 2016 WHO criteria. Receiver operating characteristic curves were performed to assess the efficacy of perfusion parameters for grading. Qualitatively, all gliomas were divided into high- and low-perfusion groups. The crosstabs chi-square test of independence was performed to calculate contingency coefficient (C) and Cramer V coefficient to assess the correlation between perfusion and IDH1 genotypes and 1p/19q status of gliomas. The rmaxCBF showed the best diagnostic efficacy; meanwhile, rmeanCBF had the best specificity for grade discrimination. In astrocytoma, there was a mild correlation between IDH1 genotypes and tumor perfusion with the Cramer's V coefficient of 0.378. There was no significant association between 1p/19q codeletion and perfusion in grade II and III gliomas.