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

Improved quantification of tumor adhesion in meningiomas using MR elastography-based slip interface imaging

Meningiomas, the most prevalent primary benign intracranial tumors, often exhibit complicated levels of adhesion to adjacent normal tissues, significantly influencing resection and causing postoperative complications. Surgery remains the primary therapeutic approach, and when combined with adjuvant radiotherapy, it effectively controls residual tumors and reduces tumor recurrence when complete removal may cause a neurologic deficit. Previous studies have indicated that slip interface imaging (SII) techniques based on MR elastography (MRE) have promise as a method for sensitively determining the presence of tumor-brain adhesion. In this study, we developed and tested an improved algorithm for assessing tumor-brain adhesion, based on recognition of patterns in MRE-derived normalized octahedral shear strain (NOSS) images. The primary goal was to quantify the tumor interfaces at higher risk for adhesion, offering a precise and objective method to assess meningioma adhesions in 52 meningioma patients. We also investigated the predictive value of MRE-assessed tumor adhesion in meningioma recurrence. Our findings highlight the effectiveness of the improved SII technique in distinguishing the adhesion degrees, particularly complete adhesion. Statistical analysis revealed significant differences in adhesion percentages between complete and partial adherent tumors (p = 0.005), and complete and non-adherent tumors (p<0.001). The improved technique demonstrated superior discriminatory ability in identifying tumor adhesion patterns compared to the previously described algorithm, with an AUC of 0.86 vs. 0.72 for distinguishing complete adhesion from others (p = 0.037), and an AUC of 0.72 vs. 0.67 for non-adherent and others. Aggressive tumors exhibiting atypical features showed significantly higher adhesion percentages in recurrence group compared to non-recurrence group (p = 0.042). This study validates the efficacy of the improved SII technique in quantifying meningioma adhesions and demonstrates its potential to affect clinical decision-making. The reliability of the technique, coupled with potential to help predict meningioma recurrence, particularly in aggressive tumor subsets, highlights its promise in guiding treatment strategies.

The value of whole tumor apparent diffusion coefficient histogram parameters in predicting meningiomas progesterone receptor expression

This letter provides a critical assessment of a previous study on the utility of whole tumor apparent diffusion coefficient (ADC) histogram characteristics in predicting meningioma progesterone receptor expression. While acknowledging the benefits of employing classical diffusion-weighted imaging (DWI) for non-invasive tumor evaluation, it also emphasizes significant drawbacks. Advanced imaging techniques such as diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) were not used in the study, which could have provided a more comprehensive understanding of tumor microstructure and heterogeneity. Furthermore, the inclusion of necrotic and cystic areas in ADC analysis may distort results due to their different diffusion properties. While focusing on first-order ADC histogram characteristics is useful, it ignores the potential insights gained from higher-order features and texture analysis. These limitations indicate that future research should combine improved imaging modalities with thorough analytical methodologies to increase the predictive value of imaging biomarkers for meningioma features and progesterone receptor expression.

Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI

Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (p = 0.012), edema volume (p = 0.004), enhancement (p = 0.001), margin (p < 0.001), and tumor-brain interface (p < 0.001). Five radiomics features were selected for model development. The prediction model for radiomics features demonstrated an average validation accuracy of 0.74, while the model for clinical imaging features showed an average validation accuracy of 0.69. When applied to external test data (Hospital 2), the radiomics model achieved an area under the receiver operating characteristics curve (AUC) of 0.72 and accuracy of 0.69, while the clinical imaging model achieved an AUC of 0.82 and accuracy of 0.81. An improved performance was obtained from the model constructed by combining radiomics and clinical imaging features. In the combined model, the AUC and accuracy for meningioma grading were 0.86 and 0.73, respectively. In conclusion, this study demonstrates the potential value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas. The combination of both radiomics and clinical imaging features achieved the highest AUC among the models. Therefore, the combined model of radiomics and clinical imaging features may offer a more effective tool for predicting clinical outcomes in meningioma patients.

MerlinS13 phosphorylation controls meningioma Wnt signaling and magnetic resonance imaging features

Meningiomas are the most common primary intracranial tumors and are associated with inactivation of the tumor suppressor NF2/Merlin, but one-third of meningiomas retain Merlin expression and typically have favorable clinical outcomes. Biochemical mechanisms underlying Merlin-intact meningioma growth are incompletely understood, and non-invasive biomarkers that predict meningioma outcomes and could be used to guide treatment de-escalation or imaging surveillance of Merlin-intact meningiomas are lacking. Here we integrate single-cell RNA sequencing, proximity-labeling proteomic mass spectrometry, mechanistic and functional approaches, and magnetic resonance imaging (MRI) across meningioma cells, xenografts, and human patients to define biochemical mechanisms and an imaging biomarker that distinguish Merlin-intact meningiomas with favorable clinical outcomes from meningiomas with unfavorable clinical outcomes. We find Merlin drives meningioma Wnt signaling and tumor growth through a feed-forward mechanism that requires Merlin dephosphorylation on serine 13 (S13) to attenuate inhibitory interactions with β-catenin and activate the Wnt pathway. Meningioma MRI analyses of xenografts and human patients show Merlin-intact meningiomas with S13 phosphorylation and favorable clinical outcomes are associated with high apparent diffusion coefficient (ADC) on diffusion-weighted imaging. In sum, our results shed light on Merlin posttranslational modifications that regulate meningioma Wnt signaling and tumor growth in tumors without NF2/Merlin inactivation. To translate these findings to clinical practice, we establish a non-invasive imaging biomarker that could be used to guide treatment de-escalation or imaging surveillance for patients with favorable meningiomas.

Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI

OBJECTIVES

Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI.

METHODS

From January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort.

RESULTS

One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong's test showed no statistical difference between the AUC values of the two distinguishing models (p = 0.101).

CONCLUSIONS

An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.

T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

OBJECTIVE

To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.

METHODS

This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.

RESULTS

High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).

CONCLUSION

T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

KEY POINTS

• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

Preoperative Prediction of Intracranial Meningioma Grade Using Conventional CT and MRI

Objective

Preoperative diagnosis of tumor grade can assist in treatment-related decision-making for patients with intracranial meningioma. This study aimed to distinguish between high-grade and low-grade meningiomas using conventional CT and MRI.

Methodology

We retrospectively analyzed 173 consecutive patients with intracranial meningioma (149 low-grade and 24 high-grade tumors) who were treated surgically at the National Hospital Organization Kyushu Medical Center from 2008 to 2020. Clinical and radiological features, including tumor doubling time (Td) and relative growth rate (RGR), were compared between low-grade and high-grade meningiomas.

Results

Multivariate logistic regression analysis showed that symptomatic tumor (p=0.001), non-skull base location (p=0.006), irregular tumor shape (p=0.043), tumor heterogeneity (p=0.025), and peritumoral brain edema (p=0.003) were independent predictors of high-grade meningioma. In 53 patients who underwent surgery because of tumor progression, progression to symptoms (p=0.027), intratumoral heterogeneity (p<0.001), peritumoral brain edema (p=0.001), larger tumor volume (p=0.005), shorter Td (p<0.001), and higher RGR (P<0.001) were significantly associated with high-grade meningioma. Receiver operating characteristics (ROC) curve analysis showed that the optimal Td and annual RGR cut-off values to distinguish high-grade from low-grade meningioma were 460.5 days and 73.2%, respectively (100% sensitivity and 78.6% specificity).

Conclusion

Based on our findings, conventional CT and MRI are useful methods to predict meningioma grades before surgery. High-grade lesions are associated with non-skull base location, irregular tumor shape, intratumoral heterogeneity, and peritumoral brain edema. High-grade meningioma should be suspected in tumors that exhibit Td <460.5 days or annual RGR >73.2% or those that develop intratumoral heterogeneity or surrounding brain edema on surveillance imaging.

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

OBJECTIVE

To compare conventional magnetic resonance imaging (MRI), susceptibility-weighted imaging (SWI), and perfusion-weighted imaging (PWI) characteristics in different grades of trigone meningiomas.

METHODS

Thirty patients with trigone meningiomas were enrolled in this retrospective study. Conventional MRI was performed in all patients; SWI (17 cases), dynamic contrast-enhanced PWI (10 cases), and dynamic susceptibility contrast PWI (6 cases) were performed. Demographics, conventional MRI features, SWI- and PWI-derived parameters were compared between different grades of trigone meningiomas.

RESULTS

On conventional MRI, the irregularity of tumor shape (ρ = 0.497, P = 0.005) and the extent of peritumoral edema (ρ = 0.187, P = 0.022) might help distinguish low-grade and high-grade trigone meningiomas. On multiparametric functional MRI, rTTPmax (1.17 ± 0.06 vs 1.30 ± 0.05, P = 0.048), Kep, Ve, and iAUC demonstrated their potentiality to predict World Health Organization grades I, II, and III trigone meningiomas.

CONCLUSIONS

Conventional MRI combined with dynamic susceptibility contrast and dynamic contrast-enhanced can help predict the World Health Organization grade of trigone meningiomas.

The Current State of Radiomics for Meningiomas: Promises and Challenges

Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive “wait and see” strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.

ADC values of benign and high grade meningiomas and associations with tumor cellularity and proliferation - A systematic review and meta-analysis

INTRODUCTION

The aim of the present systematic review and meta-analysis was to compare the reported ADC values in different meningiomas and to analyze associations between ADC and cell count and proliferation activity in this tumor entity.

METHOD

MEDLINE library and SCOPUS database were screened for papers investigating ADC values of meningiomas up November 2019. The first primary endpoint of the systematic review was the reported ADC mean value of the meningioma groups. The second primary endpoint was the correlation coefficient between ADC values and proliferation index Ki 67 and cellularity.

RESULTS

For the discrimination analysis between benign and high grade meningioma 17 studies were suitable. There were 766 grade I tumors and 289 high grade meningiomas. The calculated mean ADC value of the benign grade I tumors was 0.93 × 10^-3mm²/s [95%-Confidence interval 0.84;1.03] and the mean value of the high-grade tumors was 0.77 × 10^-3mm²/s [95%-Confidence interval 0.73-0.80]. The pooled correlation coefficient between ADC and the proliferation index Ki 67 was r = -0.36 [95% CI -0.43; -0.28]. The pooled correlation coefficient between ADC and cellularity was r = -0.43 [95% CI -0.61; - 0.26].

CONCLUSION

No validated ADC threshold can be recommended for distinguishing benign from high grade meningiomas. Only a moderate inverse correlation was identified between ADC values and tumor microstructure in meningiomas and, therefore, ADC might not accurately enough to predict proliferation potential and cellularity in this entity.

Quantitative Susceptibility Mapping and Vessel Wall Imaging as Screening Tools to Detect Microbleed in Sentinel Headache

Background: MR-quantitative susceptibility mapping (QSM) can identify microbleeds (MBs) in intracranial aneurysm (IA) wall associated with sentinel headache (SH) preceding subarachnoid hemorrhage. However, its use is limited, due to associated skull base bonny and air artifact. MR-vessel wall imaging (VWI) is not limited by such artifact and therefore could be an alternative to QSM. The purpose of this study was to investigate the correlation between QSM and VWI in detecting MBs and to help develop a diagnostic strategy for SH. Methods: We performed a prospective study of subjects with one or more unruptured IAs in our hospital. All subjects underwent evaluation using 3T-MRI for MR angiography (MRA), QSM, and pre- and post-contrast VWI of the IAs. Presence/absence of MBs detected by QSM was correlated with aneurysm wall enhancement (AWE) on VWI. Results: A total of 40 subjects harboring 51 unruptured IAs were enrolled in the study. MBs evident on the QSM sequence was detected in 12 (23.5%) IAs of 11 subjects. All these subjects had a history of severe headache suggestive of SH. AWE was detected in 22 (43.1%) IAs. Using positive QSM as a surrogate for MBs, the sensitivity, specificity, positive predictive value, and negative predictive value of AWE on VWI for detecting MBs were 91.7%, 71.8%, 50%, and 96.6%, respectively. Conclusions: Positive QSM findings strongly suggested the presence of MBs with SH, whereas, the lack of AWE on VWI can rule it out with a probability of 96.6%. If proven in a larger cohort, combining QSM and VWI could be an adjunctive tool to help diagnose SH, especially in cases with negative or non-diagnostic CT and lumbar puncture.