Peritumoral edema correlates with mutational burden in meningiomas

MRI morphological features combined with apparent diffusion coefficient can predict brain invasion in meningioma

OBJECTIVES

Accurately predicting meningioma brain invasion preoperatively helps to select the appropriate surgical approach and predict prognosis, but there are few imaging features that are sufficient for discriminating it alone. We investigate the joint MR imaging features and apparent diffusion coefficient (ADC) to predict the risk of brain invasion of meningiomas preoperatively.

METHODS

In this retrospective study, 143 patients (invasion group:51, non-invasion group: 92) diagnosed with meningioma by histopathology were included. The maximum (ADCmax), minimum (ADCmin) and mean (ADCmean) values of ADC and the mean ADC values of a comparative ROI in the normal appearing white matter (ADCNAWM) were calculated. Differences between clinical features, MRI morphological features, and all ADC values were assessed by Pearson's chi-square test and Kruskal-Wallis rank-sum test. Stepwise logistic regression analysis was used to select the optimal features and construct a prediction model. Furthermore, A nomogram was used to predict the risk of brain invasion, and a decision curve was used to verify the clinical utility of the nomogram.

RESULTS

According to stepwise logistic regression analysis, we found that sex, maximum diameter, peritumoral edema and ADCmin were closely related to brain invasion in meningioma. The model of the above four variables has the optimal discriminative ability to predict brain invasion, with an AUC of 0.924 (95 % CI, 0.879-0.969) and a sensitivity of 92.2 % (95 % CI, 74.5%-98.0 %).

CONCLUSIONS

Combining clinical features, MRI morphological characteristics and ADCmin, the model exhibits excellent discriminatory ability and high sensitivity, which can be used for predicting the risk of brain invasion of meningiomas.

Analysis of prognostic factors and the role of epilepsy in neurosurgical patients with brain metastases

Background

Brain metastases (BMs) represent the most frequent brain tumors in adults. The identification of key prognostic factors is essential for choosing the therapeutic strategy tailored to each patient. Epilepsy can precede several months of other clinical presentations of BMs. This work aimed to study the impact of epilepsy and other prognostic factors on BMs patients' survival.

Methods

This retrospective study included 51 patients diagnosed with BMs and who underwent neurosurgery between 2010 and 2021. The impact of BM features and patient's clinical characteristics on the overall survival (OS) was analyzed through uni- and multivariate analysis.

Results

The average OS was 25.98 months and differed according to the histology of the primary tumor. The primary tumor localization and the presence of extracranial metastases had a statistically significant impact on the OS, and patients with single BM showed a superior OS to those with multifocal lesions. The localization of BMs in the temporal lobe correlated with the highest OS. The OS was significantly higher in patients who presented seizures in their clinical onset and in those who had better post-surgical Karnofsky performance status, no post-surgical complications, and who underwent post-surgical treatment.

Conclusion

Our study has highlighted prognostically favorable patient and tumor factors. Among those, a clinical onset with epileptic seizures can help identify brain metastasis hitherto silent. This could lead to immediate diagnostic-therapeutic interventions with more aggressive therapies after appropriate multidisciplinary evaluation.

Uncovering the molecular landscape of meningiomas and the impact of perioperative steroids on patient survival

BACKGROUND

The current literature on meningioma reveals a gap in knowledge regarding the impact of genetic factors on patient survival. Furthermore, there is a lack of data on the relationship between the perioperative use of corticosteroids and patient survival in meningioma patients. Our study aims to overcome these gaps by investigating the correlation between genetic factors and overall survival and the effect of postoperative corticosteroids and other clinical characteristics on patient outcomes in meningioma patients.

METHODS

A retrospective analysis of the medical records of 85 newly diagnosed meningioma patients treated from 2016 to 2017 with follow-up until December 2022 was performed.

RESULTS

NF2 mutations occurred in 60% of tumors, AKT1 mutations in 8.2%, and TRAF7 mutations in 3.6%. Most tumors in the parasagittal region had the NF2 mutation. On the other hand, almost all tumors in the sphenoid ridge area did not have the NF2 mutation. AKT-1-mutated meningiomas had more frequent peritumoral edema. Patients who received steroids perioperatively had worse overall survival (OS) than those without steroids (p = 0.034). Moreover, preoperative peri-meningioma edema also was associated with worse OS (p < 0.003). Contrarily, NF2 mutations did not influence survival.

CONCLUSIONS

The combination of clinical, pathomorphological, and genetic data allows us to characterize the tumor better and assess its prognosis. Corticosteroids perioperatively and peri-meningioma edema were associated with shorter OS, according to our study. Glucocorticoids should be used judiciously for the shortest time required to achieve symptomatic relief.

Imaging biomarkers associated with extra-axial intracranial tumors: a systematic review

Extra-axial brain tumors are extra-cerebral tumors and are usually benign. The choice of treatment for extra-axial tumors is often dependent on the growth of the tumor, and imaging plays a significant role in monitoring growth and clinical decision-making. This motivates the investigation of imaging biomarkers for these tumors that may be incorporated into clinical workflows to inform treatment decisions. The databases from Pubmed, Web of Science, Embase, and Medline were searched from 1 January 2000 to 7 March 2022, to systematically identify relevant publications in this area. All studies that used an imaging tool and found an association with a growth-related factor, including molecular markers, grade, survival, growth/progression, recurrence, and treatment outcomes, were included in this review. We included 42 studies, comprising 22 studies (50%) of patients with meningioma; 17 studies (38.6%) of patients with pituitary tumors; three studies (6.8%) of patients with vestibular schwannomas; and two studies (4.5%) of patients with solitary fibrous tumors. The included studies were explicitly and narratively analyzed according to tumor type and imaging tool. The risk of bias and concerns regarding applicability were assessed using QUADAS-2. Most studies (41/44) used statistics-based analysis methods, and a small number of studies (3/44) used machine learning. Our review highlights an opportunity for future work to focus on machine learning-based deep feature identification as biomarkers, combining various feature classes such as size, shape, and intensity. Systematic Review Registration: PROSPERO, CRD42022306922.

The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma

Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set.

Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area

PURPOSE

To investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.

METHODS

Consecutive patients diagnosed with meningioma by surgery (77 low-grade and 28 non-low-grade meningiomas) underwent preoperative magnetic resonance imaging including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI). Manual segmentation of the tumor area was performed to extract texture features. Segmentation of the peritumoral area was performed for peritumoral high-signal intensity (PHSI) on T2WI. Principal component analysis was performed to fuse the texture features to principal components (PCs), and PCs of each sequence of the tumor and peritumoral areas were compared between low- and non-low-grade meningiomas. Only PCs with statistical significance were used for the model construction using a support vector machine algorithm. k-fold cross-validation with receiver operating characteristic curve analysis was used to evaluate diagnostic performance.

RESULTS

Two, one, and three PCs of T1WI, apparent diffusion coefficient (ADC), and CE-T1WI, respectively, for the tumor area, were significantly different between low- and non-low-grade meningiomas, while PCs of T2WI for the tumor area and PCs for the peritumoral area were not. No significant differences were observed in PHSI. Among models of sequence combination, the model with PCs of ADC and CE-T1WI for the tumor area showed the highest area under the curve (0.84).

CONCLUSION

The model with PCs of ADC and CE-T1WI for the tumor area showed the highest diagnostic performance for differentiating between low- and non-low-grade meningiomas. Neither PHSI nor PCs in the peritumoral area showed added value.

NF2 mutations are associated with resistance to radiation therapy for grade 2 and grade 3 recurrent meningiomas

PURPOSE

High grade meningiomas have a prognosis characterized by elevated recurrence rates and radiation resistance. Recent work has highlighted the importance of genomics in meningioma prognostication. This study aimed to assess the relationship between the most common meningioma genomic alteration (NF2) and response to postoperative radiation therapy (RT).

METHODS

From an institutional tissue bank, grade 2 and 3 recurrent meningiomas with both > 30 days of post-surgical follow-up and linked targeted next-generation sequencing were identified. Time to radiographic recurrence was determined with retrospective review. The adjusted hazard of recurrence was estimated using Cox-regression for patients treated with postoperative RT stratified by NF2 mutational status.

RESULTS

Of 53 atypical and anaplastic meningiomas (29 NF2 wild-type, 24 NF2 mutant), 19 patients underwent postoperative RT. When stratified by NF2 wild-type, postoperative RT in NF2 wild-type patients was associated with a 78% reduction in the risk of recurrence (HR 0.216; 95%CI 0.068-0.682; p = 0.009). When stratified by NF2 mutation, there was a non-significant increase in the risk of recurrence for NF2 mutant patients who received postoperative RT compared to those who did not (HR 2.43; 95%CI 0.88-6.73, p = 0.087).

CONCLUSION

This study demonstrated a protective effect of postoperative RT in NF2 wild-type patients with recurrent high grade meningiomas. Further, postoperative RT may be associated with no improvement and perhaps an accelerated time to recurrence in NF2 mutant tumors. These differences in recurrence rates provide evidence that NF2 may be a valuable prognostic marker in treatment decisions regarding postoperative RT. Further prospective studies are needed to validate this relationship.

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.

Synthesizing Molecular and Immune Characteristics to Move Beyond WHO Grade in Meningiomas: A Focused Review

No portion of this manuscript has previously been presented. Meningiomas, the most common primary intracranial tumors, are histologically categorized by the World Health Organization (WHO) grading system. While higher WHO grade is generally associated with poor clinical outcomes, a significant subset of grade I tumors recur or progress, indicating a need for more reliable models of meningioma behavior. Several groups have developed risk scores based on molecular or immunologic characteristics. These classification schemes show promise, with several models preliminarily demonstrating similar or superior accuracy to WHO grading. Improved understanding of immune system recognition and targeting of meningioma subtypes is necessary to advance the predictive power, as well as develop new therapies. Here, we characterize meningioma molecular drivers, predictive of recurrence and progression, and describe specific aspects of the immune response to meningiomas while highlighting critical questions and ongoing research. Relevant manuscripts of interest were identified using a systematic approach and synthesized into this focused review. Finally, we summarize the ongoing and completed clinical trials for immunotherapy in meningiomas and offer perspective on future directions.

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.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

Efficacy of Bevacizumab in High-Grade Meningiomas: A Retrospective Clinical Study

OBJECTIVE

We investigated the role of bevacizumab (BV) in high-grade meningiomas (HGMs) by retrospective analysis.

METHODS

We retrospectively analyzed the clinical data of 139 patients with HGMs. The chi-square test was used to compare progression-free survival (PFS) and overall survival (OS) between patients who received BV and those who did not. According to whether they received BV treatment, we divided the patients into the BV group and non-BV group, and the effect of BV on PFS and OS was compared. In addition, we compared Karnofsky performance status (KPS) and steroid doses between the BV and non-BV groups.

RESULTS

There were statistically differences in PFS and OS between the BV and non-BV groups at 12 and 36 months after surgery (P<0.05). However, there was no significant difference in PFS and OS between the two groups at 60 months postoperatively (P>0.05). Using survival curves drawn by the Kaplan Meier method, we found that the PFS and OS of the BV group were greater than those of the non-BV group, and the difference was statistically significant (P<0.05).

CONCLUSION

BV could improve PFS and OS at 12 and 36 months after surgery in patients with HGMs. In addition, BV was associated with lower preoperative steroid use.

Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study

Introduction

meningioma is the most common intracranial tumor. CT scan is a common method for diagnosis. WHO classified meningioma into 3 histological grades? This study aims to evaluate the relation of different meningioma signs on CT and tumor distribution regard to WHO histological types.

Methods

In this single-center observational retrospective study, authors reviewed data of 75 meningioma patients confirmed by the WHO histological grades (WHO I/II/III) which were underwent CT scans from January 1, 2005 to December 30, 2019 at a teaching hospital, in XXXX. Data collected using patients medical records. Data were analyzed by SPSS 20 and P less than 0.05 was assumed as significant.

Result

Our study confirmed that only edema (P = 0.005) and heterogeneity (P = 0.014) had a significant association with malignant histological types. Other signs were not statistically different among WHO histology types (p > 0.05). On the subject of tumor location, atypical/malignant meningioma was significantly more common in parasagittal (P = 0.031) and front-parietal (P = 0.035) regions.

Discussion

meningiomas with Edema, heterogeneity on CT, and tumors located in parasagittal and frontoparietal regions are related to malignant histology and should be evaluated and treated more precisely.

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CT scan is a common method for diagnosis of meningioma.

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In This retrospective study 75 meningioma patients' data reviewed.

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CT scan signs of edema and heterogeneity had a significant association with malignant histological types.