Recurrence of atypical and anaplastic intracranial Meningiomas: A meta-analysis of risk factors.

BACKGROUND: The predictive role of multiple risk factors for intracranial atypical and anaplastic meningioma recurrence is convoluted. This meta-analysis assessed the predictive value of selected factors for recurrence in these Meningiomas. METHODS: Studies encompassing risk factor data including gross total resection (GTR), subtotal resection (STR), post-op radiotherapy, Ki-67 % index >3 %, and location were searched for in PubMed, Embase, and Web of Science, and thereafter analyzed using robust Bayesian meta-analysis. RESULTS: Eighteen observational studies involving 1589 patients met inclusion criteria for analysis. GTR was identified as a good prognostic factor for recurrence (OR = 0.212; 95 % CI (-1.972, -1.002); heterogeneity BF=0.702), and STR had a significantly higher risk of recurrence (OR = 4.43; 95 % CI 0.658-2.011; heterogeneity BF=0.724). Post-operative radiotherapy did not statistically significantly affect the recurrence process (OR = 1.02; 95 % CI (-1.848, 0.626); heterogeneity (BF=1.034)). Ki67 % index >3 % had an augmented chance of recurrence (OR = 2.38; 95 % CI (-0.220, 2.355); heterogeneity (BF=1.162)). A meta-regression analysis showed that WHO grade III Meningiomas had a higher chance of recurring than grade II Meningiomas. CONCLUSION: Among the selected factors, STR and Ki67 % index > 3 % were associated with a higher risk of recurrence, with post-operative radiotherapy making no difference. GTR appeared to inversely impact recurrence. Compared to grade II, grade III Meningiomas had higher odds of recurring.

Full Endoscopic Transcranial Resection of Meningiomas.

OBJECTIVE: Tumors of the skull base can be accessed through different routes. Recent advantages in minimally invasive techniques have shown that very different routes can be applied for optimal tumor resection depending on the technical equipment, the surgeon's preference, and the individual anatomy of the pathology. Here, the authors present their technique for pure endoscopic transcranial tumor resection in meningiomas. METHODS: Out of the cases of the Department of Neurosurgery, Homburg Saar Germany of the last 10 years, all endoscopic procedures for meningiomas were analyzed. Particular attention was given to evaluating the peculiarities of those meningiomas that were treated purely endoscopically. RESULTS: While the endoscope was used in a large number of skull base meningiomas in endonasal approaches or for endoscopic inspection in transcranial skull base surgery, only a small number of meningiomas was selected for a purely endoscopically performed resection. The characteristics of these cases were rather a small lesion, straight access, and a keyhole position of the craniotomy. A complete resection of the tumor was achieved in all cases. Conversion to the microscope was not necessary in any case. There were no technical issues or complications associated with a fully endoscopic resection. DISCUSSION: The endoscope is a valuable tool for visualization in meningioma surgery. In most cases, it is applied for an endonasal route or for endoscopic inspection in transcranial microsurgical cases. However, small to medium-sized meningiomas that can be accessed through the keyhole approach are good candidates for pure endoscopic resection. Because of the very high magnification and the minimally invasive nature of this approach, it should be considered more frequently in suitable lesions.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

3D Printing for Customized Bone Reconstruction in Spheno-Orbital Meningiomas: A Systematic Literature Review and Institutional Experience.

Background: The treatment of spheno-orbital meningiomas (SOMs) requires extensive bone resections, creating significant defects in a complex geometrical space. Bone reconstruction represents a fundamental step that optimizes long-term aesthetic and functional outcomes. In recent years, 3D printing technology has also been exploited for complex skull base reconstructions, but reports remain scarce. Methods: We retrospectively analyzed four consecutive patients who underwent SOM resection and one-step 3D PEEK customized reconstruction from 2019 to 2023. A systematic review of 3D printing customized implants for SOM was then performed. Results: All patients underwent a frontotemporal craniotomy, removal of SOM, and reconstruction of the superolateral orbital wall and pterional region. The aesthetic outcome was extremely satisfactory in all cases. No orbital implant malposition or infectious complications were documented. Eleven papers were included in the literature review, describing 27 patients. Most (23) patients underwent a single-stage reconstruction; in three cases, the implant was positioned to correct postoperative delayed enophthalmos. Porous titanium was the most used material (16 patients), while PEEK was used in three cases. Prosthesis malposition was described in two (7.4%) patients. Conclusions: Single-step reconstruction with a personalized 3D PEEK prosthesis represents a valid reconstruction technique for the treatment of SOMs with good aesthetic outcomes.

Controversies in neuro-oncology: Focal proton versus photon radiation therapy for adult brain tumors.

Radiation therapy (RT) plays a fundamental role in the treatment of malignant and benign brain tumors. Current state-of-the-art photon- and proton-based RT combines more conformal dose distribution of target volumes and accurate dose delivery while limiting the adverse radiation effects. PubMed was systematically searched from from 2000 to October 2023 to identify studies reporting outcomes related to treatment of central nervous system (CNS)/skull base tumors with PT in adults. Several studies have demonstrated that proton therapy (PT) provides a reduced dose to healthy brain parenchyma compared with photon-based (xRT) radiation techniques. However, whether dosimetric advantages translate into superior clinical outcomes for different adult brain tumors remains an open question. This review aims at critically reviewing the recent studies on PT in adult patients with brain tumors, including glioma, meningiomas, and chordomas, to explore its potential benefits compared with xRT.

Neuroanatomical insights into neuro-ophthalmic presentations of skull base meningiomas: Pathways to precision medicine - A meta-analysis.

Background: Skull base meningiomas are intricately related to neuro-ophthalmic functions and have been related to direct influences on both the afferent and efferent visual pathways due to close contacts with crucial neurovascular structures. The present article reviews the neuro-ophthalmic presentations of these tumors, necessitating the need for precise anatomic information for the delivery of customized diagnostic and therapeutic approaches. Methods: A literature review was conducted using PubMed and Scopus, focusing on terms related to skull base meningiomas and their neuro-ophthalmic impacts. The review included recent and seminal articles to assess advances in understanding and managing these tumors from an anatomical perspective. Results: The findings underscore the diversity in neuro-ophthalmic manifestations based on the meningioma's location, affecting visual pathways differently. For instance, meningiomas in the optic nerve sheath typically lead to isolated optic neuropathy and are often managed with radiation therapy. In contrast, those extending from the sphenoid wing require more aggressive approaches like open surgery. This review highlights how the tumor's location dictates the choice of treatment, ranging from conservative management to multidisciplinary surgical interventions. Conclusion: Proper recognition of the sites of skull base meningiomas results in a treatment tailored to provide therapy aimed explicitly at neuro-ophthalmic outcomes related to the site of the tumors. This approach will not only help to treat effectively but also avoid a wide range of complications and, in turn, increase the effectiveness of the treatment results that a patient will receive. Future studies should aim to refine these anatomical insights toward further advancements in modes of treatment.

Exploring machine learning applications in Meningioma Research (2004-2023).

Objective: This study aims to examine the trends in machine learning application to meningiomas between 2004 and 2023. Methods: Publication data were extracted from the Science Citation Index Expanded (SCI-E) within the Web of Science Core Collection (WOSCC). Using CiteSpace 6.2.R6, a comprehensive analysis of publications, authors, cited authors, countries, institutions, cited journals, references, and keywords was conducted on December 1, 2023. Results: The analysis included a total of 342 articles. Prior to 2007, no publications existed in this field, and the number remained modest until 2017. A significant increase occurred in publications from 2018 onwards. The majority of the top 10 authors hailed from Germany and China, with the USA also exerting substantial international influence, particularly in academic institutions. Journals from the IEEE series contributed significantly to the publications. "Deep learning," "brain tumor," and "classification" emerged as the primary keywords of focus among researchers. The developmental pattern in this field primarily involved a combination of interdisciplinary integration and the refinement of major disciplinary branches. Conclusion: Machine learning has demonstrated significant value in predicting early meningiomas and tailoring treatment plans. Key research focuses involve optimizing detection indicators and selecting superior machine learning algorithms. Future efforts should aim to develop high-performance algorithms to drive further innovation in this field.

Secretory Meningioma of the Right Frontal Lobe: Clinical Presentation and Pathological Insights.

This article presents a case study of a rare convexity meningioma located in the frontal lobe of the right cerebellar hemisphere. Meningiomas comprise a substantial part of central nervous system neoplasms and are classified into benign, atypical, or anaplastic categories, each encompassing a variety of histological subtypes, among which the secretory meningioma is notably rare. A 77-year-old male presented with a clinical history of headache, impaired memory functions, an initial form of apathetic-abulic syndrome, and a single seizure, which were considered to be indicative of epileptic symptoms that had been present for several weeks. The imaging studies conducted showed a convexity tumor characterized by a rounded morphology and homogeneous contrast enhancement, positioned adjacent to the frontal lobe's cortical surface. This clinical report details the pathology of a secretory type of meningioma, which is distinguished by the atypical epithelial differentiation of meningothelial cells, resulting in hyaline fiber production. The neoplasm's anatomical accessibility permitted successful surgical resection. The tumor's position was appropriate for surgical removal, and the histological variant, along with the patient's favorable clinical course, is of particular scientific interest.

Surgical outcomes of trigonal intraventricular meningiomas: a single-centre study.

Trigonal meningiomas are rare intraventricular tumours that present a surgical challenge. There is no consensus on the optimal surgical approach to these lesions, though the transtemporal and transparietal approaches are most frequently employed. We aimed to examine the approach-related morbidity and surgical nuances in treating trigonal meningiomas. This retrospective review assimilated data from 64 trigonal meningiomas operated over 15 years. Details of clinicoradiological presentation, surgical approach and intraoperative impression, pathology and incidence of various postoperative deficits were recorded. In our study, Trigonal meningiomas most frequently presented with headache and visual deterioration. The median volume of tumours was 63.6cc. Thirty-one meningiomas each (48.4%) were WHO Grade 1 and WHO Grade 2, while 2 were WHO Grade 3. The most frequent approach employed was transtemporal (38 patients, 59.4%), followed by transparietal (22 patients, 34.4%). After surgery features of raised ICP and altered mental status resolved in all patients, while contralateral limb weakness resolved in 80%, aphasia in 60%, seizures in 70%, and vision loss in 46.2%. Eighteen patients (28.13%) developed transient postoperative neurological deficits, with one patient (1.5%) developing permanent morbidity. Surgery for IVMs results in rapid improvement of neurological status, though visual outcomes are poorer in patients with low vision prior to surgery, longer duration of complaints and optic atrophy. The new postoperative deficits in some patients tend to improve on follow up. Transtemporal and transparietal approaches may be employed, based on multiple factors like tumour extension, loculation of temporal horn, size of lesion with no significant difference in their safety profile.

Atypical and anaplastic meningiomas in the later decades of life: A national cancer database analysis.

PURPOSE: We conducted a National Cancer Database (NCDB) study to investigate the epidemiological characteristics and identify predictors of outcomes associated with geriatric meningiomas. METHODS: The NCDB was queried for adults aged 60-89 years diagnosed between 2010 and 2017 with grade 2 and 3 meningiomas. The patients were classified into three age groups based on their age: 60-69 (hexagenarians), 70-79 (septuagenarians), and 80-89 (octogenarians). The log-rank test was utilized to compare the differences in overall survival (OS). Univariate and multivariate Cox proportional hazards regressions were used to evaluate the mortality risk associated with various patient and disease parameters. RESULTS: A total of 6585 patients were identified. Hexagenerians were the most common age group (49.8%), with the majority of meningiomas being classified as grade 2 (89.5%). The incidence of high-grade meningiomas increased in all age groups during the study period. Advanced age, male sex, black race, lower socioeconomic status, Charlson-Deyo score ≥ 2, and higher tumor grade were independent factors of poor survival. Among the modes of treatment, the extent of surgical resection, adjuvant radiotherapy, and treatment at a noncommunity cancer program were linked with better outcomes. CONCLUSION: In geriatric patients with high-grade meningiomas, the greater extent of surgical resection and radiotherapy are associated with improved survival. However, the management and outcome of geriatric patients with higher-grade meningiomas are also associated with several socioeconomic factors.

Spheno-Orbital Meningiomas: The Rationale behind the Decision-Making Process of Treatment Strategy.

Surgery stands as the primary treatment for spheno-orbital meningiomas, following a symptoms-oriented approach. We discussed the decision-making process behind surgical strategies through a review of medical records from 80 patients who underwent surgical resection at the University of Naples Federico II. Different surgical approaches were employed based on the tumor's location relative to the optic nerve's long axis, categorized into lateral (type I), medial (type II), and diffuse (type III). We examined clinical, neuroradiological, surgical, pathological, and outcome factors. Proptosis emerged as the most frequent symptom (97%), followed by visual impairment (59%) and ocular motility issues (35%). Type I represented 20%, type II 43%, and type III 17%. Growth primarily affected the optic canal (74%), superior orbital fissure (65%), anterior clinoid (60%), and orbital apex (59%). The resection outcomes varied, with Simpson grades I and II achieved in all type I cases, 67.5% of type II, and 18% of type III. Recurrence rates were highest in type II (41.8%) and type III (59%). Improvement was notable in proptosis (68%) and visual function (51%, predominantly type I). Surgery for spheno-orbital meningiomas should be tailored to each patient, considering individual characteristics and tumor features to improve quality of life by addressing primary symptoms like proptosis and visual deficits.

Topographic Patterns of Intracranial Meningioma Recurrences-Systematic Review with Clinical Implication.

BACKGROUND: While several risk factors for recurrences have been defined, the topographic pattern of meningioma recurrences after surgical resection has been scarcely investigated. The possibility of theoretically predicting the site of recurrence not only allows us to better understand the pathogenetic bases of the disease and consequently to drive the development of new targeted therapies, but also guides the decision-making process for treatment strategies and tailored follow-ups to decrease/prevent recurrence. METHODS: The authors performed a comprehensive and detailed systematic literature review of the EMBASE and MEDLINE electronic online databases regarding the topographic pattern of recurrence after surgical treatment for intracranial meningiomas. Demographics and histopathological, neuroradiological and treatment data, pertinent to the topography of recurrences, as well as time to recurrences, were extracted and analyzed. RESULTS: Four studies, including 164 cases of recurrences according to the inclusion criteria, were identified. All studies consider the possibility of recurrence at the previous dural site; three out of four, which are the most recent, consider 1 cm outside the previous dural margin to be the main limit to distinguish recurrences closer to the previous site from those more distant. Recurrences mainly occur within or close to the surgical bed; higher values of proliferation index are associated with recurrences close to the original site rather than within it. CONCLUSIONS: Further studies, including genomic characterization of different patterns of recurrence, will better clarify the main features affecting the topography of recurrences. A comparison between topographic classifications of intracranial meningioma recurrences after surgery and after radiation treatment could provide further interesting information.

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.

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.

OBJECTIVE: To develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM). METHODS: In this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis. RESULTS: Among the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819-0.905) in the training cohort, 0.834 (95% CI, 0.780-0.908) in the internal validation cohort and 0.867 (95% CI, 0.785-0.950) in the external validation cohort, respectively. CONCLUSIONS: The nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Background: Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. Methods: We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Results: Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. Conclusions: The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Middle third falcine meningiomas-surgical nuances for cortical venous preservation.

PURPOSE: To improve postoperative outcome in middle third falcine meningiomas by cortical venous preservation. BACKGROUND: Falcine meningiomas arise from the falx and do not involve the superior sagittal sinus (SSS). Their complete resection is often associated with the risk of venous infarction in the eloquent cortex due to overlying superficial cortical veins on the tumors. METHOD: We report one case of middle third falcine meningioma, where we used the posterior interhemispheric corridor for tumor approach. CONCLUSION: Use of the posterior interhemispheric approach, carefully raised bone flap, along with sharp dissection and vein reinforcement using fibrin glue can help to preserve the cortical veins while resecting the falcine meningiomas.

Hsa_circ_0004872 mitigates proliferation, metastasis and immune escape of meningioma cells by suppressing PD-L1.

Meningioma is a prevalent intracranial malignancy known for its aggressive growth. Circular RNAs (circRNAs) play a crucial role in the development of various cancers. However, their involvement in meningioma remains understudied. This study aimed to investigate the function and underlying mechanism of hsa_circ_0004872 in meningioma. The molecular expression of hsa_circ_0004872, PD-L1 and EIF4A3 was identified by RT-qPCR and/or western blot assays. Cell viability, migration, and invasion were assessed through CCK-8 and Transwell assays, respectively. Cytotoxicity was determined using an LDH assay, and cell apoptosis was monitored by flow cytometry. The RNA and protein interactions were assessed through RNA-protein immunoprecipitation (RIP) and RNA pull down analyses. Our findings revealed that hsa_circ_0004872 expression was significantly downregulated in both meningioma tissue samples and cells. Overexpression of hsa_circ_0004872 inhibited the proliferation, metastasis, and immune escape of meningioma cells, as well as enhanced the cytotoxicity of CD8+ T cells by suppressing PD-L1. Furthermore, hsa_circ_0004872 directly interacted with EIF4A3, leading to the degradation of PD-L1 mRNA. Finally, inhibiting EIF4A3 improved the proliferation, metastasis, and immune escape of meningioma cells, as well as the cytotoxicity of CD8+ T cells. Our study demonstrated that hsa_circ_0004872 mitigated the proliferation, metastasis,and immune escape of meningioma cells by targeting the EIF4A3/PD-L1 axis. These findings suggested that hsa_circ_0004872 and EIF4A3 might serve as promising biological markers and therapeutic targets for meningioma treatment.

Evolving concepts in meningioma management in the era of genomics.

Meningioma is the most common type of primary brain tumor. Surgical resection followed by surveillance is the first-line treatment for the majority of symptomatic meningiomas; however, recent advances in molecular sequencing, DNA methylation, proteomics, and single-cell sequencing provide insights into further characterizing this heterogeneous group of tumors with a wide range of prognoses. A subset of these tumors are highly aggressive and cause severe morbidity and mortality. Therefore, identifying those individuals with a poor prognosis and intervening are critical. This review aims to help readers interpret the molecular profiling of meningiomas to identify patients with worse prognoses and guide the management and strategy for surveillance.

MicroRNAs in meningiomas: Potential biomarkers and therapeutic targets.

Meningiomas, characterized primarily as benign intracranial or spinal tumors, present distinctive challenges due to their variable clinical behavior, with certain cases exhibiting aggressive features linked to elevated morbidity and mortality. Despite their prevalence, the underlying molecular mechanisms governing the initiation and progression of meningiomas remain insufficiently understood. MicroRNAs (miRNAs), small endogenous non-coding RNAs orchestrating post-transcriptional gene expression, have garnered substantial attention in this context. They emerge as pivotal biomarkers and potential therapeutic targets, offering innovative avenues for managing meningiomas. Recent research delves into the intricate mechanisms by which miRNAs contribute to meningioma pathogenesis, unraveling the molecular complexities of this enigmatic tumor. Meningiomas, originating from arachnoid meningothelial cells and known for their gradual growth, constitute a significant portion of intracranial tumors. The clinical challenge lies in comprehending their progression, particularly factors associated with brain invasion and heightened recurrence rates, which remain elusive. This comprehensive review underscores the pivotal role of miRNAs, accentuating their potential to advance our comprehension of meningioma biology. Furthermore, it suggests promising directions for developing diagnostic biomarkers and therapeutic interventions, holding the promise of markedly improved patient outcomes in the face of this intricate and variable disease.

A comprehensive histomolecular characterization of meningioangiomatosis: Further evidence for a precursor neoplastic lesion.

Meningioangiomatosis (MAM) remains a poorly understood lesion responsible for epileptic disease. In the past, MAM was primarily described in the context of neurofibromatosis type 2 before being mainly reported sporadically. Moreover, the malformative or tumoral nature is still debated. Because a subset of MAM are associated with meningiomas, some authors argue that MAM corresponds to an infiltration pattern of these tumors. For these reasons, MAM has not been added to the World Health Organization (WHO) Classification of Central Nervous System Tumors as a specific entity. In the present study, we characterized a series of pure MAM (n = 7) and MAM associated with meningiomas (n = 4) using histopathology, immunohistochemistry, genetic (fluorescent in situ and DNA sequencing analyses), and epigenetic (DNA-methylation profiling) data. We evidenced two distinct morphological patterns: MAM with a fibroblastic-like pattern having few lesional cells, and MAM with a more cellular pattern. A subset was associated with the genetic alterations previously reported in meningiomas (such as a KMT2C mutation and a hemizygous deletion of chromosome 22q including the NF2 gene). The DNA-methylation profile, using a t-distributed stochastic neighbor embedding analysis, evidenced that MAM (pure or associated with meningiomas) clustered in a separate group from pediatric meningiomas. The present results seem to suggest that MAM represents a neoplastic lesion and encourage the further study of similar additional series so that it may be included in a future WHO classification.