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Recent technologic advancements have facilitated the use of hypofractionated Gamma Knife-based radiosurgery (HF-GKRS) to treat large lesions or those in eloquent areas. This study aimed to analyze the preliminary results of HF-GKRS for these meningiomas, and to determine its effectiveness and safety. This single-center retrospective study analyzed data of patients who underwent HF-GKRS for large meningiomas or those in eloquent areas with > 6 months of follow-up. The primary outcome was progression-free survival (PFS). The secondary outcomes were neurological deterioration, post-treatment T2 signal changes following HF-GKRS, and tumor volume changes. Volumetric analysis of the tumors after treatment was also performed to assess changes in tumor size after HF-GKRS. Overall, 24 patients with a median follow-up period of 22 months (range: 6–49 months) were included. Among them, 18 (75%) patients had tumors in close proximity to the optic pathway, and 15 (63%) patients had large lesions (> 10 cm3). The cumulative 1- and 3-year PFS rates were 100% and 92%, respectively. The cumulative 3-year rate of adverse radiation effects was 9%. Overall, 12 patients (50%) showed tumor reduction, with a median tumor reduction rate of 45% (range: 25–58%). Our preliminary results revealed that HF-GKRS for large meningiomas or those in eloquent areas is safe and effective, with satisfactory short- and mid-term PFS and low adverse radiation effects. Further research with more patients and longer follow-up periods is required. [ABSTRACT FROM AUTHOR]

Meningioma is the most prevalent primary intracranial tumor, with approximately half of patients harboring NF2 alteration. The rationale behind the presence of NF2 alteration in meningiomas and its absence in non-nerve system tumors remains elusive. Therefore, meningiomas and several non-nerve system tumor types were analyzed using KEGG analysis and CRISPR/Cas 9 technology to determine the role of NF2 in regulating tissue specificity. Moreover, the different regulatory patterns of Ca2+ and calcium/calmodulin-dependent protein kinase II alpha (CAMK2A) that play a decisive role in NF2 tissue-specific regulation were deciphered. Our results revealed that NF2 has a positive correlation in CAMK2A expression in both meningiomas and normal nervous system tissues but not in non-nervous system tumors and tissues, implying NF2 tissue-specificity is mediated by CAMK2A-related pathways. Thus, targeting CAMK2A may represent a promising strategy for drug screening and the development of therapeutics for NF2-associated meningiomas and other nervous system tumors. [ABSTRACT FROM AUTHOR]

Objective: Radiological prediction of Ki-67 plays a crucial role in determining the clinical outcomes of meningioma patients. The aim of this review is to comprehensively review the literature to synthesize evidence on how accurate radiological prediction of the Ki-67 proliferation index is to determine the Ki-67 index's association with clinical outcomes of meningioma. Materials and methods: A narrative synthesis systematic review followed PRISMA guidelines. The literature was searched using database-specific, relevant keywords, text words, and MeSH terms (controlled vocabulary) such as "Meningioma," "ki-67," "Ki 67 proliferation index," "proliferation index," "Radiomics," and "clinical outcomes" on electronic databases PubMed, Web of Science, and Google Scholar from 2014 to April 2024. Results: Out of 218 publications identified initially, only 03 moderate-quality studies were included in this paper after methodological quality assessment using the Newcastle–Ottawa scale. Discussion: The evidence synthesis by systematic review emphasizes the importance of radiographic approaches in predicting the Ki-67 proliferation index and their consequences for prognosis and treatment results in meningioma patients. Various radiological techniques, such as pre-operative MRI with clinical and radiomic analysis and machine learning algorithms, show promise for accurately predicting Ki-67 status, with significant associations between radiological features and clinical outcomes such as recurrence, survival, and progression-free survival. The radiomics models were compared and found that Lasso with LDA radiomic models outperformed other radiomics models in terms of accuracy, sensitivity, specificity, and predictive power. Lasso with LDA model also showed significant improvements in performance when the clinical datasets of meningiomas patients were combined to predict ki-67 levels. However, the comparison to histological evaluation emphasizes the necessity for additional validation and standardization. While these approaches provide non-invasive and possibly time-saving alternatives, drawbacks such as inherent biases, methodological limits, and practical challenges with automated selection procedures highlight future research and development topics. [ABSTRACT FROM AUTHOR]

Objective Intracranial meningiomas constitute a third of all brain tumors and are among the most common indications for neurosurgical procedures performed worldwide. Most meningiomas present with an indolent, longstanding history. However, the data on outcomes of emergency surgeries for meningioma is limited. This study aims to present our experience of urgent surgical intervention in patients with meningiomas presenting acutely. We also analyze the factors influencing early neurological outcomes and complications. Materials and Methods All nonelective meningioma surgeries done on an emergency basis between January 2015 and December 2019 were retrospectively reviewed. Patients' demography, clinical, and radiological details were recorded for analysis. The surgical procedure, complications, and follow-up outcomes were also included for statistical comparison. Results Forty-four patients qualified for the study with a mean age of 49.4 ± 13.4 years. The average presenting Glasgow Coma Scale (GCS) was 13; 47.7% of cases presented with altered sensorium. The most common lesion location was convexity (25, 56.8%), and the mean tumor volume was 74.1 ± 36.5 mL. Gross peritumor edema with mass effect was seen in 16 patients (36.4%). The mean Karnofsky Performance Status at 3 months' follow-up was 89.3 ± 18.2. Patient age and tumor size did not affect outcomes. The presenting GCS of < 15 (odds ratio [OR] 8.8, confidence interval [CI] 0.95–80.72, p 0.03) and the occurrence of postoperative complications (OR 25.71, CI 2.65–249.2, p 0.001) were associated with unfavorable outcomes. Although not statistically significant, a poor tumor grade was also associated with worse clinical outcomes (p 0.20). Conclusion Emergency meningioma surgery has comparable outcomes and complication rates with routine elective procedures. Grade II/III meningiomas are more likely to present with acute neurological deterioration and carry a relatively worse prognosis. Poor presenting GCS and postoperative complications are the most critical factors associated with poor patient outcomes in our study. [ABSTRACT FROM AUTHOR]

Introduction Cavernous sinus meningiomas (CSMs) are challenging given their proximity to neurovascular structures. Treatments include surgery, radiation, or observation. There are little data comparing outcomes based on treatment. This study presents the radiographic and functional outcomes of patients with CSMs treated with either radiation, surgical resection ± radiation, or radiographic surveillance. Methods This is a retrospective review of adults diagnosed with CSMs between 2009 and 2016. Patient and tumor characteristics and outcomes were obtained from chart review. Categorical and continuous variables were compared via nonparametric tests. Results Sixty-four patients were diagnosed with CSMs between 2009 and 2016. Thirty-four had radiation. Thirteen had surgical resection with adjuvant radiation and 14 underwent observation. Fifty-four patients had a pretreatment deficit (84.4%). Radiographic control rates were 100% in the surgery group, 97.1% in the radiation group, 69.2% in the surgery + adjuvant radiation, and 90.9% in the observation group (p = 0.035). The radiotherapy group had complete improvement of symptoms in 32.4% and partial improvement in 23.1% (p = 0.022). In the surgery group, one patient had complete resolution of symptoms (6.3%), eight had partial improvement (50.0%), and seven patients had no improvement (p = 0.002). Surgery was more likely to lead to progression of symptoms posttreatment (odds ratio: 5.16, confidence interval: 1.309–20.34) (p = 0.019). Conclusion Radiation has an excellent rate of radiographic control. Surgery is an option for large or symptomatic lesions. The role of surgery is primarily for vision preservation, decompression of cranial nerves, and tumor debulking and patients should be counseled extensively regarding treatment goals and expectations prior to surgery. [ABSTRACT FROM AUTHOR]

The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence. Machine learning and more specifically, deep learning algorithms are of great help in analysing medical and clinical images to detect as well as classify diseases. In this paper, we propose a system for detecting various childhood diseases using a range of advanced Convolutional Neural Network models like EfficientNetB0, EfficientNetB3, Xception, InceptionV3, MobileNetV2, VGG19, DenseNet169, ResNet50V2, ResNet152V2, and the hybrid architecture InceptionResNetV2. These models are trained on MRI images of paediatric brain disorders to achieve high prediction accuracy. We use data visualization techniques such as segmentation and contour-based feature extraction to extract regions of interest before feeding the data into the models. The models are optimized using both ADAM and RMSprop optimizers. EfficientNetB0, when optimized with RMSprop, achieves an accuracy of 94.59%, a loss of 0.44, and an RMSE of 0.66. InceptionResNetV2, optimized with ADAM, achieves the highest accuracy of 97.59%, while EfficientNetB0 demonstrates the lowest loss (0.25) and RMSE (0.5). We also evaluate the models based on their precision, learning curves, recall, computational time, and F1 score, highlighting the effectiveness of AI-driven approaches for the diagnosis and management of children's diseases. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

The relationship between meningiomas and gonadal steroid hormones has been the subject of debate, and there is limited understanding of the connection between patient, tumor characteristics, and progesterone receptor (PGR) status. This retrospective observational study aims to explore the prognostic correlation between PGR+ and PGR-meningiomas in terms of various clinical, radiological, and surgical predictors. The analysis included 270 patients, divided into 2 groups: group A (PGR-, 194 patients), and group B (PGR+, 76 patients). The analysis showed no significant differences in terms of age, sex, clinical debut, postsurgical complications, total resection, and grading between the 2 groups. However, a significant difference was observed in the mean Karnofsky performance status at all stages of follow-up. Peritumoral edema measured in preoperative magnetic resonance imaging significantly influences the value of Karnofsky performance status in both preoperative (ANOVA, P = 0.05) and postoperative evaluation (postoperative ANOVA, P = 0.014) only in group A. In the multivariate analysis, there are no significant factors related to the clinical, biological, and surgical parameters previously examined for each measurement time (P = 0.826). The study found that PGR + meningioma patients tend to have better postoperative recovery and earlier clinical debut without any association with age prevalence or grading. [ABSTRACT FROM AUTHOR]

The aim was to conduct a comprehensive bibliometric analysis of meningioma research, focusing on publication trends, top authors, universities, countries, and thematic analysis of keywords. From Scopus databases, first we analyzed 23,180 papers (or 19,824 articles and 3356 reviews) published between 2001 and 2023. Second, we selected the top 100 most cited papers and descriptively analyzed on Vosviewer and R Studio (bibliometrix/biblioshiny). For both sets, we highlighted the annual publication trends and the impact of top-cited papers. We identified leading authors, universities, and countries contributing significantly to the field. Keyword analysis categorized research themes across all papers. Specific focus was given to the top 100 most cited documents in meningioma. Details about the annual number of papers, mean citations per year, and the citable years are provided. The top authors are presented by number of publications, citations, h-index, g-index, and m-index. Co-word analysis was performed, and the main focus of the 100 most cited papers are presented in more than 20 categories. Our findings may provide insights into the evolution of meningioma research and its scholarly impact. The analysis underscores the pivotal contributions of top authors and institutions, outlines thematic research trends, and highlights areas of significant interest and growth in the field. Our study may contribute to understanding the landscape of meningioma research, guiding future research directions and collaborations. [ABSTRACT FROM AUTHOR]

Background: Refractory epilepsy (RE) increases rapidly after stereotactic radiosurgery (SRS), but the reports on RE treatment are rare, with various methods and efficacy. In recent years, bevacizumab (BEV) has been widely used as it is effective in eliminating intracranial oedema and reducing radioactive damage. The aim of this study was to evaluate the application of additional BEV to RE after SRS in the real world. Methods: Seizure freedom and seizure response were defined as 100% and > 50% reduction in seizure frequency at baseline and 6-, 9- and 12-month follow-ups. The sustained seizure-free (SSF), sustained seizure response (SSR) was used to assess the effectiveness of BEV. The number of anti-seizure medications (ASM), seizure severity (NH3), and epilepsy quality of life rating scale (QOLIE-31) scores were compared before and 12 months after treatment. Results: Forty-one patients were included from January 2020 to December 2022. During the 1-year follow-up, 5 patients (12.2%) achieved SSF lasting 12 months, and 4.9% and 7.3% enjoyed SSF more than 6 months and 9 months, respectively. Twelve patients (29.3%) achieved SSR lasting for 12 months, and 19.5% and 24.4% of the study cohort achieved SSR more than 6 months and 9 months, respectively. Patients’ ASM, NH3, and QOLIE-31 scores significantly improved 12 months after treatment, and the adverse reactions were controllable. Conclusion: This study is the first to explore and report the additional use of BEV in the treatment of RE after SRS in meningiomas. BEV was effective and safe in the treatment of SRS-induced RE. [ABSTRACT FROM AUTHOR]

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Homozygous deletion of CDKN2A/B was recently incorporated into the World Health Organization classification for grade 3 meningiomas. While this marker is overall rare in meningiomas, its relationship to other CDKN2A alterations on a transcriptomic, epigenomic, and copy number level has not yet been determined. We therefore utilized multidimensional molecular data of 1577 meningioma samples from 6 independent cohorts enriched for clinically aggressive meningiomas to comprehensively interrogate the spectrum of CDKN2A alterations through DNA methylation, copy number variation, transcriptomics, and proteomics using an integrated molecular approach. Homozygous CDKN2A/B deletions were identified in only 7.1% of cases but were associated with significantly poorer outcomes compared to tumors without these deletions. Heterozygous CDKN2A/B deletions were identified in 2.6% of cases and had similarly poor outcomes as those with homozygous deletions. Among tumors with intact CDKN2A/B (without a homozygous or heterozygous deletion), we found a distinct difference in outcome based on mRNA expression of CDKN2A, with meningiomas that had elevated mRNA expression (CDKN2Ahigh) having a significantly shorter time to recurrence. The expression of CDKN2A was independently prognostic after accounting for copy number loss and consistently increased with WHO grade and more aggressive molecular and methylation groups irrespective of cohort. Despite the discordant and mutually exclusive status of the CDKN2A gene in these groups, both CDKN2Ahigh meningiomas and meningiomas with CDKN2A deletions were enriched for similar cell cycle pathways but at different checkpoints. High mRNA expression of CDKN2A was also associated with gene hypermethylation, Rb-deficiency, and lack of response to CDK inhibition. p16 immunohistochemistry could not reliably differentiate between meningiomas with and without CDKN2A deletions but appeared to correlate better with mRNA expression. These findings support the role of CDKN2A mRNA expression as a biomarker of clinically aggressive meningiomas with potential therapeutic implications.

Against the background of modest successes in the development of new diagnostic and therapeutic tools to improve the survival of patients with glial brain tumors, early diagnosis of this pathology remains relevant. Endogenous noncoding miRNAs that regulate the expression of target mRNAs have become attractive targets for the development of circulating biomarker-based assays, because sample acquisition does not require invasive sampling such as biopsy.Purpose of the study. To determine the levels of circulating microRNAs in the blood plasma of patients with glial tumors, meningiomas and apparently healthy donors, using high-output sequencing.Material and methods. 26 blood plasma samples were selected from the biobank data base of the National Medical Research Center for Oncology, and the total RNA was studied using the NGS sequencing method. The sample included: 2 cases of oligodendroglioma (grades 2–3), 6 – astrocytomas of 2–4 degrees of malignancy, 7 – glioblastomas of 4 degrees of malignancy, 7 – benign neoplasms (meningiomas), 4 – control (conditionally healthy donors).Results. During the primary analysis, a pool of 71 differentially expressed microRNAs was identified, the expression of which was tumor-specific: 20 microRNAs for glioblastoma, 4 microRNAs for astrocytoma, 23 microRNAs for oligodendroglioma, 24 microRNAs for meningioma. At the same time, 47 microRNAs showed increased levels in the blood plasma compared to the control group, 15 showed a corresponding decrease in levels. A comparative analysis identified microRNAs that specifically differentiate each tumor type.Conclusion. The results obtained seem promising and set the vector for further research, which will include expanding the sample and validating the identified biomarkers to determine their diagnostic value.

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. [ABSTRACT FROM AUTHOR]

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. In light of recent advances in the molecular profiling of meningiomas, this review provides practical guidance for incorporating genomic, methylomic, and proteomic information about meningiomas into management decisions. [ABSTRACT FROM AUTHOR]

Background: Meningiomas are the most prevalent benign intracranial tumors. When they are of the invasive subtypes, i.e., grades II and III, they can recur rapidly and present a real challenge for physicians. This study is focused on the use of stereotactic radiosurgery to manage high-grade meningiomas. Method: Medline via PubMed was searched from inception to December 2022 to retrieve studies on stereotactic radiation therapy for patients with grade II-III meningiomas. This study was conducted under PRISMA guidelines. Result: A total of 29 articles involving 1446 patients with grade II-III meningiomas treated with stereotactic radiation therapy were included in the present study. Of these studies, 11 were conducted exclusively on patients with atypical meningiomas (grade II), 1 targeted anaplastic meningiomas (grade III), and 17 articles were carried out on both grade II and III meningiomas. The pooled 1, 2, 3, 5, and 10-year overall survival (OS) of grade II meningiomas was 0.96 [p < 0.01], 0.89 [p = 0.01], 0.90 [p = 0.09], 0.81 [p < 0.01], and 0.66 [p = 0.55], respectively. The pooled 2, 5, and 10-year OS of grade III meningiomas was 0.64 [p = 0.01], 0.41 [p = 0.01], and 0.19 [p < 0.01], respectively. Conclusions: Although long-term prospective studies are still required, the outcomes of stereotactic radiation therapy appear promising regarding overall outcome and progression-free survival. [ABSTRACT FROM AUTHOR]

A meningioma is a common primary central nervous system tumor. The histological features of meningiomas vary significantly depending on the grade and subtype, leading to differences in treatment and prognosis. Therefore, early diagnosis, grading, and typing of meningiomas are crucial for developing comprehensive and individualized diagnosis and treatment plans. The advancement of artificial intelligence (AI) in medical imaging, particularly radiomics and deep learning (DL), has contributed to the increasing research on meningioma grading and classification. These techniques are fast and accurate, involve fully automated learning, are non-invasive and objective, enable the efficient and non-invasive prediction of meningioma grades and classifications, and provide valuable assistance in clinical treatment and prognosis. This article provides a summary and analysis of the research progress in radiomics and DL for meningioma grading and classification. It also highlights the existing research findings, limitations, and suggestions for future improvement, aiming to facilitate the future application of AI in the diagnosis and treatment of meningioma. [ABSTRACT FROM AUTHOR]

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. Highlights: Hsa_circ_0004872 was downregulated in meningiomas and negatively correlated with PD-L1. Overexpression of hsa_circ_0004872 hindered the metastasis and immune escape of meningioma cells. Hsa_circ_0004872 competitively interacted to EIF4A3 to decay PD-L1 mRNA. EIF4A3 upregulation facilitated the metastasis and immune escape of meningioma cells. [ABSTRACT FROM AUTHOR]

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis. [ABSTRACT FROM AUTHOR]

Purpose Skull base meningiomas represent a very challenging pathology due to relatively difficult surgical access. In contrast, stereotactic radiosurgery (SRS) proved to be an effective and more secure treatment technique based on the greater accuracy in delivering precise focused radiation into the target, sparing at the same time healthy surrounding tissues. Methods and Results Our study, based on almost 20 years of experience in delivering SRS treatments using various models of Leksell Gamma-Knife units, reports a high tumor control rate for complex-shaped skull base meningiomas close to critical structures. We retrospectively evaluated the risk factors and complications after high-dose irradiation in patients undergoing single-fraction radiosurgery combined with clinical imaging criteria established using MRI scans (in T1 weighted imaging with gadolinium and the edema in T2 weighted sequences). The mean volume of the tumors was 18.6 cubic centimetres (only tumors with a volume in excess of 15 cubic centimetres were included in the study). The median administered marginal dose was 12.5 Gy. Mean imaging follow-up was 112 months. Tumor control rate was not influenced by sex, age, tumor site, neurological status of the patient or irradiated volume, even though larger meningiomas are associated with poor long-term local control in most published series. The long-term follow-up data indicates tumor control in 88,5% of patients after 10 years, with low incidence of complications. Conclusions Current practice shows a slight potential increase in the incidence of meningiomas, the superiority of the individual techniques needing to be confirmed in prospective and methodologically rigorous studies with at least 20 years of follow-up.

Neurofibromatosis 2 (NF2) is characterised by numerous tumours in the central and peripheral nervous systems due to NF2 gene abnormalities that cause the tumour suppressor protein, Merlin, to disappear. Often referred to as Multiple Inherited Schwannomas, Meningiomas, and Ependymomas (MISME), a distinctive characteristic of NF2 is bilateral vestibular schwannomas manifesting in late adolescence with symptoms such as sensorineural hearing loss, tinnitus, and balance issues. Two distinct phenotypes, Wishart and Feiling-Gardner, characterise NF2. This case report discusses the case of a paediatric patient who presented with bilateral hearing loss, giddiness, and blurring of vision and sought a Magnetic Resonance Imaging (MRI) examination which revealed bilateral vestibular schwannomas, non vestibular schwannomas, left sphenoid wing meningiomas, multidirectional spinal schwannomas, spinal nerve sheath tumours, and lesions in the retroperitoneal region. Despite an absent family history, significant involvement of cranial nerves strongly indicates classical NF2. Management focuses on preserving function, and surgery is contemplated for symptomatic lesions and tumours causing cord compression. Gamma Knife radiosurgery and targeted therapies have been investigated.

Abstract Background This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery. Materials A total of 326 patients with pathologically confirmed meningiomas were enrolled. Samples were randomly split with a 6:2:2 ratio to the training set, validation set, and test set. Volumetric regions of interest (VOIs) were manually drawn on each slice using the ITK-SNAP software. An automatic segmentation model based on SegResNet was developed for the meningioma segmentation. Segmentation performance was evaluated by dice coefficient and 95% Hausdorff distance. Intra class correlation (ICC) analysis was applied to assess the agreement between radiomic features from manual and automatic segmentations. Radiomics features derived from automatic segmentation were extracted by pyradiomics. After feature selection, a model for meningiomas grading was built. Results The DLM detected meningiomas in all cases. For automatic segmentation, the mean dice coefficient and 95% Hausdorff distance were 0.881 (95% CI: 0.851–0.981) and 2.016 (95% CI:1.439–3.158) in the test set, respectively. Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). For meningioma classification, the radiomics model based on automatic segmentation performed well in grading meningiomas, yielding a sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.778 (95% CI: 0.701–0.856), 0.860 (95% CI: 0.722–0.908), 0.848 (95% CI: 0.715–0.903) and 0.842 (95% CI: 0.807–0.895) in the test set, respectively. Conclusions The DLM yielded favorable automated detection and segmentation of meningioma and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

Concurrent occurrence of schwannoma and meningiomas are rare, and are found especially in association with neurofibromatosis type 2 (NF2). Occurrence of mixed tumor without the aforementioned conditions is extremely rare. We present three cases of mixed tumor in different locations, including two with NF2 and one without NF2. We analyse the relationship of mixed tumor with NF2 and its clinical implications. Presence of mixed schwannoma–meningioma should prompt screening for NF2. Thus aids in early diagnosis of unsuspected NF2 cases. We observed that irrespective of different locations, cases with NF2 showed frequent recurrence of schwannoma as compared to case who did not fit in the existing clinical criteria for NF2. Collision tumor and thereby NF2 mutations indicates the prognosis and recurrence of the tumor, thereby guides in deciding the management. [ABSTRACT FROM AUTHOR]

Resection of meningiomas (THMs) at the torcular Herophili poses challenges to surgeons due to complex regional anatomy. The current study aims to evaluate factors affecting the extent of resections, progression-free survival, and the role of radiation. We did a retrospective study of all the THM patients treated at our institute between May 1987 and June 2022. The demographic data, signs and symptoms, surgical notes, postoperative details, and radiotherapy were gathered retrospectively. Survival analysis was done with Kaplan-Meier tests along with predictors of the extent of resection as well as recurrence. A total of 39 patients qualified to be included in the study, with 10 male patients (male:female 1:2.9) and an average age of 50.8 years. The average follow-up duration was 75.9 months. Simpson's grade 2 excision was achieved in 19 (48.7%) patients, followed by Simpson's grade 3 excision in 17 patients (43.6%). Progression-free survival in subtotal resection was 60 months, and 100 months in gross total resection. Statistically, the extent of the resection was determined by the involvement of sinuses/torcula and the number of quadrants involved around torcula. A total of 16 patients received radiosurgery for the residual or small reccurrence of the lesion. Follow-up revealed reccurrence in 5 cases. Torcular meningiomas are relatively uncommon, described in few reports, and represent a therapeutic dilemma. Though some experts recommend complete removal of tumor and reconstruction of the sinus, others suggest maximum safe resection, followed by radiosurgery. The present study reflects reasonable control of the residual lesion with radiosurgery after maximum safe resection. [ABSTRACT FROM AUTHOR]

The Simpson grading scale for the classification of the extent of meningioma resection provided a tremendous movement forward in 1957 suggesting increasing the extent of resection improves recurrence rates. However, equal, if not greater, movements forward have been made in the neurosurgical community over the last half a century owing to improvements in neuroimaging capabilities, microsurgical techniques, and radiotherapeutic strategies. Sughrue et al proposed the idea that these advancements have altered what a "recurrence" and "subtotal resection" truly means in modern neurosurgery compared with Simpson's era, and that a mandated use of the Simpson Scale is likely less clinically relevant today. A subsequent period of debate ensued in the literature which sought to re-examine the clinical value of using the Simpson Scale in modern neurosurgery. While a large body of evidence has recently been provided, these data generally continue to support the clinical importance of gross tumor resection as well as the value of adjuvant radiation therapy and the importance of recently updated World Health Organization classifications. However, there remains a negligible interval benefit in performing overly aggressive surgery and heroic maneuvers to remove the last bit of tumor, dura, and/or bone just for the simple act of achieving a lower Simpson score. Ultimately, meningioma surgery may be better contextualized as a continuous set of weighted risk–benefit decisions throughout the entire operation. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue. [ABSTRACT FROM AUTHOR]

Neurofibromatosis 2 (NF2) is characterised by numerous tumours in the central and peripheral nervous systems due to NF2 gene abnormalities that cause the tumour suppressor protein, Merlin, to disappear. Often referred to as Multiple Inherited Schwannomas, Meningiomas, and Ependymomas (MISME), a distinctive characteristic of NF2 is bilateral vestibular schwannomas manifesting in late adolescence with symptoms such as sensorineural hearing loss, tinnitus, and balance issues. Two distinct phenotypes, Wishart and Feiling-Gardner, characterise NF2. This case report discusses the case of a paediatric patient who presented with bilateral hearing loss, giddiness, and blurring of vision and sought a Magnetic Resonance Imaging (MRI) examination which revealed bilateral vestibular schwannomas, non vestibular schwannomas, left sphenoid wing meningiomas, multidirectional spinal schwannomas, spinal nerve sheath tumours, and lesions in the retroperitoneal region. Despite an absent family history, significant involvement of cranial nerves strongly indicates classical NF2. Management focuses on preserving function, and surgery is contemplated for symptomatic lesions and tumours causing cord compression. Gamma Knife radiosurgery and targeted therapies have been investigated. [ABSTRACT FROM AUTHOR]

Background: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery. Materials: A total of 326 patients with pathologically confirmed meningiomas were enrolled. Samples were randomly split with a 6:2:2 ratio to the training set, validation set, and test set. Volumetric regions of interest (VOIs) were manually drawn on each slice using the ITK-SNAP software. An automatic segmentation model based on SegResNet was developed for the meningioma segmentation. Segmentation performance was evaluated by dice coefficient and 95% Hausdorff distance. Intra class correlation (ICC) analysis was applied to assess the agreement between radiomic features from manual and automatic segmentations. Radiomics features derived from automatic segmentation were extracted by pyradiomics. After feature selection, a model for meningiomas grading was built. Results: The DLM detected meningiomas in all cases. For automatic segmentation, the mean dice coefficient and 95% Hausdorff distance were 0.881 (95% CI: 0.851–0.981) and 2.016 (95% CI:1.439–3.158) in the test set, respectively. Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636–0.933). For meningioma classification, the radiomics model based on automatic segmentation performed well in grading meningiomas, yielding a sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.778 (95% CI: 0.701–0.856), 0.860 (95% CI: 0.722–0.908), 0.848 (95% CI: 0.715–0.903) and 0.842 (95% CI: 0.807–0.895) in the test set, respectively. Conclusions: The DLM yielded favorable automated detection and segmentation of meningioma and can help deploy radiomics for preoperative meningioma differentiation in clinical practice. [ABSTRACT FROM AUTHOR]