Meningiomas, the most prevalent primary intracranial tumors, exhibit significant intratumor heterogeneity, which is crucial for understanding their molecular drivers and potential therapeutic targets. A study conducted multiplatform genomic profiling on 86 spatially distinct samples from 13 meningiomas, revealing mechanisms underlying this heterogeneity and identifying new molecular therapy targets (ref: Magill doi.org/10.1038/s41467-020-18582-7/). Another investigation focused on the sensitivity of meningioma cells to the cyclin-dependent kinase inhibitor TG02, finding that cell cultures from tumors assigned to a more malignant methylation class exhibited greater sensitivity, suggesting a potential link between methylation status and therapeutic response (ref: von Achenbach doi.org/10.1016/j.tranon.2020.100852/). Additionally, the identification of novel fusion transcripts in meningiomas, including six distinct fusion events, highlights the complexity of their genomic landscape and may provide insights into tumor biology and treatment strategies (ref: Khan doi.org/10.1007/s11060-020-03599-1/). High copy-number variation burdens were also noted in cranial meningiomas, with specific genomic regions correlating with clinical phenotypes, indicating that genomic alterations may influence tumor behavior and patient outcomes (ref: Ma doi.org/10.3389/fonc.2020.01382/). Proteomic analyses further elucidated signaling cascades in meningiomas, identifying druggable targets and pathways that could be exploited for therapeutic intervention (ref: Mukherjee doi.org/10.3389/fonc.2020.01600/). Lastly, a voxel-wise analysis of meningioma locations based on biological characteristics provided insights into tumor behavior and potential surgical implications (ref: Sun doi.org/10.3389/fonc.2020.01412/).

The surgical management of meningiomas has evolved with various techniques tailored to tumor location and characteristics. A comparative study of three surgical approaches for frontobasal meningiomas (FBMs) demonstrated that the choice of technique—purely endoscopic endonasal, purely microscopic bifrontal transcranial, or a combined approach—should be guided by surgical experience and tumor specifics, with incomplete tumor removal noted in some cases (ref: Kahilogullari doi.org/10.1097/SCS.0000000000006970/). The eyebrow supraorbital keyhole craniotomy, enhanced by endoscopic assistance, has emerged as a minimally invasive option for olfactory groove meningiomas, showing comparable tumor control rates to traditional open craniotomy while reducing frontal lobe injury (ref: Youngerman doi.org/10.1007/s00701-020-04552-x/). Furthermore, the surgical technique for diaphragma sellae meningiomas requires a thorough understanding of skull base anatomy due to the complex neurovascular relationships involved, emphasizing the importance of preoperative evaluation (ref: Belouaer doi.org/10.1007/s00701-020-04581-6/). Visual outcomes following surgery for spheno-orbital meningiomas indicated significant improvements in preoperative visual acuity, highlighting the potential for surgical intervention to restore vision in patients with minimal impairment (ref: Zamanipoor Najafabadi doi.org/10.1007/s00701-020-04554-9/). Additionally, telehealth applications have been explored for clinical decision-making in meningioma management, demonstrating the potential for improved patient access to care (ref: Boggs doi.org/10.5195/ijt.2020.6302/).

Recent advancements in imaging techniques have significantly enhanced the diagnosis and management of meningiomas. A novel content-based medical image retrieval system utilizing contrastive loss based similarity on GoogLeNet encodings has shown promising results in retrieving MRI images of brain tumors, including meningiomas, thereby aiding in diagnostic accuracy (ref: Deepak doi.org/10.1016/j.compbiomed.2020.103993/). Machine learning-based radiomics analysis has also been employed to predict meningioma grades using multiparametric MRI, achieving an area under the curve (AUC) of 0.75-0.80, which underscores the potential of radiomic models in clinical decision-making (ref: Hu doi.org/10.1016/j.ejrad.2020.109251/). Furthermore, presurgical detection of brain invasion status in meningiomas through texture analysis of contrast-enhanced imaging has demonstrated high predictive accuracy, with an AUC of 0.999 for differentiating grade I from grade II meningiomas (ref: Kandemirli doi.org/10.1016/j.clineuro.2020.106205/). A meta-analysis of endoscope-assisted surgical approaches for anterior skull base meningiomas highlighted the effectiveness of these techniques, with gross total resection rates and visual improvement metrics supporting their use (ref: Khan doi.org/10.1007/s00701-020-04544-x/). These imaging advancements not only facilitate better surgical planning but also contribute to improved patient outcomes.

The management of meningiomas encompasses a multidisciplinary approach aimed at optimizing clinical outcomes. A study investigating the use of dual-energy CT (DECT) for radiation therapy planning demonstrated enhanced soft tissue contrast, which could improve delineation of structures during treatment (ref: Noid doi.org/10.3389/fonc.2020.01694/). Machine learning-based radiomics has been applied to predict meningioma grades using multiparametric MRI, yielding valuable insights that may guide clinical decision-making (ref: Hu doi.org/10.1016/j.ejrad.2020.109251/). Furthermore, genomic profiling of meningiomas has revealed significant intratumor heterogeneity, which may influence treatment strategies and patient management (ref: Magill doi.org/10.1038/s41467-020-18582-7/). The identification of novel fusion transcripts in meningiomas also contributes to understanding tumor biology and potential therapeutic targets (ref: Khan doi.org/10.1007/s11060-020-03599-1/). Additionally, the expression of GLUT3 and hypoxia-inducible factor-1 (HIF-1) in meningiomas has been correlated with tumor size and grade, providing further insights into tumor behavior and potential prognostic markers (ref: Mei doi.org/10.1155/2020/). These findings underscore the importance of integrating genomic, radiological, and clinical data to enhance patient management and outcomes in meningioma care.

The treatment landscape for meningiomas is evolving, with ongoing research into novel therapeutic strategies. A retrospective multicenter study evaluated the efficacy of Brivaracetam in patients with brain tumor-related epilepsy, suggesting favorable outcomes and tolerability in this population (ref: Maschio doi.org/10.3389/fneur.2020.00813/). The sensitivity of meningioma cells to the cyclin-dependent kinase inhibitor TG02 was explored, revealing that tumor methylation class may be a more significant predictor of drug sensitivity than WHO grade, indicating the need for personalized treatment approaches (ref: von Achenbach doi.org/10.1016/j.tranon.2020.100852/). Genomic profiling has identified mechanisms underlying intratumor heterogeneity, which may inform targeted therapies and improve treatment outcomes (ref: Magill doi.org/10.1038/s41467-020-18582-7/). The identification of novel fusion transcripts in meningiomas also opens avenues for targeted therapies, enhancing our understanding of tumor biology (ref: Khan doi.org/10.1007/s11060-020-03599-1/). Radiomics analysis using multiparametric MRI has shown promise in predicting meningioma grades, which could guide therapeutic decisions (ref: Hu doi.org/10.1016/j.ejrad.2020.109251/). Collectively, these studies highlight the importance of integrating genomic, radiological, and clinical data to develop effective treatment strategies for meningiomas.

Understanding the epidemiology and risk factors associated with meningiomas is critical for prevention and early detection. A study on childhood cancer survivors revealed that radiotherapy significantly increases the risk of subsequent neoplasms, including meningiomas, emphasizing the need for long-term monitoring of this population (ref: Morton doi.org/10.1200/PO.20.00141/). Additionally, research into neuromuscular morbidity following radiotherapy for Hodgkin lymphoma highlighted the complications that can arise, further underscoring the importance of understanding treatment-related risks (ref: Oishi doi.org/10.1093/braincomms/). The expression of GLUT3 and HIF-1 in meningiomas has been correlated with tumor size and grade, suggesting that metabolic factors may play a role in tumor development and progression (ref: Mei doi.org/10.1155/2020/). These findings indicate that both genetic predispositions and treatment histories contribute to the risk profile for meningiomas, necessitating a comprehensive approach to patient management and surveillance.

Neurological morbidity and quality of life in meningioma patients are critical considerations in treatment planning and patient care. A study investigating fatigue in brain tumor patients found that self-reported fatigue correlated with objective measures of brain activity, suggesting a potential biomarker for fatigue in this population (ref: de Dreu doi.org/10.1016/j.nicl.2020.102406/). Additionally, the retrospective analysis of brain tumor-related epilepsy patients treated with Brivaracetam indicated that this medication may improve quality of life by effectively managing seizure activity (ref: Maschio doi.org/10.3389/fneur.2020.00813/). The expanded neuromuscular morbidity observed in Hodgkin lymphoma patients post-radiotherapy also highlights the long-term effects of treatment on neurological function, which may be relevant for meningioma patients undergoing similar therapies (ref: Oishi doi.org/10.1093/braincomms/). These studies emphasize the importance of addressing neurological symptoms and their impact on quality of life in meningioma patients, advocating for a holistic approach to treatment that includes supportive care and symptom management.

Innovative diagnostic techniques are enhancing the accuracy and efficiency of meningioma identification and characterization. A novel approach utilizing molecular fragment spectra generated by laser ablation combined with a spiking neural network has been proposed for rapid pathological identification of brain tumors, potentially reducing intraoperative waiting times (ref: Teng doi.org/10.1364/BOE.397268/). Additionally, the differentiation of solitary fibrous tumors from angiomatous meningiomas using signal value differences in T1- and T2-weighted images has shown promise, indicating that imaging characteristics can aid in preoperative diagnosis (ref: He doi.org/10.1016/j.clineuro.2020.106221/). The identification of novel fusion transcripts in meningiomas also contributes to the understanding of tumor biology and may facilitate the development of targeted diagnostic assays (ref: Khan doi.org/10.1007/s11060-020-03599-1/). These advancements highlight the importance of integrating innovative technologies into clinical practice to improve diagnostic accuracy and patient outcomes in meningioma management.