Diagnostic challenges in meningioma

Epstein-Barr Virus-Associated Smooth-Muscle Tumor of the Brain

Epstein-Barr virus, a herpesvirus, has been associated with a variety of cancers, including Burkitt, Hodgkin, and non-Hodgkin lymphomas; posttransplant lymphoproliferative disorders; gastric carcinoma; and nasopharyngeal carcinoma, in both immunocompetent and immunocompromised individuals. Previous studies have established a connection between Epstein-Barr virus and the development of smooth-muscle tumors. Smooth-muscle tumors of the brain are very rare and are often misdiagnosed as meningiomas on imaging. To our knowledge, advanced imaging findings such as MR perfusion of smooth-muscle tumors of the brain have never been reported. We describe the radiologic and pathologic features of the Epstein-Barr virus-associated smooth-muscle tumors of the brain in a person with newly diagnosed advanced HIV.

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.

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.

Biomarkers for prognosis of meningioma patients: A systematic review and meta-analysis

Meningioma is the most common primary brain tumor and many studies have evaluated numerous biomarkers for their prognostic value, often with inconsistent results. Currently, no reliable biomarkers are available to predict the survival, recurrence, and progression of meningioma patients in clinical practice. This study aims to evaluate the prognostic value of immunohistochemistry-based (IHC) biomarkers of meningioma patients. A systematic literature search was conducted up to November 2023 on PubMed, CENTRAL, CINAHL Plus, and Scopus databases. Two authors independently reviewed the identified relevant studies, extracted data, and assessed the risk of bias of the studies included. Meta-analyses were performed with the hazard ratio (HR) and 95% confidence interval (CI) of overall survival (OS), recurrence-free survival (RFS), and progression-free survival (PFS). The risk of bias in the included studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool. A total of 100 studies with 16,745 patients were included in this review. As the promising markers to predict OS of meningioma patients, Ki-67/MIB-1 (HR = 1.03, 95%CI 1.02 to 1.05) was identified to associate with poor prognosis of the patients. Overexpression of cyclin A (HR = 4.91, 95%CI 1.38 to 17.44), topoisomerase II α (TOP2A) (HR = 4.90, 95%CI 2.96 to 8.12), p53 (HR = 2.40, 95%CI 1.73 to 3.34), vascular endothelial growth factor (VEGF) (HR = 1.61, 95%CI 1.36 to 1.90), and Ki-67 (HR = 1.33, 95%CI 1.21 to 1.46), were identified also as unfavorable prognostic biomarkers for poor RFS of meningioma patients. Conversely, positive progesterone receptor (PR) and p21 staining were associated with longer RFS and are considered biomarkers of favorable prognosis of meningioma patients (HR = 0.60, 95% CI 0.41 to 0.88 and HR = 1.89, 95%CI 1.11 to 3.20). Additionally, high expression of Ki-67 was identified as a prognosis biomarker for poor PFS of meningioma patients (HR = 1.02, 95%CI 1.00 to 1.04). Although only in single studies, KPNA2, CDK6, Cox-2, MCM7 and PCNA are proposed as additional markers with high expression that are related with poor prognosis of meningioma patients. In conclusion, the results of the meta-analysis demonstrated that PR, cyclin A, TOP2A, p21, p53, VEGF and Ki-67 are either positively or negatively associated with survival of meningioma patients and might be useful biomarkers to assess the prognosis.

Predicting meningioma grades and pathologic marker expression via deep learning

OBJECTIVES

To establish a deep learning (DL) model for predicting tumor grades and expression of pathologic markers of meningioma.

METHODS

A total of 1192 meningioma patients from two centers who underwent surgical resection between September 2018 and December 2021 were retrospectively included. The pathological data and post-contrast T1-weight images for each patient were collected. The patients from institute I were subdivided into training, validation, and testing sets, while the patients from institute II served as the external testing cohort. The fine-tuned ResNet50 model based on transfer learning was adopted to classify WHO grade in the whole cohort and predict Ki-67 index, H3K27me3, and progesterone receptor (PR) status of grade 1 meningiomas. The predictive performance was evaluated by the accuracy and loss curve, confusion matrix, receiver operating characteristic curve (ROC), and area under curve (AUC).

RESULTS

The DL prediction model for each label achieved high predictive performance in two cohorts. For WHO grade prediction, the area under the curve (AUC) was 0.966 (95%CI 0.957-0.975) in the internal testing set and 0.669 (95%CI 0.643-0.695) in the external validation cohort. The AUC in predicting Ki-67 index, H3K27me3, and PR status were 0.905 (95%CI 0.895-0.915), 0.773 (95%CI 0.760-0.786), and 0.771 (95%CI 0.750-0.792) in the internal testing set and 0.591 (95%CI 0.562-0.620), 0.658 (95%CI 0.648-0.668), and 0.703 (95%CI 0.674-0.732) in the external validation cohort, respectively.

CONCLUSION

DL models can preoperatively predict meningioma grades and pathologic marker expression with favorable predictive performance.

CLINICAL RELEVANCE STATEMENT

Our DL model could predict meningioma grades and expression of pathologic markers and identify high-risk patients with WHO grade 1 meningioma, which would suggest a more aggressive operative intervention preoperatively and a more frequent follow-up schedule postoperatively.

KEY POINTS

WHO grades and some pathologic markers of meningioma were associated with therapeutic strategies and clinical outcomes. A deep learning-based approach was employed to develop a model for predicting meningioma grades and the expression of pathologic markers. Preoperative prediction of meningioma grades and the expression of pathologic markers was beneficial for clinical decision-making.

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions

Objective.To address the challenge of meningioma grading, this study aims to investigate the potential value of peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics and deep learning techniques.Approach.The primary focus is on developing a transfer learning-based meningioma feature extraction model (MFEM) that leverages both vision transformer (ViT) and convolutional neural network (CNN) architectures. Additionally, the study explores the significance of the PTE region in enhancing the grading process.Main results.The proposed method demonstrates excellent grading accuracy and robustness on a dataset of 98 meningioma patients. It achieves an accuracy of 92.86%, precision of 93.44%, sensitivity of 95%, and specificity of 89.47%.Significance.This study provides valuable insights into preoperative meningioma grading by introducing an innovative method that combines radiomics and deep learning techniques. The approach not only enhances accuracy but also reduces observer subjectivity, thereby contributing to improved clinical decision-making processes.

Chronic hyperglycemia and intracranial meningiomas

Meningiomas are among the most common primary tumors of the central nervous system. Previous research into the meningioma histological appearance, genetic markers, transcriptome and epigenetic landscape has revealed that benign meningiomas significantly differ in their glucose metabolism compared to aggressive lesions. However, a correlation between the systemic glucose metabolism and the metabolism of the tumor hasn't yet been found. We hypothesized that chronic levels of glycaemia (approximated with glycated hemoglobin (HbA1c)) are different in patients with aggressive and benign meningiomas. The study encompassed 71 patients with de novo intracranial meningiomas, operated on in three European hospitals, two in Croatia and one in Spain. Our results show that patients with WHO grade 2 meningiomas had significantly higher HbA1c values compared to patients with grade 1 lesions (P = 0.0290). We also found a significant number of patients (19/71; 26.7%) being hyperglycemic, harboring all the risks that such a condition entails. Finally, we found a significant correlation between our patients' age and their preoperative HbA1c levels (P = 0.0008, ρ(rho) = 0.388), suggesting that older meningioma patients are at a higher risk of having their glycaemia severely dysregulated. These findings are especially important considering the current routine and wide-spread use of corticosteroids as anti-edematous treatment. Further research in this area could lead to better understanding of meningiomas and have immediate clinical impact.

Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas

Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.

A Prospective Registry Study of 68Ga-DOTATATE PET/CT Incorporation Into Treatment Planning of Intracranial Meningiomas

PURPOSE

The current standard for meningioma treatment planning involves magnetic resonance imaging-based guidance. Somatostatin receptor ligands such as 68Ga-DOTATATE are being explored for meningioma treatment planning due to near-universal expression of somatostatin receptors 1 and 2 in meningioma tissue. We hypothesized that 68Ga-DOTATATE positron emission tomography (PET)-guided treatment management for patients with meningiomas is safe and effective and can identify which patients benefit most from adjuvant radiation therapy.

METHODS AND MATERIALS

A single-institution prospective registry study was created for inclusion of patients with intracranial meningiomas who received a 68Ga-DOTATATE PET/CT to assist with radiation oncologist decision making. Patients who received a PET scan from January 1, 2018, to February 25, 2022, were eligible for inclusion.

RESULTS

Of the 60 patients included, 40%, 47%, and 5% had World Health Organization grades 1, 2, and 3 meningiomas, respectively, and 8% (5 patients) had no grade assigned. According to Radiation Therapy Oncology Group 0539 criteria, 22%, 72%, and 7% were categorized as high, intermediate, and low risk, respectively. After completing their PET scans, 48 patients, 11 patients, and 1 patient proceeded with radiation therapy, observation, and redo craniotomy, respectively. The median follow-up for the entire cohort was 19.5 months. Of the 3 patients (5%) who experienced local failure between 9.2 and 28.5 months after diagnosis, 2 had PET-avid disease in their postoperative cavity and elected for observation before recurrence, and 1 high-risk patient with multifocal disease experienced local failure 2 years after a second radiation course and multiple previous recurrences. Notably, 5 patients did not have any local PET uptake and were observed; none of these patients experienced recurrence. Only 1 grade 3 toxicity was attributed to PET-guided radiation.

CONCLUSIONS

This study examined one of the largest known populations of patients with intracranial meningiomas followed by physicians who used 68Ga-DOTATATE PET-guided therapy. Incorporating 68Ga-DOTATATE PET into future trials may assist with clinician decision making and improve patient outcomes.

Lethal Complications of Meningiomas: A Case Series

Background

Meningiomas are the most prevalent type of primary intracranial tumor in adults, comprising nearly one-third of all intracranial tumors. They are typically benign, slow-growing, and asymptomatic but may cause neurological symptoms as they expand due to mass effect. Classification is determined by World Health Organization (WHO) grades 1 to 3 following pathological examination corresponding to benign, atypical, and anaplastic (malignant), respectively, reflecting their rate of growth and risk of recurrence. The vast majority are WHO grade 1 and their slow growth permits timely presentation for elective resection; however, meningiomas in vulnerable locations and coexisting morbidities can result in sudden death.

Objectives

We present a series of four adult patients with meningiomas which resulted in death, including a case of fatal seizure, midline hemorrhagic meningioma, postresection meningitis, and compression of the cerebellum.

Research design

Retrospective review of the authors' cases was conducted. Available pathology, medical, and autopsy records including gross images were reviewed in each case. The inclusion criteria were adult patients (>18 years old) and that the cause of death had to include meningioma.

Subjects

The four patients included a 61-year-old male, an 84-year-old female, a 62-year-old male, and a 37-year-old female.

Measures

Qualitative; autopsy reports describing cause of death and pathology report findings including gross and microscopic analysis.

Conclusions

Meningiomas are often benign in nature but can rarely result in death. Size and location of the tumor and risk factors are contributory. Autopsy examination can be instrumental in identifying the cause and mechanism of deaths associated with meningiomas.

Unique magnetic resonance spectroscopy profile of intracranial meningiomas compared to gliomas: a systematic review

BACKGROUND AND PURPOSE

The goal of this study was to systematically review the metabolic profile of meningiomas using magnetic resonance spectroscopy in comparison to gliomas, as measured by mean metabolite ratios.

METHODS

Following the PRISMA guidelines, a systematic literature review was performed using the PubMed, Ovid Embase, Web of Science, and the Cochrane databases from inception to May 2021. Studies were selected based on predetermined inclusion and exclusion criteria.

RESULTS

Eight studies were ultimately selected with 207 patients included. Fifty-nine patients were diagnosed with meningioma (age = 48.4, 66.7% female) and 148 patients diagnosed with glioma (age = 56.4, 49.2% female). Three studies reported elevated Cho/Cr in meningiomas compared to gliomas (5.71 vs. 1.46, p < 0.05, 7.02 vs. 2.62, p < 0.05, and 4.64 vs. 2.52, p = 0.001). One study reported Ala/Cr to be significantly elevated in meningiomas compared to gliomas (1.30 vs. undetectable, p < 0.001). One study reported myo-Inositol/Cr to be significantly elevated in meningiomas in comparison to gliomas (1.44 vs. 1.08, p < 0.05). One study reported Glu/Cr to be significantly elevated in meningiomas in comparison to gliomas (3.47 vs. 0.89, p = 0.002). Two studies reported Cho/NAA to be significantly elevated in meningiomas in comparison to gliomas (4.46 vs. 2.6, p = 0.004, and 5.8 vs. 2.55, p < 0.05). Two studies reported NAA/Cr was significantly elevated in gliomas compared to meningiomas (undetectable vs. 1.54, p < 0.001 and undetectable vs. 0.58, p < 0.05).

CONCLUSIONS

Significant differences in metabolite ratios between tumor types were reported in Cho/Cr, Ala/Cr, Glu/Cr, Cho/NAA, myoI/Cr and NAA/Cr between meningiomas and gliomas.

Domestic Animal Models of Central Nervous System Tumors: Focus on Meningiomas

Intracranial primary tumors (IPTs) are aggressive forms of malignancies that cause high mortality in both humans and domestic animals. Meningiomas are frequent adult IPTs in humans, dogs, and cats, and both benign and malignant forms cause a decrease in life quality and survival. Surgery is the primary therapeutic approach to treat meningiomas, but, in many cases, it is not resolutive. The chemotherapy and targeted therapy used to treat meningiomas also display low efficacy and many side effects. Therefore, it is essential to find novel pharmacological approaches to increase the spectrum of therapeutic options for meningiomas. This review analyzes the similarities between human and domestic animal (dogs and cats) meningiomas by evaluating the molecular and histological characteristics, diagnosis criteria, and treatment options and highlighting possible research areas to identify novel targets and pharmacological approaches, which are useful for the diagnosis and therapy of this neoplasia to be used in human and veterinary medicine.

Postoperative Seizure Prophylaxis in Meningioma Resection: A Systematic Review and Meta-Analysis

BACKGROUND

Seizures in the early postoperative period may impair patient recovery and increase the risk of complications. The aim of this study is to determine whether there is any advantage in postoperative seizure prophylaxis following meningioma resection.

METHODS

This systematic review was conducted in accordance with PRISMA guidelines. PUBMED, Web of Science, Embase, Science Direct, and Cochrane were searched for papers until April 2023.

RESULTS

Among nine studies, a total of 3249 patients were evaluated, of which 984 patients received antiepileptic drugs (AEDs). No significant difference was observed in the frequency of seizure events between patients who were treated with antiepileptic drugs (AEDs) and those who were not. (RR 1.22, 95% CI 0.66 to 2.40; I2 = 57%). Postoperative seizures occurred in 5% (95% CI: 1% to 9%) within the early time period (<7 days), and 9% (95% CI: 1% to 17%) in the late time period (>7 days), with significant heterogeneity between the studies (I2 = 91% and 97%, respectively). In seizure-naive patients, the rate of postoperative seizures was 2% (95% CI: 0% to 6%) in the early period and increased to 6% (95% CI: 0% to 15%) in the late period. High heterogeneity led to the use of random-effects models in all analyses.

CONCLUSIONS

The current evidence does not provide sufficient support for the effectiveness of prophylactic AED medications in preventing postoperative seizures in patients undergoing meningioma resection. This underscores the importance of considering diagnostic criteria and conducting individual patient analysis to guide clinical decision-making in this context.

Unveiling a Biomarker Signature of Meningioma: The Need for a Panel of Genomic, Epigenetic, Proteomic, and RNA Biomarkers to Advance Diagnosis and Prognosis

Meningiomas are the most prevalent primary intracranial tumors. The majority are benign but can undergo dedifferentiation into advanced grades classified by World Health Organization (WHO) into Grades 1 to 3. Meningiomas' tremendous variability in tumor behavior and slow growth rates complicate their diagnosis and treatment. A deeper comprehension of the molecular pathways and cellular microenvironment factors implicated in meningioma survival and pathology is needed. This review summarizes the known genetic and epigenetic aberrations involved in meningiomas, with a focus on neurofibromatosis type 2 (NF2) and non-NF2 mutations. Novel potential biomarkers for meningioma diagnosis and prognosis are also discussed, including epigenetic-, RNA-, metabolomics-, and protein-based markers. Finally, the landscape of available meningioma-specific animal models is overviewed. Use of these animal models can enable planning of adjuvant treatment, potentially assisting in pre-operative and post-operative decision making. Discovery of novel biomarkers will allow, in combination with WHO grading, more precise meningioma grading, including meningioma identification, subtype determination, and prediction of metastasis, recurrence, and response to therapy. Moreover, these biomarkers may be exploited in the development of personalized targeted therapies that can distinguish between the 15 diverse meningioma subtypes.

The role of m6A RNA methylation regulator in meningioma

Meningiomas are common intracranial tumors, and the effect of surgical resection is often unsatisfactory. N6-Methyladenosine (m6A)-related regulator expression levels are related to cancer occurrence and development. This study aimed to investigate the roles of m6A RNA methylation regulators in meningiomas, as these are currently unclear. Two m6A methylation-regulated genes (METTL3 and IGF2BP2) were identified as survival-associated linear models for RiskScore through bioinformatics analysis. Univariate and multivariate Cox regression analyses showed that the overall survival of patients with meningioma in the high-risk group was substantially shorter than that in the low-risk group. Weighted gene co-expression network analysis constructed a co-expression network based on the m6A methylation model (RiskScore). Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses identified the biological processes of hub module gene behavior, and Cytoscape constructed an m6A methylation-related gene regulatory network. In vitro experiments verified that the mRNA and protein expression levels of METTL3 and IGF2BP2 were lower in meningioma cells than in normal meningioma cells. Therefore, central regulators of m6A methylation (METTL3 and IGF2BP2) could potentially serve as novel therapeutic targets in meningioma. Subsequently, a novel methylation signature (RiskScore) was developed for prognostic prediction in patients with meningioma.

Humps and bumps of head: review of meningiomas of the scalp

Meningiomas are a type of tumor that arises from meningothelial cells and primarily develops in intracranial space, being some of the most common benign tumors of the central nervous system. However, meningiomas can rarely occur on the scalp and are called primary cutaneous meningiomas. Since the pathogenesis of these lesions is still uncertain, these tumors still pose challenges in terms of histopathological diagnosis. In this review, we will discuss the main cases of scalp meningiomas in the literature, their classification, pathological and immunohistochemical diagnosis, differential diagnosis with other scalp lesions and the most effective treatment. This study highlights the importance of immunohistochemistry in the differential diagnosis of skin lesions located on the scalp.

A Novel Report of Suspected Prostate Adenocarcinoma to Orbital Roof Meningioma Metastasis

: Tumor-to-meningioma metastasis (TTMM) is an uncommon phenomenon, in which a primary malignant tumor metastasizes to a recipient preexisting meningioma. Herein, the authors report a case of a 74-year-old man with a known history of metastatic prostate adenocarcinoma who with frontal headache and right orbital apex syndrome. Initial CT studies demonstrated a right orbital roof osseous lesion. Subsequent MRI was reported as characteristic of an intraosseous meningioma with intracranial and intraorbital extensions. A biopsy of the right orbital mass was obtained and returned a diagnosis of metastatic prostate cancer. The combination of imaging and pathologic findings suggested that the clinical scenario was overall most in keeping with a skull bone-based prostate adenocarcinoma metastasis infiltrating a preexisting meningioma. This is a rare case of TTMM in an orbit-based meningioma, presenting with an orbital apex syndrome.

Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning

OBJECTIVES

To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.

METHODS

In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model.

RESULTS

On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading.

CONCLUSIONS

An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation.

KEY POINTS

• The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

Case Report: Recurrent meningioma with multiple metastases

Post-surgery recurrence of meningiomas with multiple extracranial metastases is rare. Currently, information on extracranial metastases is limited, and no clear predictors and standardized treatment protocols can be applied clinically. Herein, we report a case of meningioma that recurred after two surgeries and had multiple distant metastases. Computed tomography revealed multiple enlarged lymph nodes in the para-aortic arch, left lower lung region, retroperitoneum, and abdominopelvic region, as well as soft tissue mass-like lesions under the liver capsule in the right lobe of the liver. Magnetic resonance imaging showed space-occupying lesions under the cranial plate of the left parietal lobe. Tissue biopsy confirmed the diagnosis of recurrent meningioma with extracranial metastases. Immune checkpoint inhibitors and anti-angiogenic drugs were administered. After two treatment cycles, the patient's clinical symptoms were significantly relieved, and the imaging assessment confirmed a stable disease. Although it did not meet our expectations, this combination therapy still demonstrated a possible benefit in improving meningioma patients' survival and quality of life. In this report, along with the case, we also review the relevant literature on the subject and discuss the associated risk factors and treatment options.

Review of meningioma diagnosis and management

Meningiomas are the most common intracranial tumors in adult patients. Although the majority of meningiomas are diagnosed as benign, approximately 20% of cases are high-grade tumors that require significant clinical treatment. The gold standard for grading central nervous system tumors comes from the World Health Organization Classification of Tumors of the central nervous system. Treatment options also depend on the location, imaging, and histopathological features of the tumor. This review will cover diagnostic strategies for meningiomas, including 2021 updates to the World Health Organization's grading of meningiomas. Meningioma treatment plans are variable and highly dependent on tumor grading. This review will also update the reader on developments in the treatment of meningiomas, including surgery, radiation therapy and monoclonal antibody treatment.

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Meningioma was the most primary intracranial tumor, but the molecular characteristics and the treatment of malignant meningioma were still unclear. Nine malignant progression-related genes based prognostic signatures were identified by transcriptome analysis between benign meningioma and malignant meningioma. The external dataset GEO136661 and quantitative Real-time Polymerase Chain Reaction were used to verify the prognostic factors. has-miR-3605-5p, hsa-miR-664b-5p, PNRC2, BTBD8, EXTL2, SLFN13, DGKD, NSD2, and BVES were closed with malignant progression. Moreover, Doxorubicin was identified by Connectivity Map website with the differential malignant progression-related genes. CCK-8 assay, Edu assay, wound healing assay, and trans-well experiment were used to reveal that Doxorubicin could inhibit proliferation, migration and invasion of IOMM-Lee Cells.

Neoplasms and tumor-like lesions of the sellar region: imaging findings with correlation to pathology and 2021 WHO classification

The sellar region represents a complex anatomical area, composed of multiple structures of different embryological derivation, including the skull base and the pituitary gland, along with vascular, nervous, and meningeal structures. Masses arising in this region include benign and malignant lesions arising from the pituitary gland itself, but also from vestigial embryological residues or surrounding tissues, that may require different therapeutic approaches. While assessing sellar region masses, the combination of clinical presentation and imaging features is fundamental to define hypotheses about their nature. MR represents the imaging modality of choice, providing information about the site of the lesion, its imaging features, and relation with adjacent structures, while CT is useful to confirm the presence of lesion calcifications or to reveal tumor invasion of bony structures. The aim of this pictorial review is to provide an overview of the common neoplasms and tumor-like conditions of the sellar region, according to the 2021 WHO Classification of Tumors of the Central Nervous System (fifth edition), with an emphasis on the radiologic-pathologic correlation. After a brief introduction on the anatomy of this region and the imaging and pathological techniques currently used, the most relevant MRI characteristics, clinical findings, and pathological data, including histologic and molecular features, will be shown and discussed, with the aim of facilitating an appropriate differential diagnosis among these entities.

Preoperative prediction and histological stratification of intracranial solitary fibrous tumours by machine-learning models

AIM

To explore the effectiveness and feasibility of machine-learning models based on magnetic resonance imaging (MRI) radiomics features in differentiating intracranial solitary fibrous tumour (ISFT) from angiomatous meningioma (AM) and stratifying ISFT histologically.

MATERIALS AND METHODS

This study retrospectively recruited 268 patients with a histological diagnosis of ISFT (n=120) or AM (n=148), and 116 of the ISFT patients were used for stratified analysis of histological grade. The radiomics features were extracted from axial T1-weighted imaging (WI), T2WI and contrast-enhanced T1WI sequences. All patients were assigned randomly to the training group and test group in a ratio of 7:3. The models were optimised by 10-fold cross-validation in the training group, and the independent test group was used for further testing of the models. The performances of machine-learning models based on radiomics, clinical, and fusion features in predicting and stratifying ISFT were evaluated.

RESULTS

ISFT and AM differed significantly in terms of age, tumour shape, enhancement pattern, and margin. There was no significant difference in the clinical characteristics between World Health Organization (WHO) grade II and WHO grade III ISFT. When used to differentiate ISFT from AM, the area under the curve (AUC) values of the machine-learning models based on radiomics, clinical, and fusion features in the test group were 0.917, 0.923 and 0.950, respectively. When used for histological stratification of ISFT, the model based on the radiomics signature achieved an AUC value of 0.786 in the test group.

CONCLUSIONS

Machine-learning models can contribute in the prediction and histological stratification of ISFT non-invasively, which can help clinical differential diagnosis and treatment decisions.

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen's Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

A Case of Giant Cutaneous Lopez Type III Meningioma of the Scalp

Meningiomas are the most common central nervous system (CNS) tumors. Extracranial meningiomas are rare, constituting 2% of all meningiomas. We describe a case of Lopez type III meningioma of the scalp in a 72-year-old gentleman who had a long-standing giant scalp mass and presented with recent mild left-sided limb weakness and numbness. Magnetic resonance imaging (MRI) of the skull demonstrated a right frontoparietal tumor extending through the skull into the scalp. Tumor excision revealed World Health Organization (WHO) grade 1 meningioma. Clinicians should correlate a cutaneous skull mass and new onset of neurological symptoms. Cutaneous meningioma is an important differential diagnosis.

Meningioma and Other Meningeal Tumors

Meningiomas develop from meningothelial cells and approximately account for more than 30 percent of central nervous system (CNS) tumors. They can occur anywhere in the dura, most often intracranially and at dural reflection sites. Half of the cases are usually at parasagittal/falcine and convexity locations; other common sites are sphenoid ridge, suprasellar, posterior fossa, and olfactory groove. The female-to-male ratio is approximately 2 or 3-1, and the median age at diagnosis is 65 years. Meningiomas are generally extremely slow-growing tumors; many are asymptomatic or paucisymptomatic at diagnosis and are discovered incidentally. Clinical manifestations, when present, are influenced by the tumor site and by the time course over which it develops. Meningiomas are divided into three grades. Grade I represents the vast majority of cases; they are considered typical or benign, although their CNS location can still lead to severe morbidity or mortality, resulting in a reported ten-year net survival of over 80%. Atypical (WHO grade II) meningiomas are considered "intermediate grade" malignancies and represent 5-7% of cases. They show a tendency for recurrence and malignant degeneration with a relevant increase in tumor cell migration and surrounding tissue infiltration; ten-year net survival is reported over 60%. The anaplastic subtype (WHO III) represents only 1-3% of cases, and it is characterized by a poor prognosis (ten-year net survival of 15%). The treatment of choice for these tumors stands on complete microsurgical resection in case the subsequent morbidities are assumed minimal. On the other hand, and in case the tumor is located in critical regions such as the skull base, or the patient may have accompanied comorbidities, or it is aimed to avoid intensive treatment, some other approaches, including stereotactic radiosurgery and radiotherapy, were recommended as safe and effective choices to be considered as a primary treatment option or complementary to surgery. Adjuvant radiosurgery/radiotherapy should be considered in the case of atypical and anaplastic histology, especially when a residual tumor is identifiable in postoperative imaging. A "watchful waiting" strategy appears reasonable for extremely old individuals and those with substantial comorbidities or low-performance status, while there is a reduced threshold for therapeutic intervention for relatively healthy younger individuals due to the expectation that tumor progression will inevitably necessitate proactive treatment. To treat and manage meningioma efficiently, the assessments of both neurosurgeons and radiation oncologists are essential. The possibility of other rarer tumors, including hemangiopericytomas, solitary fibrous tumors, lymphomas, metastases, melanocytic tumors, and fibrous histiocytoma, must be considered when a meningeal lesion is diagnosed, especially because the ideal diagnostic and therapeutic approaches might differ significantly in every tumor type.

Type IV collagen-derived angiogenesis inhibitor: canstatin low expressing in brain-invasive meningiomas using liquid chromatography-mass spectrometry (LC-MS/MS)

PURPOSE

Brain invasion in meningiomas is considered an indicator of more aggressive behavior and worse prognosis. But the precise definition and the prognostic role of brain invasion remains unsolved duo to lacking a standardized workflow of surgical sampling and the histopathological detection. Searching for molecular biomarker expression correlating with brain invasion, could contribute to establish a molecular pathological diagnosis without problems of subjective interobserver variation and deeply understand the mechanism of brain invasion and develop innovative therapeutic strategies.

METHODS

We utilized liquid chromatography tandem mass spectrometry to quantify protein abundances between non-invasive meningiomas (n = 21) and brain-invasive meningiomas (n = 21) spanning World Health Organization grades I and III. After proteomic discrepancies were analyzed, the 14 most up-regulated or down-regulated proteins were recorded. Immunohistochemical staining for glial fibrillary acidic protein and most likely brain invasion-related proteins was performed in both groups.

RESULTS

A total of 6498 unique proteins were identified in non-invasive and brain-invasive meningiomas. Canstatin expression in the non-invasive group was 2.1-fold that of the brain-invasive group. The immunohistochemical staining showed canstatin expressed in both groups, and the non-invasive group showed stronger staining for canstatin in the tumor mass (p = 0.0132) than the brain-invasive group, which showed moderate intensity.

CONCLUSION

This study demonstrated the low expression of canstatin in meningiomas with brain invasion, a finding that provide a basis for understanding the mechanism of brain invasion of meningiomas and may contribute to establish molecular pathological diagnosis and identify novel therapeutic targets for personalized care.

Advances in Central Nervous System Tumor Classification

Historically, the classification of tumors of the central nervous system (CNS) relies on the histologic appearance of cells under a microscope; however, the molecular era of medicine has resulted in new diagnostic paradigms anchored in the intrinsic biology of disease. The 2021 World Health Organization (WHO) reformulated the classification of CNS tumors to incorporate molecular parameters, in addition to histology, to define many tumor types. A contemporary classification system with integrated molecular features aims to provide an unbiased tool to define tumor subtype, the risk of tumor progression, and even the response to certain therapeutic agents. Meningiomas are heterogeneous tumors as depicted by the current 15 distinct variants defined by histology in the 2021 WHO classification, which also incorporated the first moelcular critiera for meningioma grading: homozygous loss of CDKN2A/B and TERT promoter mutation as criteria for a WHO grade 3 meningioma. The proper classification and clinical management of meningioma patients requires a multidisciplinary approach, which in addition to the information on microscopic (histology) and macroscopic (Simpson grade and imaging), should also include molecular alterations. In this chapter, we present the most up-to-date knowledge in CNS tumor classification, particularly in meningioma, in the molecular era and how it could affect their future classification and clinical management of patients with these diseases.

Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics

Meningiomas are one of the most common intracranial primary central nervous system tumors. Regardless of the pathological grading and histological subtypes, maximum safe resection is the recommended treatment option for meningiomas. However, considering tumor heterogeneity, surgical treatment options and prognosis often vary greatly among meningiomas. Therefore, an accurate preoperative surgical risk assessment of meningiomas is of great clinical importance as it helps develop surgical treatment strategies and improve patient prognosis. In recent years, an increasing number of studies have proved that magnetic resonance imaging (MRI) radiomics has wide application values in the diagnostic, identification, and prognostic evaluations of brain tumors. The vital importance of MRI radiomics in the surgical risk assessment of meningiomas must be apprehended and emphasized in clinical practice. This narrative review summarizes the current research status of MRI radiomics in the preoperative surgical risk assessment of meningiomas, focusing on the applications of MRI radiomics in preoperative pathological grading, assessment of surrounding tissue invasion, and evaluation of tumor consistency. We further analyze the prospects of MRI radiomics in the preoperative assessment of meningiomas angiogenesis and adhesion with surrounding tissues, while pointing out the current challenges of MRI radiomics research.

Clinical Management of Supratentorial Non-Skull Base Meningiomas

Supratentorial non-skull base meningiomas are the most common primary central nervous system tumor subtype. An understanding of their pathophysiology, imaging characteristics, and clinical management options will prove of substantial value to the multi-disciplinary team which may be involved in their care. Extensive review of the broad literature on the topic is conducted. Narrowing the scope to meningiomas located in the supratentorial non-skull base anatomic location highlights nuances specific to this tumor subtype. Advances in our understanding of the natural history of the disease and how findings from both molecular pathology and neuroimaging have impacted our understanding are discussed. Clinical management and the rationale underlying specific approaches including observation, surgery, radiation, and investigational systemic therapies is covered in detail. Future directions for probable advances in the near and intermediate term are reviewed.

Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas

PURPOSE

The 2021 World Health Organization classification includes telomerase reverse transcriptase promoter (TERTp) mutation status as a factor for differentiating meningioma grades. Therefore, preoperative prediction of TERTp mutation may assist in clinical decision making. However, no previous study has applied fractal analysis for TERTp mutation status prediction in meningiomas. The purpose of this study was to assess the utility of three-dimensional (3D) fractal analysis for predicting the TERTp mutation status in grade 2 meningiomas.

METHODS

Forty-eight patients with surgically confirmed grade 2 meningiomas (41 TERTp-wildtype and 7 TERTp-mutant) were included. 3D fractal dimension (FD) and lacunarity values were extracted from the fractal analysis. A predictive model combining clinical, conventional, and fractal parameters was built using logistic regression analysis. Receiver operating characteristic curve analysis was used to assess the ability of the model to predict TERTp mutation status.

RESULTS

Patients with TERTp-mutant grade 2 meningiomas were older (P = 0.029) and had higher 3D FD (P = 0.026) and lacunarity (P = 0.004) values than patients with TERTp-wildtype grade 2 meningiomas. On multivariable logistic analysis, higher 3D FD values (odds ratio = 32.50, P = 0.039) and higher 3D lacunarity values (odds ratio = 20.54, P = 0.014) were significant predictors of TERTp mutation status. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.84 (95% confidence interval 0.71-0.93), 83.3%, 71.4%, and 85.4%, respectively.

CONCLUSION

3D FD and lacunarity may be useful imaging biomarkers for predicting TERTp mutation status in grade 2 meningiomas.

The impact of brain invasion criteria on the incidence and distribution of WHO grade 1, 2, and 3 meningiomas

BACKGROUND

In 2016 brain invasion was added as a standalone diagnostic criterion for Grade 2 meningiomas in the WHO Classification of Brain Tumors. The aim of this study was to compare the incidence and distribution of meningiomas, and agreement, between the 2007 and 2016 WHO criteria.

METHODS

All cases of intracranial meningiomas diagnosed between 2007 and 2020 at a tertiary care academic hospital were identified. The incidence of each meningioma grade in the WHO 2007 and WHO 2016 cohorts were compared. Additionally, each case in the 2007 cohort was re-graded according to the WHO 2016 criteria to determine the intra-class correlation (ICC) between criteria.

RESULTS

Of 814 cases, 532 (65.4%) were in the 2007 WHO cohort and 282 (34.6%) were in the 2016 WHO cohort. There were no differences in the distribution of meningioma grades between cohorts (P = .11). Incidence rates were: 75.0% vs. 75.2% for Grade 1, 22.7% vs. 24.5% for Grade 2, and 2.3% vs. 0.4% for Grade 3, for the 2007 and 2016 cohorts, respectively. Upon re-grading, 21 cases (3.9%) were changed. ICC between original and revised grade was 0.92 (95% CI: 0.91-0.93). Amongst Grade 2 meningiomas with brain invasion, 75.8% had three or more atypical histologic features or an elevated mitotic index.

CONCLUSIONS

Including brain invasion as a standalone diagnostic criterion for Grade 2 meningiomas had minimal impact on the incidence of specific meningioma grade tumors. There is strong agreement between the 2007 and 2016 WHO criteria, likely due to cosegregation of grade elevating features.

The Complementary Role of 68Ga-DOTATATE PET/CT in Diagnosis of Recurrent Meningioma

Introduction: Contrast-enhanced brain MRI is the choice of imaging modality in diagnosis and posttreatment evaluation, its role is limited in distinguishing recurrent lesion from postoperative change. 68Ga-DOTATATE is a somatostatin analog PET tracer which has high affinity to meningioma expressing somatostatin receptor. Methods and subjects: In this case series review, we described 8 patients with brain MRI suspected of recurrent meningioma who underwent focused 68Ga-DOTATATE PET/CT scan for radiation treatment planning. Results: The combined brain MRI and PET/CT allowed improved conspicuity of the lesions and aided radiation treatment planning. The time from the initial surgery to PET/CT scans varied widely ranging from 1 year to 12 years. Three patients had PET/CT shortly after the initial surgery (1-3 years) and underwent targeted radiation therapy. Subsequent imaging showed no evidence of recurrence. Four patients had prolonged time between the PET/CT and the initial surgery (7-12 years) which showed extensive tumor burden. All four patients expired shortly after the last PET/CT scan. Conclusion: 68Ga-DOTATATE PET shows promising complementary role in detection and treatment planning of recurrent meningioma.

Molecular determinants of outcomes in meningiomas

Meningiomas are the most common intracranial primary tumor in adults. Surgery is the predominant therapeutic modality for symptomatic meningiomas. Although the majority of meningiomas are benign, there exists a subset of meningiomas that are clinically aggressive. Recent advances in genetics and epigenetics have uncovered molecular alterations that drive tumor meningioma biology with prognostic and therapeutic implications. In this review, we will discuss the advances on molecular determinants of therapeutic response in meningiomas to date and discuss findings of targeted therapies in meningiomas.

Development of an Image-Based Methodology for the Evaluation of Histopathological Features in Human Meningioma

Meningioma is the most common intracranial tumor in adulthood. With a clear female predominance and a recurrence rate that reaches 20%, it is, despite being considered a benign tumor, a pathology that greatly compromises post-diagnosis quality of life. Its prone to recur or progress to a higher degree is difficult to predict in the absence of obvious histological criteria. This project aims to develop an automatic methodology to aid in the diagnosis of meningiomas that is objective and easily reproducible. The methodology is based on histopathological image analysis using artificial intelligence and machine learning algorithms. It includes a semi-automatic process of identification and cleaning of the scanned samples, an automatic detection of the nuclei of each image and, finally, the parameterization of the samples. The obtained data together with the clinical information will be analyzed using statistical methods in order to provide a methodology to support clinical diagnosis and decision-making in patient management. The result is the development of an effective methodology that generates a set of data associated with morphological parameters with different trends according to the pathological groups studied. A tool has been developed that allows an effective semiautomatic analysis of the images to evaluate these parameters in an objective and reproducible way, helping in clinical decision-making and facilitating to undertake projects with large sample series. Clinical Relevance- The main contribution of this project is in the field of neuropathology, for the diagnosis of meningiomas, the most common brain tumor. The present project provides an objective and quantifiable prognosis methodology for the meningiomas, offering a more precise monitoring of the treatment applied to the patient, resulting in a better prognosis and better quality of life.

Clinical Significance of Molecular Alterations and Systemic Therapy for Meningiomas: Where Do We Stand?

Meningiomas are common intracranial tumors that can be treated successfully in most cases with surgical resection and/or adjuvant radiotherapy. However, approximately 20% of patients show an aggressive clinical course with tumor recurrence or progressive disease, resulting in significant morbidity and increased mortality. Despite several studies that have investigated different cytotoxic agents in aggressive meningiomas in the past several years, limited evidence of efficacy and clinical benefit has been reported thus far. Novel molecular alterations have been linked to a particular clinicopathological phenotype and have been correlated with grading, location, and prognosis of meningiomas. In this regard, SMO, AKT, and PIK3CA mutations are typical of anterior skull base meningiomas, whereas KLF4 mutations are specific for secretory histology, and BAP1 alterations are common in progressive rhabdoid meningiomas. Alterations in TERT, DMD, and BAP1 correlate with poor outcomes. Moreover, some actionable mutations, including SMO, AKT1, and PIK3CA, regulate meningioma growth and are under investigation in clinical trials. PD-L1 and/or M2 macrophage expression in the microenvironment provides evidence for the investigation of immunotherapy in progressive meningiomas.

Cycle-consistent adversarial networks improves generalizability of radiomics model in grading meningiomas on external validation

The heterogeneity of MRI is one of the major reasons for decreased performance of a radiomics model on external validation, limiting the model’s generalizability and clinical application. We aimed to establish a generalizable radiomics model to predict meningioma grade on external validation through leveraging Cycle-Consistent Adversarial Networks (CycleGAN). In this retrospective study, 257 patients with meningioma were included in the institutional training set. Radiomic features (n = 214) were extracted from T2-weighted (T2) and contrast-enhanced T1 (T1C) images. After radiomics feature selection, extreme gradient boosting classifiers were developed. The models were validated in the external validation set consisting of 61 patients with meningiomas. To reduce the gap in generalization associated with the inter-institutional heterogeneity of MRI, the smaller image set style of the external validation was translated into the larger image set style of the institutional training set using CycleGAN. On external validation before CycleGAN application, the performance of the combined T2 and T1C models showed an area under the curve (AUC), accuracy, and F1 score of 0.77 (95% confidence interval 0.63–0.91), 70.7%, and 0.54, respectively. After applying CycleGAN, the performance of the combined T2 and T1C models increased, with an AUC, accuracy, and F1 score of 0.83 (95% confidence interval 0.70–0.97), 73.2%, and 0.59, respectively. Quantitative metrics (by Fréchet Inception Distance) showed that CycleGAN can decrease inter-institutional image heterogeneity while preserving predictive information. In conclusion, leveraging CycleGAN may be helpful to increase the generalizability of a radiomics model in differentiating meningioma grade on external validation.

Glutamine anaplerosis is required for amino acid biosynthesis in human meningiomas

BACKGROUND

We postulate that meningiomas undergo distinct metabolic reprogramming in tumorigenesis and unraveling their metabolic phenotypes provide new therapeutic insights. Glutamine catabolism is key to the growth and proliferation of tumors. Here, we investigated the metabolomics of freshly resected meningiomas and glutamine metabolism in patient-derived meningioma cells.

METHODS

1H NMR spectroscopy of tumor tissues from meningioma patients was used to differentiate the metabolite profiles of grade-I and grade-II meningiomas. Glutamine metabolism was examined using 13C/15N glutamine tracer, in 5 patient-derived meningioma cells.

RESULTS

Alanine, lactate, glutamate, glutamine, and glycine were predominantly elevated only in grade-II meningiomas by 74%, 76%, 35%, 75%, and 33%, respectively, with alanine and glutamine levels being statistically significant (P ≤ .02). 13C/15N glutamine tracer experiments revealed that both grade-I and -II meningiomas actively metabolize glutamine to generate various key carbon intermediates including alanine and proline that are necessary for the tumor growth. Also, it is shown that glutaminase (GLS1) inhibitor, CB-839 is highly effective in downregulating glutamine metabolism and decreasing proliferation in meningioma cells.

CONCLUSION

Alanine and glutamine/glutamate are mainly elevated in grade-II meningiomas. Grade-I meningiomas possess relatively higher glutamine metabolism providing carbon/nitrogen for the biosynthesis of key nonessential amino acids. GLS1 inhibitor (CB-839) is very effective in downregulating glutamine metabolic pathways in grade-I meningiomas leading to decreased cellular proliferation.

Use of advanced neuroimaging and artificial intelligence in meningiomas

Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.

Prognostic impact of genetic alterations and methylation classes in meningioma

Meningiomas are classified based on histological features, but genetic and epigenetic features are emerging as relevant biomarkers for outcome prediction and may supplement histomorphological evaluation. We investigated meningioma‐relevant mutations and their correlation with DNA methylation clusters and patient survival times. Formalin‐fixed and paraffin‐embedded samples of 126 meningioma patients (WHO grade I 52/126; 41.3%; WHO grade II: 48/126; 38.1%; WHO grade III: 26/126; 20.6%) were investigated. We analyzed NF2, TRAF7, KLF4, ARID, SMO, AKT,TERT promotor, PIK3CA, and SUFU mutations using panel sequencing and correlated them to DNA methylation classes (MC) determined using 850k EPIC arrays. The TRAKL mutation genotype was characterized by the presence of any of the following mutations: TRAF7, AKT1, and KLF4. Survival data including progression‐free survival (PFS) and overall survival (OS) was retrieved from chart review. Mutations were evident in 90/126 (71.4%) specimens with mutations in NF2 (39/126; 31.0%), TRAF7 (39/126; 31.0%) and KLF4 (25/126; 19.8%) being the most frequent ones. Two or more mutations were observed in 35/126 (27.8%) specimens. While TRAKL was predominantly found in benign MC, NF2 was associated with malign MC (p < 0.05). TRAF7, KLF4, and TRAKL mutation genotype were associated with improved PFS and OS (p < 0.05). TERT promotor methylation, intermediate, and malign MC were associated with impaired PFS and OS (p < 0.05). Methylation cluster showed better prognostic discrimination for PFS and OS (c‐index 0.77/0.75) than each of the individual mutations (c‐index 0.63/0.68). In multivariate analysis correcting for age, gender, MC, and WHO grade, none of the individual mutations except TERT remained an independent significant prognostic factor for PFS. Molecular profiling including mutational analysis and DNA methylation classification may facilitate more precise prognostic assessment and identification of potential targets for personalized therapy in meningioma patients.

Consensus core clinical data elements for meningiomas

BACKGROUND

With increasing molecular analyses of meningiomas, there is a need to harmonize language used to capture clinical data across centers to ensure that molecular alterations are appropriately linked to clinical variables of interest. Here the International Consortium on Meningiomas presents a set of core and supplemental meningioma-specific Common Data Elements (CDEs) to facilitate comparative and pooled analyses.

METHODS

The generation of CDEs followed the four-phase process similar to other National Institute of Neurological Disorders and Stroke (NINDS) CDE projects: discovery, internal validation, external validation, and distribution.

RESULTS

The CDEs were organized into patient- and tumor-level modules. In total, 17 core CDEs (10 patient-level and 7-tumour-level) as well as 14 supplemental CDEs (7 patient-level and 7 tumour-level) were defined and described. These CDEs are now made publicly available for dissemination and adoption.

CONCLUSIONS

CDEs provide a framework for discussion in the neuro-oncology community that will facilitate data sharing for collaborative research projects and aid in developing a common language for comparative and pooled analyses. The meningioma-specific CDEs presented here are intended to be dynamic parameters that evolve with time and The Consortium welcomes international feedback for further refinement and implementation of these CDEs.

A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features

Background

Brain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI.

Methods

One hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed.

Results

Traditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set).

Conclusions

This study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.

Intrameningioma metastasis: A case-based literature review

A tumor-to-tumor metastasis inside a meningioma is a rare phenomenon. Malignant neoplasms of the breast and lung are the most common primary tumors. Other sites of origin include prostate, renal and gastric neoplasms. The included case files were retrieved from the medical records of the University Hospital of Crete, Greece. A review of the literature was conducted in March 2020 via PubMed. Relevant search results were few. We report a case of a 66-year-old female, with known Small Cell Lung Cancer, who presented with left-sided hemiparesis. The Magnetic Resonance Imaging scan revealed a right frontal extra-axial mass. The patient underwent a craniotomy and a gross total removal of the tumor. Histological examination of the excised mass revealed metastatic adenocarcinoma deposits inside a meningioma: tumor-to-tumor metastasis. Reviewing the available literature, it has been hypothesized that the following factors play a role in the pathophysiology of this phenomenon: progesterone and estrogen receptors, cell-to-cell adhesion molecules, rich vascularization, favorable metabolic, micro-and immunological environment. Meningiomas seem to be the most common type of intracranial neoplasm to host a metastasis. There is a difference between tumor-to-tumor metastasis and collision tumors. The former implies a recipient role of the host tumor, and the latter refers to a co-localization of two different tumors that grow into one another, both being in the same organ. Tumor-to-tumor brain metastasis is a well-described phenomenon but with unclear pathophysiology. Deeper knowledge could be beneficial for its management.

Cervical intra-extradural meningioma with en-plaque, dumbbell-shaped, and an unusual calcified pattern in a young patient

Background:

Most spinal meningiomas primarily grow in the intradural extramedullary location. Epidural meningiomas are uncommon; if detected, they usually coexist with intradural lesions. They inhere more aggressive and invasive characteristics compared with their counterparts inside the dura.

Case Description:

We report a 22-year-old female who was admitted to the hospital with weakness and numbness in both lower limbs. Her cervical magnetic resonance imaging revealed an en-plaque and dumbbell-shaped lesion located from C5 to C8. After gadolinium injection, the whole mass was enhanced and unveiled two portions: intradural and extradural. The bone window of the computed tomography scan revealed calcification inside the lesion. The patient underwent tumor removal surgery. The pathology findings showed a psammomatous meningioma. After 6 months of surgery, the patient has been able to walk with walkers.

Conclusion:

We should consider spinal meningioma as a differential diagnosis when encountering an extradural lesion in the cervical region. The optimal surgical treatment for young patient with epidural meningiomas is radical surgery with dura attachment removal.

Diagnostic nomogram model for predicting preoperative pathological grade of meningioma

Background

Meningioma is the most common primary tumor of the central nervous system. Preoperative diagnosis of high-grade meningioma is helpful for the selection of treatment options. The aim of our study is to establish a diagnostic nomogram model for preoperative prediction of the pathological grade of meningioma.

Methods

The predictive model was established from a cohort of 215 clinicopathologically confirmed meningioma between January 2012 and December 2017. Radiomic features were collected from preoperative magnetic resonance imaging (MRI) and computed tomography of patients with meningioma. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction and feature selection. Multivariate logistic regression was used to build a predictive model and presented as a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated using bootstrapping validation.

Results

High-grade meningioma was observed in 47 patients (22%). The predictors included in the nomogram were tumor-brain interface, bone invasion, and tumor location. The final diagnostic model exhibited good calibration and discrimination with a C-index of 0.874 (95% confidence interval: 0.818–0.929) and a higher C-index of 0.868 in internal validation. Decision curve analysis (DCA) indicated that the nomogram is very useful in clinical practice.

Conclusions

This study provides a nomogram model with tumor-brain interface, bone invasion, and tumor location that can effectively predict the preoperative pathological grading of patients with meningioma and thus help clinicians provide more reasonable treatment strategies for meningioma patients.

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning model and its gap from the test performance under different conditions, using real-world brain tumor radiomics data. We conducted two classification tasks of different difficulty levels with magnetic resonance imaging (MRI) radiomics features: (1) "Simple" task, glioblastomas [n = 109] vs. brain metastasis [n = 58] and (2) "difficult" task, low- [n = 163] vs. high-grade [n = 95] meningiomas. Additionally, two undersampled datasets were created by randomly sampling 50% from these datasets. We performed random training-test set splitting for each dataset repeatedly to create 1,000 different training-test set pairs. For each dataset pair, the least absolute shrinkage and selection operator model was trained and evaluated using various validation methods in the training set, and tested in the test set, using the area under the curve (AUC) as an evaluation metric. The AUCs in training and testing varied among different training-test set pairs, especially with the undersampled datasets and the difficult task. The mean (±standard deviation) AUC difference between training and testing was 0.039 (±0.032) for the simple task without undersampling and 0.092 (±0.071) for the difficult task with undersampling. In a training-test set pair with the difficult task without undersampling, for example, the AUC was high in training but much lower in testing (0.882 and 0.667, respectively); in another dataset pair with the same task, however, the AUC was low in training but much higher in testing (0.709 and 0.911, respectively). When the AUC discrepancy between training and test, or generalization gap, was large, none of the validation methods helped sufficiently reduce the generalization gap. Our results suggest that machine learning after a single random training-test set split may lead to unreliable results in radiomics studies especially with small sample sizes.

Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method

The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.

Loss of H3K27me3 in meningiomas

BACKGROUND

There is a critical need for objective and reliable biomarkers of outcome in meningiomas beyond WHO classification. Loss of H3K27me3 has been reported as a prognostically unfavorable alteration in meningiomas. We sought to independently evaluate the reproducibility and prognostic value of H3K27me3 loss by immunohistochemistry (IHC) in a multicenter study.

METHODS

IHC staining for H3K27me3 and analyses of whole slides from 181 meningiomas across three centers was performed. Staining was analyzed by dichotomization into loss and retained immunoreactivity, and using a 3-tiered scoring system in 151 cases with clear staining. Associations of grouping with outcome were performed using Kaplan-Meier survival estimates.

RESULTS

A total of 21 of 151 tumors (13.9%) demonstrated complete loss of H3K27me3 staining in tumor with retained endothelial staining. Overall, loss of H3K27me3 portended a worse outcome with shorter times to recurrence in our cohort, particularly for WHO grade 2 tumors which were enriched in our study. There were no differences in recurrence-free survival (RFS) for WHO grade 3 patients with retained vs loss of H3K27me3. Scoring by a 3-tiered system did not add further insights into the prognostic value of this H3K27me3 loss. Overall, loss of H3K27me3 was not independently associated with RFS after controlling for WHO grade, extent of resection, sex, age, and recurrence status of tumor on multivariable Cox regression analysis.

CONCLUSIONS

Loss of H3K27me3 identifies a subset of WHO grade 2 and possibly WHO grade 1 meningiomas with increased recurrence risk. Pooled analyses of a larger cohort of samples with standardized reporting of clinical definitions and staining patterns are warranted.

PET Imaging of Meningioma Using the Novel SSTR-Targeting Peptide 18F-SiTATE

ABSTRACT

PET using 68Ga-labeled somatostatin receptor (SSTR) ligands adds significant information in meningioma patients. 18F-SiTATE is a novel, 18F-labeled SSTR-targeting peptide with remarkable imaging properties. Here, we present a 72-year-old woman with falx meningioma and transosseous extension. 18F-SiTATE PET/CT was performed 12 months after the previous 68Ga-DOTATOC PET/CT with comparable quantitative uptake and very good spatial resolution. So far, the widespread use of SSTR ligands for NET and meningioma imaging is hampered by cost-intensive 68Ge/68Ga generators, low activity amounts, lower spatial resolution, and short half-life. 18F-SiTATE might foster widespread use of SSTR ligands, overcoming the shortcomings of 68Ga-labeled ligands.