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Background Atypical meningiomas are an intermediate grade brain tumour with a recurrence rate of 39–58 %. It is not known whether early adjuvant radiotherapy reduces the risk of tumour recurrence and whether the potential side-effects are justified. An alternative management strategy is to perform active monitoring with magnetic resonance imaging (MRI) and to treat at recurrence. There are no randomised controlled trials comparing these two approaches. Methods/Design A total of 190 patients will be recruited from neurosurgical/neuro-oncology centres across the United Kingdom, Ireland and mainland Europe. Adult patients undergoing gross total resection of intracranial atypical meningioma are eligible. Patients with multiple meningioma, optic nerve sheath meningioma, previous intracranial tumour, previous cranial radiotherapy and neurofibromatosis will be excluded. Informed consent will be obtained from patients. This is a two-stage trial (both stages will run in parallel): Stage 1 (qualitative study) is designed to maximise patient and clinician acceptability, thereby optimising recruitment and retention. Patients wishing to continue will proceed to randomisation. Stage 2 (randomisation) patients will be randomised to receive either early adjuvant radiotherapy for 6 weeks (60 Gy in 30 fractions) or active monitoring. The primary outcome measure is time to MRI evidence of tumour recurrence (progression-free survival (PFS)). Secondary outcome measures include assessing the toxicity of the radiotherapy, the quality of life, neurocognitive function, time to second line treatment, time to death (overall survival (OS)) and incremental cost per quality-adjusted life year (QALY) gained. Discussion ROAM/EORTC-1308 is the first multi-centre randomised controlled trial designed to determine whether early adjuvant radiotherapy reduces the risk of tumour recurrence following complete surgical resection of atypical meningioma. The results of this study will be used to inform current neurosurgery and neuro-oncology practice worldwide. Trial registration ISRCTN71502099 on 19 May 2014.

Purpose Atypical meningioma (AM) recurs in up to half of patients after surgical resection and may require adjuvant therapy to improve patient prognosis. Various clinicopathological features have been shown to have prognostic implications in AM, but an integrated prediction model is lacking. Thus, in this study, we aimed to develop and validate an integrated prognostic model for AM. Methods A retrospective cohort of 528 adult AM patients surgically treated at our institution were randomly assigned to a training or validation group in a 7:3 ratio. Sixteen baseline demographic, clinical, and pathological parameters, progression-free survival (PFS), and overall survival (OS) were analysed. Sixty-five combinations of machine learning (ML) algorithms were used for model training and validation to predict tumour recurrence and patient mortality. Results The random survival forest (RSF) model was the best model for predicting recurrence and death. Primary or secondary tumour, Ki-67 index, extent of resection, tumour size, brain involvement, tumour necrosis, and age contributed significantly to the model. The C-index value of the RSF recurrence prediction model reached 0.8080. The AUCs for 1-, 3-, and 5-year PFS were 0.83, 0.82, and 0.86, respectively. The C-index value of the RSF death prediction model reached 0.8890. The AUCs for 3-year and 5-year OS were 0.88 and 0.89, respectively. Conclusion A high-performing integrated RSF predictive model for AM recurrence and patient mortality was proposed that may guide therapeutic decision-making and long-term monitoring.

Introduction Our purpose was to determine whether perfusion MR imaging can be used to differentiate benign and malignant meningiomas on the basis of the differences in perfusion of tumor parenchyma and/or peritumoral edema. Methods A total of 33 patients with preoperative meningiomas (25 benign and 8 malignant) underwent conventional and dynamic susceptibility contrast perfusion MR imaging. Maximal relative cerebral blood volume (rCBV) and the corresponding relative mean time to enhance (rMTE) (relative to the contralateral normal white matter) in both tumor parenchyma and peritumoral edema were measured. The independent samples t -test was used to determine whether there was a statistically significant difference in the mean rCBV and rMTE ratios between benign and malignant meningiomas. Results The mean maximal rCBV values of benign and malignant meningiomas were 7.16±4.08 (mean±SD) and 5.89±3.86, respectively, in the parenchyma, and 1.05±0.96 and 3.82±1.39, respectively, in the peritumoral edema. The mean rMTE values were 1.16±0.24 and 1.30±0.32, respectively, in the parenchyma, and 0.91±0.25 and 1.24±0.35, respectively, in the peritumoral edema. The differences in rCBV and rMTE values between benign and malignant meningiomas were not statistically significant ( P >0.05) in the parenchyma, but both were statistically significant ( P <0.05) in the peritumoral edema. Conclusion Perfusion MR imaging can provide useful information on meningioma vascularity which is not available from conventional MRI. Measurement of maximal rCBV and corresponding rMTE values in the peritumoral edema is useful in the preoperative differentiation between benign and malignant meningiomas.

Meningiomas are the most common primary intracranial tumor. Important advances are occurring in meningioma research. These are expected to accelerate, potentially leading to impactful changes on the management of meningiomas in the near and medium term. This review will cover the histo- and molecular pathology of meningiomas, including recent 2016 updates to the WHO classification of CNS tumors. We will discuss clinical and radiographic presentation and therapeutic management. Surgery and radiotherapy, the two longstanding primary therapeutic modalities, will be discussed at length. In addition, data from prior and ongoing investigations of other treatment modalities, including systemic and targeted therapies, will be covered. This review will quickly update the reader on the contemporary management and future directions in meningiomas.

Although meningiomas are the most common intracranial tumours, the level of evidence to provide recommendations for the diagnosis and treatment of meningiomas is low compared with other tumours such as high-grade gliomas. The meningioma task force of the European Association of Neuro-Oncology (EANO) assessed the scientific literature and composed a framework of the best possible evidence-based recommendations for health professionals. The provisional diagnosis of meningioma is mainly made by MRI. Definitive diagnosis, including histological classification, grading, and molecular profiling, requires a surgical procedure to obtain tumour tissue. Therefore, in many elderly patients, observation is the best therapeutic option. If therapy is deemed necessary, the standard treatment is gross total surgical resection including the involved dura. As an alternative, radiosurgery can be done for small tumours, or fractionated radiotherapy in large or previously treated tumours. Treatment concepts combining surgery and radiosurgery or fractionated radiotherapy, which enable treatment of the complete tumour volume with low morbidity, are being developed. Pharmacotherapy for meningiomas has remained largely experimental. However, antiangiogenic drugs, peptide receptor radionuclide therapy, and targeted agents are promising candidates for future pharmacological approaches to treat refractory meningiomas across all WHO grades.

The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M–mutant; RELA fusion–positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma—a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.

Background Incidental discovery accounts for 30% of newly-diagnosed intracranial meningiomas. There is no consensus on their optimal management. This review aimed to evaluate the outcomes of different management strategies for these tumors. Methods Using established systematic review methods, six databases were scanned up to September 2017. Pooled event proportions were estimated using a random effects model. Meta-regression of prognostic factors was performed using individual patient data. Results Twenty studies (2130 patients) were included. Initial management strategies at diagnosis were: surgery (27.3%), stereotactic radiosurgery (22.0%) and active monitoring (50.7%) with a weighted mean follow-up of 49.5 months (SD = 29.3). The definition of meningioma growth and monitoring regimens varied widely impeding relevant meta-analysis. The pooled risk of symptom development in patients actively monitored was 8.1% (95% CI 2.7–16.1). Associated factors were peritumoral edema (OR 8.72 [95% CI 0.35–14.90]) and meningioma diameter ≥ 3 cm (OR 34.90 [95% CI 5.17–160.40]). The pooled proportion of intervention after a duration of active monitoring was 24.8% (95% CI 7.5–48.0). Weighted mean time-to-intervention was 24.8 months (SD = 18.2). The pooled risks of morbidity following surgery and radiosurgery, accounting for cross-over, were 11.8% (95% CI 3.7–23.5) and 32.0% (95% CI 10.6–70.5) respectively. The pooled proportion of operated meningioma being WHO grade I was 94.0% (95% CI 88.2–97.9). Conclusion The management of incidental meningioma varies widely. Most patients who clinically or radiologically progressed did so within 5 years of diagnosis. Intervention at diagnosis may lead to unnecessary overtreatment. Prospective data is needed to develop a risk calculator to better inform management strategies.

Malignant meningiomas are a rare type of central nervous system tumor. Therefore, little is known about the best methods for treating malignant meningiomas and their prognostic factors. The aim of this study was to identify risk factors and develop a prognostic model for patients with malignant meningiomas. First, 2360 cases of malignant meningiomas from the Surveillance, Epidemiology, and End Results database were randomly divided into the primary and validation cohorts. Next, multivariate Cox regression identified several independent predictors of survival in malignant meningioma patients. These included patient demographics (age, gender, race), tumor features (laterality, size, stage), treatment timing (months from diagnosis to treatment), treatment modality, and tumor history (counts of both in situ/malignant and benign/borderline tumors), as well as year of diagnosis. Third, a nomogram was used to represent the prediction model, which was built based on independent predictors and optimized using the Akaike information criterion. Finally, we evaluated the predictive performance using the concordance index and receiver operating characteristic curve and clinical value using decision curve analysis. Notably, the strong discrimination ability of the model was demonstrated by the concordance index of the nomogram and the area under the receiver operating characteristic curve, both of which were between 0.7 and 0.8. The calibration plots in both cohorts showed high agreement between actual observation and nomogram prediction, and decision curve analysis demonstrated the significant clinical value of the nomogram. In conclusion, the nomogram is a practical and useful tool for assessing prognosis and determining effective treatment approaches.

The main goal of brain tumor surgery is to maximize tumor resection while preserving brain function. However, existing imaging and surgical techniques do not offer the molecular information needed to delineate tumor boundaries. We have developed a system to rapidly analyze and classify brain tumors based on lipid information acquired by desorption electrospray ionization mass spectrometry (DESI-MS). In this study, a classifier was built to discriminate gliomas and meningiomas based on 36 glioma and 19 meningioma samples. The classifier was tested and results were validated for intraoperative use by analyzing and diagnosing tissue sections from 32 surgical specimens obtained from five research subjects who underwent brain tumor resection. The samples analyzed included oligodendroglioma, astrocytoma, and meningioma tumors of different histological grades and tumor cell concentrations. The molecular diagnosis derived from mass-spectrometry imaging corresponded to histopathology diagnosis with very few exceptions. Our work demonstrates that DESI-MS technology has the potential to identify the histology type of brain tumors. It provides information on glioma grade and, most importantly, may help define tumor margins by measuring the tumor cell concentration in a specimen. Results for stereotactically registered samples were correlated to preoperative MRI through neuronavigation, and visualized over segmented 3D MRI tumor volume reconstruction. Our findings demonstrate the potential of ambient mass spectrometry to guide brain tumor surgery by providing rapid diagnosis, and tumor margin assessment in near–real time.