Predicting the risk of postoperative recurrence and high-grade histology in patients with intracranial meningiomas using routine preoperative MRI

Intratumoral and peritumoral radiomics model for the preoperative prediction of cribriform component in invasive lung adenocarcinoma: a multicenter study

PURPOSE

This study aimed to investigate the predictive value of intratumoral and peritumoral radiomics model for the cribriform component (CC) of invasive lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

The 144 patients with invasive LUAD from our center were randomly divided into training set (n = 100) and internal validation set (n = 44) in a ratio of 7:3, and 75 patients from center 2 were regarded as the external validation set. Clinical risk factors were examined using univariate and multivariate logistic regression to construct the clinical model. We extracted radiomics features from gross tumor volume (GTV), gross and peritumoral volume (GPTV), and peritumoral volume (PTV), respectively. Radiomics models were constructed with selected features. A combined model based on the optimal Radscore and clinically independent predictors was constructed, and its predictive performance was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

RESULTS

The area under curves (AUCs) of the GTV model were 0.882 (95% CI 0.817-0.948), 0.794 (95% CI 0.656-0.932), and 0.766 (95% CI 0.657-0.875) in the training, internal validation, and external validation sets, and the PTV model had AUCs of 0.812 (95% CI 0.725-0.899), 0.749 (95% CI 0.597-0.902), and 0.670 (95% CI 0.543-0.798) in the training, internal validation, and external validation sets, respectively. However, the GPTV radiomics model showed better predictive performance compared with the GTV and PTV radiomics models, with the AUCs of 0.950 (95% CI 0.911-0.989), 0.844 (95% CI 0.728-0.959), and 0.815 (95% CI 0.713-0.917) in the training, internal validation and external validation sets, respectively. In the clinical model, tumor shape, lobulation sign and maximal diameter were the independent predictors of CC in invasive LUAD. The combined model including independent clinical predictors and GPTV-Radscore show the considerable instructive to clinical practice, with the AUCs of 0.954(95% CI 0.918-0.990), 0.861(95% CI 0.752-0.970), and 0.794(95% CI 0.690-0.898) in training, internal validation, and external validation sets, respectively. DCA showed that the combined model had good clinical value and correction effect.

CONCLUSION

Radiomics model is a very powerful tool for predicting CC growth pattern in invasive LUAD and can help clinicians make the strategies of treatment and surveillance in patients with invasive LUAD.

Risk factors associated with higher WHO grade in meningiomas: a multicentric study of 552 skull base meningiomas

The histological grade is crucial for therapeutic management, and its reliable preoperative detection can significantly influence treatment approach. Lacking established risk factors, this study identifies preoperative predictors of high-grade skull base meningiomas and discusses the implications of non-invasive detection. A multicentric study was conducted on 552 patients with skull base meningiomas who underwent primary surgical resection between 2014 and 2019. Data were gathered from clinical, surgical and pathology records and radiological diagnostics. The predictive factors of higher WHO grade were analysed in univariate analysis and multivariate stepwise selection logistic regression analysis. Histological analysis revealed 511 grade 1 (92.6%) and 41 grade 2 (7.4%) meningiomas. A prognostic model predicting the probability of WHO grade 2 skull base meningioma (AUC 0.79; SE 0.04; 95% Wald Confidence Limits (0.71; 0.86)) based on meningioma diameter, presence of an arachnoid plane and cranial nerve palsy was built. Accurate preoperative detection of WHO grade in skull base meningiomas is essential for effective treatment planning. Our logistic regression model, based on diameter, cranial nerve palsy, and arachnoid plane, is tailored for detecting WHO grade 2 skull base meningiomas, even in outpatient settings.

Radiographic predictors of peritumoral brain edema in intracranial meningiomas: a review of current controversies and illustrative cases

Meningiomas are among the most common primary tumors of the central nervous system. In the past several decades, many researchers have emphasized the importance of radiographic findings and their possible role in predicting the various aspects of the meningioma biology. One of the factors most commonly analyzed with respect to the lesions' clinical behavior is peritumoral brain edema (PTBE), not only one of the most common signs associated with meningiomas, but also a significant clinical problem. Radiographic predictors of PTBE are usually noted as being the size of the tumor, its location, irregular margins, heterogeneity, and the peritumoral arachnoid plane with its pial vascular recruitment. Here, we review the available literature on the topic of these radiographic predictors of PTBE formation, we analyze the methodology of the research conducted, and we highlight the many controversies still present. Indeed, the evidence about PTBE pathogenesis, predictive factors, and clinical significance still seems to be mostly inconclusive, despite intense research in the area. We believe that by highlighting the many inconsistencies in the methodology used, we can showcase how little is actually known about the pathogenesis of PTBE, which in turn has important clinical implications. Additionally, we provide several MR images of intracranial meningiomas from our own practice which, we believe, showcase the unpredictable nature of PTBE, and demonstrate vividly the topics we discuss.

MRI features and tumor-infiltrating CD8 + T cells-based nomogram for predicting meningioma recurrence risk

OBJECTIVE

This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk.

METHODS

Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram.

RESULTS

The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively.

CONCLUSIONS

Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk.

CLINICAL RELEVANCE STATEMENT

The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.

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

BACKGROUND

While several risk factors for recurrences have been defined, the topographic pattern of meningioma recurrences after surgical resection has been scarcely investigated. The possibility of theoretically predicting the site of recurrence not only allows us to better understand the pathogenetic bases of the disease and consequently to drive the development of new targeted therapies, but also guides the decision-making process for treatment strategies and tailored follow-ups to decrease/prevent recurrence.

METHODS

The authors performed a comprehensive and detailed systematic literature review of the EMBASE and MEDLINE electronic online databases regarding the topographic pattern of recurrence after surgical treatment for intracranial meningiomas. Demographics and histopathological, neuroradiological and treatment data, pertinent to the topography of recurrences, as well as time to recurrences, were extracted and analyzed.

RESULTS

Four studies, including 164 cases of recurrences according to the inclusion criteria, were identified. All studies consider the possibility of recurrence at the previous dural site; three out of four, which are the most recent, consider 1 cm outside the previous dural margin to be the main limit to distinguish recurrences closer to the previous site from those more distant. Recurrences mainly occur within or close to the surgical bed; higher values of proliferation index are associated with recurrences close to the original site rather than within it.

CONCLUSIONS

Further studies, including genomic characterization of different patterns of recurrence, will better clarify the main features affecting the topography of recurrences. A comparison between topographic classifications of intracranial meningioma recurrences after surgery and after radiation treatment could provide further interesting information.

Progression/Recurrence of Meningioma: An Imaging Review Based on Magnetic Resonance Imaging

Meningiomas are the most common primary central nervous system tumors. The preferred treatment is maximum safe resection, and the heterogeneity of meningiomas results in a variable prognosis. Progression/recurrence (P/R) can occur at any grade of meningioma and is a common adverse outcome after surgical treatment and a major cause of postoperative rehospitalization, secondary surgery, and mortality. Early prediction of P/R plays an important role in postoperative management, further adjuvant therapy, and follow-up of patients. Therefore, it is essential to thoroughly analyze the heterogeneity of meningiomas and predict postoperative P/R with the aid of noninvasive preoperative imaging. In recent years, the development of advanced magnetic resonance imaging technology and machine learning has provided new insights into noninvasive preoperative prediction of meningioma P/R, which helps to achieve accurate prediction of meningioma P/R. This narrative review summarizes the current research on conventional magnetic resonance imaging, functional magnetic resonance imaging, and machine learning in predicting meningioma P/R. We further explore the significance of tumor microenvironment in meningioma P/R, linking imaging features with tumor microenvironment to comprehensively reveal tumor heterogeneity and provide new ideas for future research.

Achieving Gross Total Resection in Neurosurgery: A Review of Intraoperative Techniques and Their Influence on Surgical Goals

The definition of complete resection in neurosurgery depends on tumor type, surgical aims, and postoperative investigations, directly guiding the choice of intraoperative tools. Most common tumor types present challenges in achieving complete resection due to their infiltrative nature and anatomical constraints. The development of adjuvant treatments has altered the balance between oncological aims and surgical risks. We review local recurrence associated with incomplete resection based on different definitions and emphasize the importance of achieving maximal safe resection in all tumor types. Intraoperative techniques that aid surgeons in identifying tumor boundaries are used in practice and in preclinical or clinical research settings. They encompass both conservative and invasive techniques. Among them, morphological tools include imaging modalities such as intraoperative magnetic resonance imaging, ultrasound, and optical coherence tomography. Fluorescence-guided surgery, mainly using 5-aminolevulinic acid, enhances gross total resection in glioblastomas. Nuclear methods, including positron emission tomography probes, provide tumor detection based on beta or gamma emission after a radiotracer injection. Mass spectrometry- and spectroscopy-based methods offer molecular insights. The adoption of these techniques depends on their relevance, effectiveness, and feasibility. With the emergence of positron emission tomography imaging for use in recurrence benchmarking, positron emission tomography probes raise particular interest among those tools. While all such tools provide valuable insights, their clinical benefits need further evaluation.

Predicting histological grade in symptomatic meningioma by an objective estimation of the tumoral surface irregularity

INTRODUCTION

Predicting the histopathologic grade of meningioma is relevant because local recurrence is significantly greater in WHO grade II-III compared to WHO grade I tumours, which would ideally benefit from a more aggressive surgical strategy. It has been suggested that higher WHO grade tumours are more irregularly-shaped. However, irregularity is a subjective and observer-dependent feature. In this study, the tumour surface irregularity of a large series of meningiomas, measured upon preoperative MRI, is quantified and correlated with the WHO grade.

METHODS

Unicentric retrospective observational study of a cohort of symptomatic meningiomas surgically removed in the time period between January 2015 and December 2022. Using specific segmentation software, the Surface Factor (SF) was calculated for each meningioma. SF is an objective parameter that compares the surface of a sphere (minimum surface area for a given volume) with the same volume of the tumour against the actual surface of the tumour. This ratio varies from 0 to 1, being 1 the maximum sphericity. Since irregularly-shaped meningiomas present proportionally greater surface area, the SF tends to decrease as irregularity increases. SF was correlated with WHO grade and its predictive power was estimated with ROC curve analysis.

RESULTS

A total of 176 patients (64.7% females) were included in the study; 120 WHO grade I (71.9%), 43 WHO grade II (25.7%) and 4 WHO grade III (2.4%). A statistically significant difference was found between the mean SF of WHO grade I and WHO grade II-III tumours (0.8651 ± 0.049 versus 0.7081 ± 0.105, p < 0.0001). Globally, the SF correctly classified more than 90% of cases (area under ROC curve 0.940) with 93.3% sensibility and 80.9% specificity. A cutoff value of 0.79 yielded the maximum precision, with positive and negative predictive powers of 82.6% and 92.6%, respectively. Multivariate analysis yielded SF as an independent prognostic factor of WHO grade.

CONCLUSION

The Surface Factor is an objective and quantitative parameter that helps to identify aggressive meningiomas preoperatively. A cutoff value of 0.79 allowed differentiation between WHO grade I and WHO grade II-III with high precision.

Time to Recurrence of Intracranial Meningiomas from a Monoinstitutional Surgical Series

BACKGROUND

Meningiomas show variable tendency to recur. While risk factors of recurrence have been largely investigated in literature, a paucity of data is available on the time to recurrence. Our purpose was to identify main factors affecting the time to recurrence to assist preoperative treatment decision-making strategy and to define a tailored clinical and neuroradiological follow-up.

METHODS

Data of 35 patients with intracranial meningioma recurrences have been retrospectively reviewed. Demographic (patient age at initial diagnosis and sex), radiologic (meningioma location, pattern of regrowth and topography of recurrences at first reoperation), pathologic (WHO grade and Ki67-MIB1 at initial surgery and at first reoperation, progesterone receptor [PR] expression), and surgical (extent of resection at initial surgery according to Simpsons grading system, number of reoperations) factors were analyzed.

RESULTS

Time to recurrence ranged from 20 to 120 months. Extent of resection at initial surgery was Simpson grade I in 7 patients (20%), grade II in 10 (28.5%), grade III in 14 (40%), and grade IV in 4 (11.5%). Longer median time to recurrence was observed for skull base localization (P < 0.01), Simpson grades I and II versus grades III (P = 0.01) and IV (P = 0.02), values of Ki67-MIB1 ≤ 4% (P = 0.001), and PR > 60% (P = 0.03); conversely, sex, age, number of reoperations, unchanged/progression of Ki67, and/or World Health Organization grade between first surgery and reoperation did not correlate in statistically significant way with time to recurrence.

CONCLUSIONS

The extent of resection and the Ki67-MIB1 represent the most important factors predicting shorter recurrence time of intracranial meningiomas. Patients with incomplete (Simpson grades III and IV) resection and high Ki67-MIB1 values, especially at non-skull base localization and with low PR values, require a closer short-term clinical and radiologic follow-up in the first years after surgery.

Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features

BACKGROUND

This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. Another purpose of our work was to develop a radiomics model based on the radiomics features extracted from automatic segmentation to differentiate low- and high-grade meningiomas before surgery.

MATERIALS

A total of 326 patients with pathologically confirmed meningiomas were enrolled. Samples were randomly split with a 6:2:2 ratio to the training set, validation set, and test set. Volumetric regions of interest (VOIs) were manually drawn on each slice using the ITK-SNAP software. An automatic segmentation model based on SegResNet was developed for the meningioma segmentation. Segmentation performance was evaluated by dice coefficient and 95% Hausdorff distance. Intra class correlation (ICC) analysis was applied to assess the agreement between radiomic features from manual and automatic segmentations. Radiomics features derived from automatic segmentation were extracted by pyradiomics. After feature selection, a model for meningiomas grading was built.

RESULTS

The DLM detected meningiomas in all cases. For automatic segmentation, the mean dice coefficient and 95% Hausdorff distance were 0.881 (95% CI: 0.851-0.981) and 2.016 (95% CI:1.439-3.158) in the test set, respectively. Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636-0.933). Features extracted on manual and automatic segmentation are comparable: the average ICC value was 0.804 (range, 0.636-0.933). For meningioma classification, the radiomics model based on automatic segmentation performed well in grading meningiomas, yielding a sensitivity, specificity, accuracy, and area under the curve (AUC) of 0.778 (95% CI: 0.701-0.856), 0.860 (95% CI: 0.722-0.908), 0.848 (95% CI: 0.715-0.903) and 0.842 (95% CI: 0.807-0.895) in the test set, respectively.

CONCLUSIONS

The DLM yielded favorable automated detection and segmentation of meningioma and can help deploy radiomics for preoperative meningioma differentiation in clinical practice.

Disease-Based Prognostication: Neuro-Oncology

Primary malignant and non-malignant brain and other central nervous system (CNS) tumors, while relatively rare, are a disproportionate source of morbidity and mortality. Here we provide a brief overview of approaches to modeling important clinical outcomes, such as overall survival, that are critical for clinical care. Because there are a large number of histologically distinct types of primary malignant and non-malignant brain and other CNS tumors, this chapter will provide an overview of prognostication considerations on the most common primary non-malignant brain tumor, meningioma, and the most common primary malignant brain tumor, glioblastoma. In addition, information on nomograms and how they can be used as individualized prognostication tools by clinicians to counsel patients and their families regarding treatment, follow-up, and prognosis is described. The current state of nomograms for meningiomas and glioblastomas are also provided.

Interdisciplinary Therapeutic Approaches to Atypical and Malignant Meningiomas

Meningiomas have the highest incidence among brain tumors. In contrast to benign tumors that constitute the majority of this tumor entity, the treatment of aggressive meningiomas (WHO Grade 2 and 3) is more challenging, requiring gross total removal of the tumor and the affected dura and adjuvant radiotherapy. Sometimes the location and/or the configuration of the tumor do not favor radical surgical resection without endangering the patient's clinical condition after surgery and pharmacological therapy has, until now, not been proven to be a reliable alternative. Discussion: In this narrative review, we discuss the current literature with respect to the management of meningiomas, discussing the importance of the grade of resection in the overall prognosis of the patient and in the planning of adjuvant therapy. Conclusions: According to the location and size of the tumor, radical resection should be taken into consideration. In patients with aggressive meningiomas, adjuvant radiotherapy should be performed after surgery. In cases of skull base meningiomas, a maximal, though safe, resection should take place before adjuvant therapy. An interdisciplinary approach is beneficial for patients with primary or recurrent meningioma.

Decision-making tree for surgical treatment in meningioma: a geriatric cohort study

Controversies persist regarding the benefits of surgery in elderly patients with meningiomas. The objective of this study was to develop decision-making scale to clarify the necessity for surgical intervention and provide clinical consultation for this special population. This retrospective cohort study was conducted at a single center and included 478 elderly patients (≥ 65 years) who underwent meningioma resection. Follow-up was recorded to determine recurrence and mortality rates. Univariate and multivariate analyses were performed to identify significantly preoperative factors, and prognostic prediction models were developed with determined cutoff values for the prognostic index (PI). Model discrimination was evaluated using Kaplan-Meier curves based on the PI stratification, which categorized patients into low- and high-risk groups. A decision-making tree was then established based on the risk stratification from both models. Among all patients analyzed (n = 478), 62 (13.0%) experience recurrence and 47 (10.0%) died during the follow-up period. Significantly preoperative parameters from both models included advanced age, aCCI, recurrent tumor, motor cortex involvement, male sex, peritumoral edema, and tumor located in skull base (all P < 0.05). According to the classification of PI from the two models, the decision-making tree provided four recommendations that can be used for clinical consultation. Surgery is not recommended for patients assigned to the high-risk group in both models. Patients who meet the low-risk criteria in any model may undergo surgical intervention, but the final decision should depend on the surgeon's expertise.

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.

Risk factors for peritumoral edema after radiosurgery for intracranial benign meningiomas: a long-term follow-up in a single institution

OBJECTIVE

Peritumoral edema (PTE) is recognized as a complication following stereotactic radiosurgery (SRS). The aim of this paper was to evaluate the risk of post-SRS PTE for intracranial benign meningiomas and determine the predictive factors.

METHODS

Between 2006 and 2021, 227 patients with 237 WHO grade I meningiomas were treated with Novalis linear accelerator SRS. All patients were treated with a single-fraction dose of 11–20 Gy (median 14 Gy). The median tumor volume was 3.32 cm3 (range 0.24–51.7 cm3).

RESULTS

The median follow-up was 52 months (range 12–178 months). The actuarial local tumor control rates at 2, 5, and 10 years after SRS were 99.0%, 96.7%, and 86.3%, respectively. Twenty-seven (11.9%) patients developed new or worsened post-SRS PTE, with a median onset time of 5.2 months (range 1.2–50 months). Only 2 patients developed post-SRS PTE after 24 months. The authors evaluated factors related to new-onset or worsened PTE after SRS. In univariate analysis, initial tumor volume > 10 cm3 (p = 0.03), total marginal dose > 14 Gy (p < 0.001), preexisting edema (p < 0.0001), tumor location (p < 0.001), parasagittal location (p < 0.0001), superior sagittal sinus (SSS) involvement (p < 0.0001), and SSS invasion (p < 0.015) were found to be significant risk factors. In multivariate analysis, total marginal dose > 14 Gy (HR 3.38, 95% CI 1.37–8.33, p = 0.008), preexisting SRS edema (HR 12.86, 95% CI 1.09–4.15, p < 0.0001), tumor location (HR 2.13, 95% CI 1.04–3.72, p = 0.027), parasagittal location (HR 8.84, 95% CI 1.48–52.76, p = 0.017), and SSS invasion (HR 0.34, 95% CI 0.13–0.89, p = 0.027) were significant risk factors. Twelve (5.3%) patients were symptomatic. Ten of 27 patients had complete resolution of neurological symptoms and edema improvement with steroid treatment. Steroid treatment failed in 2 patients, who subsequently required resection for PTE.

CONCLUSIONS

Radiosurgery is a safe and effective method of treating benign intracranial meningiomas according to long-term follow-up. We also identified total marginal dose > 14 Gy, preexisting PTE, parasagittal location, and SSS invasion as predictors of post-SRS PTE. Risk factors for post-SRS PTE should be considered in meningioma treatment.

A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma

PURPOSE

This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.

METHODS

A total of 132 patients with pathologically confirmed meningiomas were consecutively enrolled (105 in the training cohort and 27 in the test cohort). Radiomics features and deep learning features were extracted from T1 weighted images (T1WI) (both axial and sagittal) and the maximum slice of the axial tumor lesion, respectively. Then, the synthetic minority oversampling technique (SMOTE) was utilized to balance the sample numbers. The optimal discriminative features were selected for model building. LightGBM algorithm was used to develop DLRM by a combination of radiomics features and deep learning features. For comparison, a radiomics model (RM) and a deep learning model (DLM) were constructed using a similar method as well. Differentiating efficacy was determined by using the receiver operating characteristic (ROC) analysis.

RESULTS

A total of 15 features were selected to construct the DLRM with SMOTE, which showed good discrimination performance in both the training and test cohorts. The DLRM outperformed RM and DLM for differentiating low- and high-grade meningiomas (training AUC: 0.988 vs. 0.980 vs. 0.892; test AUC: 0.935 vs. 0.918 vs. 0.718). The accuracy, sensitivity, and specificity of the DLRM with SMOTE were 0.926, 0.900, and 0.924 in the test cohort, respectively.

CONCLUSION

The DLRM with SMOTE based on enhanced T1WI images has favorable performance for noninvasively individualized prediction of meningioma grades, which exhibited favorable clinical usefulness superior over the radiomics features.

A Novel MRI-Based Risk Stratification Algorithm for Predicting Postoperative Recurrence of Meningioma: More Benefits to Patients

Pathological grading of meningioma is insufficient to predict recurrence after resection and to guide individualized treatment strategies. One hundred and thirty-three patients with meningiomas who underwent total resection were enrolled in this retrospective study. Univariate analyses were conducted to evaluate the association between factors and recurrence. Least absolute shrinkage and selection operator (Lasso) was used to further select variables to build a logistic model. The predictive efficiency of the model and WHO grade was compared by using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and net reclassification improvement (NRI). Patients were given a new risk layer based on a nomogram. The recurrence of meningioma in different groups was observed through the Kaplan-Meier curve. Univariate analysis demonstrated that 11 risk factors were associated with prognosis (P < 0.05). The result of ROC proved that the quantified risk-scoring system (AUC = 0.853) had a higher benefit than pathological grade (AUC = 0.689, P = 0.011). The incidence of recurrence of the high risk cohort (69%) was significantly higher than that of the low risk cohort (9%) by Kaplan-Meier analysis (P < 0.001). And all patients who did not relapse in the high risk group received adjuvant radiotherapy. The novel risk stratification algorithm has a significant value for the recurrence of meningioma and can help in optimizing the individualized design of clinical therapy.

EANO guideline on the diagnosis and management of meningiomas

Meningiomas are the most common intracranial tumors. Yet, only few controlled clinical trials have been conducted to guide clinical decision making, resulting in variations of management approaches across countries and centers. However, recent advances in molecular genetics and clinical trial results help to refine the diagnostic and therapeutic approach to meningioma. Accordingly, the European Association of Neuro-Oncology (EANO) updated its recommendations for the diagnosis and treatment of meningiomas. A provisional diagnosis of meningioma is typically made by neuroimaging, mostly magnetic resonance imaging. Such provisional diagnoses may be made incidentally. Accordingly, a significant proportion of meningiomas, notably in patients that are asymptomatic or elderly or both, may be managed by a watch-and-scan strategy. A surgical intervention with tissue, commonly with the goal of gross total resection, is required for the definitive diagnosis according to the WHO classification. A role for molecular profiling including gene panel sequencing and genomic methylation profiling is emerging. A gross total surgical resection including the involved dura is often curative. Inoperable or recurrent tumors requiring treatment can be treated with radiosurgery, if the size or the vicinity of critical structures allows that, or with fractionated radiotherapy (RT). Treatment concepts combining surgery and radiosurgery or fractionated RT are increasingly used, although there remain controversies regard timing, type, and dosing of the various RT approaches. Radionuclide therapy targeting somatostatin receptors is an experimental approach, as are all approaches of systemic pharmacotherapy. The best albeit modest results with pharmacotherapy have been obtained with bevacizumab or multikinase inhibitors targeting vascular endothelial growth factor receptor, but no standard of care systemic treatment has been yet defined.

Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study

Introduction

meningioma is the most common intracranial tumor. CT scan is a common method for diagnosis. WHO classified meningioma into 3 histological grades? This study aims to evaluate the relation of different meningioma signs on CT and tumor distribution regard to WHO histological types.

Methods

In this single-center observational retrospective study, authors reviewed data of 75 meningioma patients confirmed by the WHO histological grades (WHO I/II/III) which were underwent CT scans from January 1, 2005 to December 30, 2019 at a teaching hospital, in XXXX. Data collected using patients medical records. Data were analyzed by SPSS 20 and P less than 0.05 was assumed as significant.

Result

Our study confirmed that only edema (P = 0.005) and heterogeneity (P = 0.014) had a significant association with malignant histological types. Other signs were not statistically different among WHO histology types (p > 0.05). On the subject of tumor location, atypical/malignant meningioma was significantly more common in parasagittal (P = 0.031) and front-parietal (P = 0.035) regions.

Discussion

meningiomas with Edema, heterogeneity on CT, and tumors located in parasagittal and frontoparietal regions are related to malignant histology and should be evaluated and treated more precisely.

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CT scan is a common method for diagnosis of meningioma.

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In This retrospective study 75 meningioma patients' data reviewed.

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CT scan signs of edema and heterogeneity had a significant association with malignant histological types.

CNS Invasion in Meningioma-How the Intraoperative Assessment Can Improve the Prognostic Evaluation of Tumor Recurrence

Simple Summary

Brain invasion has been integrated into the new WHO classification of meningiomas to improve the prognostic assessment regarding tumor recurrence. However, its role has been questioned. One of the reasons is that for complete histopathological assessment, tissue sampling of the complete brain–tumor interface is necessary, but not always surgically and technically feasible. Therefore, the additional intraoperative assessment of CNS invasion may be of value for a more precise assessment of this tumor characteristic. We therefore studied the prognostic impact of the histopathological and intraoperative assessment of CNS invasion regarding radiographic tumor recurrence and found that both factors by themselves do not reach a prognostic significance. However, if both factors are combined, CNS invasion is an independent negative prognostic factor. Our findings show the prognostic potential of a thorough assessment and underline the need for a standardization and documentation of meningioma tissue sampling for the optimal recurrence risk assessment.

Abstract

The detection of the infiltrative growth of meningiomas into CNS tissue has been integrated into the WHO classification as a stand-alone marker for atypical meningioma. However, its prognostic impact has been questioned. Infiltrative growth can also be detected intraoperatively. The prognostic impact of the intraoperative detection of the central nervous system tissue invasion of meningiomas was analyzed and compared to the histopathological assessment. The clinical data of 1517 cases with follow-up data regarding radiographic recurrence was collected. Histopathology and operative reports were reviewed and invasive growth was seen during resection in 23.7% (n = 345) while histopathology detected it in 4.8% (n = 73). The histopathological and intraoperative assessments were compatible in 63%. The prognostic impact of histopathological and intraoperative assessment was significant in the univariate but not in the multivariate analysis. Both methods of assessment combined reached statistical significance in the multivariate analysis (p = 0.0409). A score including all independent prognostic factors divided the cohort into three prognostic subgroups with a risk of recurrence of 33.8, 64.7 and 88.5%, respectively. The intraoperative detection of the infiltrative growth of primary meningiomas into the central nervous system tissue can complement the histopathological assessment of CNS invasion. The combined assessment is an independent prognostic factor regarding tumor recurrence and allows a risk-adapted tumor stratification.