Scalp Invasion by Atypical or Anaplastic Meningioma Is a Risk Factor for Development of Systemic Metastasis

Secreted clusterin inhibits tumorigenesis by modulating tumor cells and macrophages in human meningioma

BACKGROUND

Meningioma is the most common primary intracranial tumor with a high frequency of postoperative recurrence, yet the biology of the meningioma malignancy process is still obscure.

METHODS

To identify potential therapeutic targets and tumor suppressors, we performed single-cell transcriptome analysis through meningioma malignancy, which included 18 samples spanning normal meninges, benign and high-grade in situ tumors, and lung metastases, for extensive transcriptome characterization. Tumor suppressor candidate gene and molecular mechanism were functionally validated at the animal model and cellular levels.

RESULTS

Comprehensive analysis and validation in mice and clinical cohorts indicated clusterin (CLU) had suppressive function for meningioma tumorigenesis and malignancy by inducing mitochondria damage and triggering type 1 interferon pathway dependent on its secreted isoform, and the inhibition effect was enhanced by TNFα as TNFα also induced type 1 interferon pathway. Meanwhile, both intra- and extracellular CLU overexpression enhanced macrophage polarization towards M1 phenotype and TNFα production, thus promoting tumor killing and phagocytosis.

CONCLUSIONS

CLU might be a key brake of meningioma malignance by synchronously modulating tumor cells and their microenvironment. Our work provides comprehensive insights into meningioma malignancy and a potential therapeutic strategy.

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.

ADC histogram parameters differentiating atypical from transitional meningiomas: correlation with Ki-67 proliferation index

BACKGROUND

Preoperative differentiation of atypical meningioma (AtM) from transitional meningioma (TrM) is critical to clinical treatment.

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating AtM from TrM and its correlation with the Ki-67 proliferation index (PI).

METHODS

Clinical, imaging, and pathological data of 78 AtM and 80 TrM were retrospectively collected. Regions of interest (ROIs) were delineated on axial ADC images using MaZda software and histogram parameters (mean, variance, skewness, kurtosis, 1st percentile [ADCp1], 10th percentile [ADCp10], 50th percentile [ADCp50], 90th percentile [ADCp90], and 99th percentile [ADCp99]) were generated. The Mann-Whitney U test was used to compare the differences in histogram parameters between the two groups; receiver operating characteristic (ROC) curves were used to assess diagnostic efficacy in differentiating AtM from TrM preoperatively. The correlation between histogram parameters and Ki-67 PI was analyzed.

RESULTS

All histogram parameters of AtM were lower than those of TrM, and the variance, skewness, kurtosis, ADCp90, and ADCp99 were significantly different (P < 0.05). Combined ADC histogram parameters (variance, skewness, kurtosis, ADCp90, and ADCp99) achieved the best diagnostic performance for distinguishing AtM from TrM. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 0.800%, 76.25%, 67.95%, 70.15%, 70.93%, and 73.61%, respectively. All histogram parameters were negatively correlated with Ki-67 PI (r = -0.012 to -0.293).

CONCLUSION

ADC histogram analysis is a potential tool for non-invasive differentiation of AtM from TrM preoperatively, and ADC histogram parameters were negatively correlated with the Ki-67 PI.

The value of an apparent diffusion coefficient histogram model in predicting meningioma recurrence

OBJECTIVE

To investigate the predictive value of a model combining conventional MRI features and apparent diffusion coefficient (ADC) histogram parameters for meningioma recurrence.

MATERIALS AND METHODS

Seventy-two meningioma patients confirmed by surgical and pathological findings in our hospital (January 2017-June 2020) were retrospectively and divided into the recurrence and non-recurrence group. MaZda software was used to delineate the region of interest at the largest tumor level and generate histogram parameters. Univariate and multivariate logistic regression analysis were used to construct the nomogram for predicting recurrence. The predictive efficacy and diagnostic of this model were assessed by calibration and decision curve analysis, and receiver operating characteristic curve, respectively.

RESULTS

Maximum diameter, necrosis, enhancement uniformity, age, Simpson, tumor shape, and ADC first percentile (ADCp1) were significantly different between the two groups (p < 0.05), with the latter four being independent risk factors for recurrence. The model constructed combining the four factors had the best predictive efficacy, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.965(0.892-0.994), 90.3%, 92.6%, 88.9%, 83.3%, and 95.2%, respectively. The calibration curve showed good agreement between the model-predicted and actual probabilities of recurrence. The decision curve analysis indicated good clinical availability of the model.

CONCLUSION

This model based on conventional MRI features and ADC histogram parameters can directly and reliably predict meningioma recurrence, providing a guiding basis for selecting treatment options and individualized treatment.

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.

Surgical management of skull base meningiomas and vestibular schwannomas

PURPOSE OF REVIEW

The aim of this study is to discuss surgical management of meningiomas and schwannomas of skull base.

RECENT FINDINGS

Meningiomas and schwannomas are typically benign neoplasm with a good prognosis after surgery. Patients should be treated individually related to several features: size and localization of tumor and its proximity with deep critical neurovascular structures, neurological status, age and comorbidity. Also, the widespread use of neuroimaging and the progressive and constant aging of the populations inevitably result in the increase of detection rate of incidental (asymptomatic) neoplasm.Nowadays, there are still controversies about the correct management strategy.

SUMMARY

Surgery represents the gold standard treatment, with the objective of gross total resection; however, it is not always feasible due to localization, encasement of neuro-vascular structure, invasion of cranial nerve and brain parenchyma. Stereotactic radiosurgery and radiation therapy are important to achieve a satisfactory functional outcome and tumor control in case of residue or recurrence. A multidisciplinary approach is pivotal.

Type III Cutaneous Atypical Meningioma of the Scalp

Cutaneous meningiomas can be a diagnostic challenge, as they are not only found very rarely in extracranial sites, including the skin, but also because of the histopathologic overlap with several other spindle cell tumors. Cutaneous meningiomas are divided into type I (congenital), type II (ectopic) and type III (via a direct extension) lesions. We present a rare case of atypical meningioma of the skin in a 71-year-old female. A patient presented with a painful and enlarging lesion on the left central frontal scalp. Biopsy showed bone and soft tissue with involvement of a spindle cell neoplasm, consisting of whorled nests with atypical features, including variably increased mitotic index, areas of hypercellularity, and sheeted architecture. The overall findings were consistent with an atypical meningioma (WHO grade 2). Atypical meningiomas constitute only 5 - 15% of all meningiomas. A skull MRI was later performed, which demonstrated a left frontal tumor consistent with an atypical meningioma that had eroded through the skull. Dermatopathologists should consider cutaneous meningioma as a differential diagnosis of spindle cell neoplasms of the skin and subcutaneous tissue in head and neck. This article is protected by copyright. All rights reserved.