Uncommon Cranial Meningioma: Key Imaging Features on Conventional and Advanced Imaging

Parapharyngeal meningioma extending through foramen ovale: a case report

BACKGROUND

Meningioma is a common non-glial tumor of the brain. Extracranial meningiomas in the parapharyngeal space are especially rare. Herein we report a case of extracranial meningioma in the parapharyngeal space and give a comprehensive description of its complete clinical course and radiological findings, which may provide helpful information in the diagnosis and treatment of extracranial meningiomas in the parapharyngeal space.

CASE PRESENTATION

A 61-year-old man presented a slowly increased mass under the left ear without pain and numbness over one year. Ultrasound examination detected a hypoechoic uneven mass behind the left parotid gland with a clear boundary, and color Doppler flow imaging revealed blood flow signals within the mass. Unenhanced computed tomography (CT) of the craniofacial region revealed a homogenous soft tissue mass in the parapharyngeal space without calcification. Magnetic resonance imaging (MRI) showed that a homogenous soft tissue mass was hyperintense on T2-weighted image, hypointense on T1-weighted image, and obviously enhanced after contrast enhancement in the parapharyngeal space. Coronal MRI showed that the lesion originated from basicranial dura extending into parapharyngeal space through the left foramen ovale at the skull base. Finally, histopathological and immunohistochemical analyses confirmed the final diagnosis of extracranial meningiomas in the parapharyngeal space.

CONCLUSION

Extracranial meningiomas of the parapharyngeal space are rare and often pose a diagnostic challenge. Preoperative imaging examinations, especially CT and MRI, can aid in the accurate preoperative diagnosis, especially when intracranial extensions and dural tail signs are observed.

Lesion delineation framework for vestibular schwannoma, meningioma and brain metastasis for gamma knife radiosurgery using stereotactic magnetic resonance images

BACKGROUND AND OBJECTIVE

GKRS is an effective treatment for smaller intracranial tumors with a high control rate and low risk of complications. Target delineation in medical MR images is essential in the planning of GKRS and follow-up. A deep learning-based algorithm can effectively segment the targets from medical images and has been widely explored. However, state-of-the-art deep learning-based target delineation uses fixed sizes, and the isotropic voxel size may not be suitable for stereotactic MR images which use different anisotropic voxel sizes and numbers of slices according to the lesion size and location for clinical GKRS planning. This study developed an automatic deep learning-based segmentation scheme for stereotactic MR images.

METHODS

We retrospectively collected stereotactic MR images from 506 patients with VS, 1,069 patients with meningioma and 574 patients with BM who had been treated using GKRS; the lesion contours and individual T1W+C and T2W MR images were extracted from the GammaPlan system. The three-dimensional patching-based training strategy and dual-pathway architecture were used to manage inconsistent FOVs and anisotropic voxel size. Furthermore, we used two-parametric MR image as training input to segment the regions with different image characteristics (e.g., cystic lesions) effectively.

RESULTS

Our results for VS and BM demonstrated that the model trained using two-parametric MR images significantly outperformed the model trained using single-parametric images with median Dice coefficients (0.91, 0.05 versus 0.90, 0.06, and 0.82, 0.23 versus 0.78, 0.34, respectively), whereas predicted delineations in meningiomas using the dual-pathway model were dominated by single-parametric images (median Dice coefficients 0.83, 0.17 versus 0.84, 0.22). Finally, we combined three data sets to train the models, achieving the comparable or even higher testing median Dice (VS: 0.91, 0.07; meningioma: 0.83, 0.22; BM: 0.84, 0.23) in three diseases while using two-parametric as input.

CONCLUSIONS

Our proposed deep learning-based tumor segmentation scheme was successfully applied to multiple types of intracranial tumor (VS, meningioma and BM) undergoing GKRS and for segmenting the tumor effectively from stereotactic MR image volumes for use in GKRS planning.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1^st (ADC1), 10^th (ADC10), 50^th (ADC50), 90^th (ADC90), and 99^th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

Grading of meningioma tumors based on analyzing tumor volumetric histograms obtained from conventional MRI and apparent diffusion coefficient images

Background

Our purpose was to evaluate the application of volumetric histogram parameters obtained from conventional MRI and apparent diffusion coefficient (ADC) images for grading the meningioma tumors.

Results

Tumor volumetric histograms of preoperative MRI images from 45 patients with the diagnosis of meningioma at different grades were analyzed to find the histogram parameters. Kruskal-Wallis statistical test was used for comparison between the parameters obtained from different grades. Multi-parametric regression analysis was used to find the model and parameters with high predictive value for the classification of meningioma. Mode; standard deviation on post-contrast T1WI, T2-FLAIR, and ADC images; kurtosis on post-contrast T1WI and T2-FLAIR images; mean and several percentile values on ADC; and post-contrast T1WI images showed significant differences among different tumor grades (P < 0.05). The multi-parametric linear regression showed that the ADC histogram parameters model had a higher predictive value, with cutoff values of 0.212 (sensitivity = 79.6%, specificity = 84.3%) and 0.180 (sensitivity = 70.9%, specificity = 80.8%) for differentiating the grade I from II, and grade II from III, respectively.

Conclusions

The multi-parametric model of volumetric histogram parameters in some of the conventional MRI series (i.e., post-contrast T1WI and T2-FLAIR images) along with the ADC images are appropriate for predicting the meningioma tumors’ grade.

Meningioma with ring enhancement on MRI: a rare case report

Background

Meningiomas typically manifest on magnetic resonance imaging (MRI) as iso- to hypointense on T1-weighted imaging and iso- to hyperintense on T2-weighted imaging. After contrast administration, they usually homogeneously enhance and exhibit a visible dural tail. Meningiomas with atypical findings may be misdiagnosed.

Case presentation

We report a 50-year-old female patient with a pathologically diagnosed fibrous meningioma (World Health Organization grade I) that exhibited ring enhancement on MRI.

Conclusions

Meningiomas may rarely present with ring enhancement on MRI. The natural history and mechanisms of cystic degeneration and enhancement in the various types of meningioma require further study.

Accurate Preoperative Distinction of Intracranial Hemangiopericytoma From Meningioma Using a Multihabitat and Multisequence-Based Radiomics Diagnostic Technique

Background: Intracranial hemangiopericytoma (IHPC) and meningioma are both meningeal neoplasms, but they have extremely different malignancy and outcomes. Because of their similar radiological characteristics, they are difficult to distinguish prior to surgery, leading to a high rate of misdiagnosis.

Methods: We enrolled 292 patients (IHPC, 155; meningiomas, 137) with complete clinic-radiological and histopathological data, from a 10-year database established at Tiantan hospital. Radiomics analysis of tumor and peritumoral edema was performed on multisequence magnetic resonance images, and a fusion radiomics signature was generated using a machine-learning strategy. By combining clinic-radiological data with the fusion radiomics signature, we developed an integrated diagnostic approach that we named the IHPC and Meningioma Diagnostic Tool (HMDT).

Results: The HMDT displayed remarkable diagnostic ability, with areas under the curve (AUCs) of 0.985 and 0.917 in the training and validation cohorts, respectively. The calibration curve showed excellent agreement between the diagnosis predicted by HMDT and the histological outcome, with p-values of 0.801 and 0.622 for the training and the validation cohorts, respectively. Cross-validation showed no statistical difference across three divisions of the cohort, with average AUCs of 0.980 and 0.941 for the training and validation cohorts, respectively. Stratification analysis showed consistent performance of the HMDT in distinguishing IHPC from highly misdiagnosed subgroups of grade I meningioma and angiomatous meningioma (AM) with AUCs of 0.913 and 0.914 in the validation cohorts for the two subgroups.

Conclusions: By integrating clinic-radiological information with radiomics signature, the proposed HMDT could assist in preoperative diagnosis to distinguish IHPC from meningioma, providing the basis for strategic decisions regarding surgery.

Radio-pathological review of a metastatic renal cell carcinoma within a meningioma: A case report of collision tumor

We report a case of a collision tumor, a meningioma complicated with metastasis from a primary renal cell carcinoma. A 75-year-old man, with known history of renal cell carcinoma, and 10-year history of stable meningioma developed neurological symptoms. Computed tomography and magnetic resonance imaging revealed left frontal intracranial extra-axial mass with imaging criteria suspicious for an atypical meningioma or hemangiopericytoma. Given the history of a known primary, the possibility of brain metastasis was included. Pathology confirmed the presence of metastatic renal cell carcinoma nidus with a surrounding meningioma. Tumor-to-tumor metastasis or collision tumor is a very rare phenomenon. Atypical radiologic findings with positive history of a primary extracranial tumor should raise the suspicion of potential metastases.

Late intracranial metastasis from adenoid-cystic carcinoma of the parotid gland: Imaging, histologic and molecular features

We describe the neuroradiologic, histologic, and genetic features of a very unusual intracranial dural metastasis from adenoid cystic carcinoma of the parotid gland detected 27 years after the initial diagnosis.

Noninvasive Differentiation of Meningiomas and Dural Metastases Using Intratumoral Vascularity Obtained by Arterial Spin Labeling

Purpose

Using conventional magnetic resonance imaging (MRI) techniques, the imaging features of meningiomas and dural metastases overlap and a differentiation between these tumor entities therefore remains difficult, particularly in patients with a known primary neoplasm. The purpose of this study was to explore the potential role of normalized vascular intratumoral signal intensity values (nVITS) obtained from pulsed arterial spin labeling (PASL) to differentiate between meningiomas and dural metastases.

Methods

In this study PASL was performed in 46 patients with meningiomas (n = 30) and dural metastases (n = 16) on a 3T scanner, in addition to the routine diagnostic imaging protocol. The ratio between the vascular signal intensity of the tumor and the contralateral normal white matter obtained by PASL images was defined as nVITS.

Results

Meningiomas showed significantly higher nVITS values compared to dural metastases (p < 0.001). The optimal nVITS cut-off value to differentiate between the 2 tumor entities was 1.989, with 100% sensitivity and 81.2% specificity.

Conclusion

The nVITS values obtained by PASL provide a fast and noninvasive MRI technique with which to differentiate between meningiomas and dural metastases in a routine clinical setting based on tumor vascularity.

Feline meningioma with extensive nasal involvement

Case summary

A 9-year-old male neutered domestic longhair cat was presented with a 3 week history of lethargy and pain of unknown origin. A large extra-axial mass was demonstrated on MRI of the head, with cribriform plate destruction, extensive nasal invasion and intracranial expansion, producing a severe mass effect. The mass was isointense on T1-weighted imaging, predominantly hypointense with some hyperintense areas on T2-weighted imaging and fluid attenuation inversion recovery, markedly contrast enhancing, and caused transtentorial and cerebellar herniation. Histopathological evaluation confirmed a transitional (mixed) meningioma.

Relevance and novel information

To our knowledge this is the first report of a meningioma with extensive nasal involvement in a cat. Based on this case, meningioma should be considered as a differential diagnosis for tumours involving the nasal cavity and frontal lobe with cribriform plate destruction.

Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma

PURPOSE

To assess the role of histogram analysis of apparent diffusion coefficient (ADC) maps based on whole-tumor in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from angiomatous meningioma (AM).

MATERIALS AND METHODS

Pathologically confirmed intracranial SFT/HPC (n = 15) and AM (n = 20) were retrospectively collected and their clinical and conventional MRI features were analyzed. Diffusion-weighted (DW) images (b = 0 and 1000 s/mm²) were processed with the mono-exponential model. Regions of interest covering the whole tumor were drawn on all slices of the ADC maps to obtain histogram parameters, including mean ADC (ADCmean), median ADC (ADCmedian), maximum ADC (ADCmax), minimum ADC (ADCmin), skewness and kurtosis, as well as the 5th, 10th, 25th, 75th, 90th and 95th percentile ADC (ADC5, ADC10, ADC25, ADC75, ADC90 and ADC95). Differences of histogram parameters between SFT/HPC and AM were compared using Mann-Whitney U test. Receiver operating characteristic (ROC) curve was used to determine the diagnostic performance.

RESULTS

The ADCmin (P = 0.001) and ADC5 (P = 0.045) were significantly lower in SFT/HPCs than in AMs, while no significant difference was found in sex, age, conventional MRI features or any other histogram parameters between the two entities (P = 0.051-1.000). ADCmin showed the best diagnostic performance (area under curve [AUC], 0.86; sensitivity, 81.3%; specificity, 83.3%) in differentiating SFT/HPC from AM with optimal cutoff value being 569.00 × 10^-6  mm²/s, followed by ADC5 (AUC, 0.72; sensitivity, 68.8%; specificity, 75%) with optimal cutoff value being 781.97 × 10^-6  mm²/s.

CONCLUSION

SFT/HPC and AM share similar conventional MR appearances. Whole-tumor histogram analysis of ADC maps may be a useful tool for differential diagnosis, with ADCmin and ADC5 being potential parameters.

Parasagittal meningiomas – literature review and a case report

Meningiomas are tumors that can develop anywhere along the neuraxis, but with increased concentration in some specific areas. Parasagittal meningiomas have the dural attachment on the external layer of the superior sagittal sinus (SSS) and invade the parasagittal angle displacing brain away from its normal position. Among meningiomas, the parasagittal location is the most common (22%). Taking into account their anatomic insertion along SSS, parasagittal meningiomas can have their dural attachment in the anterior, the middle or the posterior third of the SSS. Most frequently parasagittal meningiomas are located in the middle third of the superior sagittal sinus (between coronal suture and lambdoid suture). The clinical picture of parasagittal meningiomas depends on the tumor location along the SSS and so is the attitude towards ligation and reconstruction of the sinus. Controversial issues regarding surgical management of parasagittal meningiomas concerning leaving a tumor remnant that invades the SSS instead attempting total resection, or the attitude in the case of totally occluded segment of a sinus are summarized in this paper. The special care for the venous system is emphasized. The recurrence matter is also approached underlining the importance of adjuvant radiosurgery for the management of residual tumors. Results described in the main papers of the literature are reviewed. Conclusions are referring to the historical evolution regarding the surgical management of parasagittal meningiomas: aggressiveness of resection, sinus reconstruction, importance of adjuvant techniques: radiosurgery, endovascular surgery and to the importance of microsurgery and careful and meticulous planning of the approach in order to avoid interference with venous collaterals. A suggestive clinical case from the authors experience is presented.