Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review

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.

Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning

BACKGROUND

Meningiomas are among the most common intracranial tumors. In these tumors, volumetric assessment is not only important for planning therapeutic intervention but also for follow-up examination.However, a highly accurate automated volumetric method for meningiomas using single-modality magnetic resonance imaging (MRI) has not yet been reported. Here, we aimed to develop a deep learning-based automated volumetry method for meningiomas in MRI and investigate its accuracy and potential clinical applications.

METHODS

For deep learning, we used MRI images of patients with meningioma who were referred to Osaka University Hospital between January 2007 and October 2020. Imaging data of eligible patients were divided into three non-overlapping groups: training, validation, and testing. The model was trained and tested using the leave-oneout cross-validation method. Dice index (DI) and root mean squared percentage error (RMSPE) were measured to evaluate the model accuracy. Result: A total of 178 patients (64.6 ± 12.3 years [standard deviation]; 147 women) were evaluated. Comparison of the deep learning model and manual segmentation revealed a mean DI of 0.923 ± 0.051 for tumor lesions. For total tumor volume, RMSPE was 9.5 ± 1.2%, and Mann-Whitney U test did not show a significant difference between manual and algorithm-based measurement of the tumor volume (p = 0.96).

CONCLUSION

The automatic tumor volumetry algorithm developed in this study provides a potential volume-based imaging biomarker for tumor evaluation in the field of neuroradiological imaging, which will contribute to the optimization and personalization of treatment for central nervous system tumors in the near future.

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features

PURPOSE

This study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency.

METHODS

202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada's consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency.

RESULTS

The logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873-1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758-0.885) in the training cohort and 0.943 (95 % CI, 0.873-1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency.

CONCLUSIONS

The proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging

OBJECTIVE

We sought to investigate the value of a clinical-radiomics model based on magnetic resonance imaging in differentiating fibroblastic meningiomas from non-fibroblastic meningiomas.

METHODS

Clinical, imaging, and postoperative pathologic data of 423 patients (128 fibroblastic meningiomas and 295 non-fibroblastic meningiomas) were randomly categorized into training (n = 296) and validation (n = 127) groups at a 7:3 ratio. The Selectpercentile and LASSO were used to selected the highly correlated features from 3376 radiomics features. Different classifiers were used to train and verify the model. The receiver operating characteristic curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE) were drawn to evaluate the performance. The optimal radiomics model was selected. Calibration curves and decision curve analysis were used to verify the clinical utility and consistency of the nomogram constructed from the radiomics features and clinical factors.

RESULTS

Thirteen radiomics features were selected from contrast-enhanced T1-weighted imaging and T2-weighted imaging after dimensionality reduction. The prediction performance of random forest radiomics model is slightly lower than that of the clinical-radiomics model. The area under the curve, SEN, SPE, and ACC of the clinical-radiomics model training set were 0.836 (95% confidence interval, 0.795-0.878), 0.922, 0.583, and 0.686, respectively. The area under the curve, SEN, SPE, and ACC of the validation set were 0.756 (95% confidence interval, 0.660-0.846), 0.816, 0.596, and 0.661, respectively.

CONCLUSIONS

The diagnostic efficacy of the clinical-radiomics model of fibroblastic meningioma and non-fibroblastic meningioma was better than that of the radiomics prediction model alone and can be used as a potential tool for clinical surgical planning and evaluation of patient prognosis.

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets

BACKGROUND

Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios.

OBJECTIVE

This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization.

METHODS

This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining.

RESULTS

The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D.

CONCLUSIONS

Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.

Clinical Significance of Stiffness during Endoscopic Surgery for Intracerebral Hemorrhage: A Retrospective Study

Studies regarding hematoma stiffness and removal difficulty are scarce. This study explored the association between hematoma stiffness and surgical results of endoscopic hematoma removal for intracerebral hemorrhage. It also aimed to clarify factors associated with hematoma stiffness. We classified intracerebral hematoma as either soft or firm stiffness by retrospectively evaluating operative videos by two neurosurgeons. The interobserver reliability of the classification was assessed by calculating the κ values. We investigated the relationship between hematoma stiffness and surgical results. Favorable hematoma removal (FHR) was defined as a residual hematoma volume of ≤15 mL or removal rate of ≥70%. Furthermore, we compared the background characteristics, imaging findings, and laboratory data between the two groups. Forty patients were included in this study. The mean baseline hematoma volume was 69.9 mL (range, 41.3-97.6 mL). FHR was accomplished in 35 cases (87.5%). Thirty-four patients (85%) were in the soft hematoma group (group S). Six patients (15%) were in the firm hematoma group (group F). Classification of hematoma stiffness demonstrated an excellent degree of interobserver agreement (κ score = 0.91). Patients in group S had a high FHR rate (p = 0.018) and short endoscopic procedure times (p = 0.00034). The island sign was present in group S (p = 0.030). Patients in group F had significantly high fibrinogen levels (p = 0.049) and low serum total calcium (p = 0.032), hemoglobin (p = 0.041), and hematocrit (p = 0.011) levels. Hematoma stiffness during endoscopic surgery for intracerebral hemorrhage correlates with surgical results, including the endoscopic procedure time and accomplishing rate of FHR.

A rare case of multiple supratentorial brain lesions due to meningiomatosis

Meningeal tumors represent the most common primary central nervous system tumors. The term "multiple meningiomas" or "meningiomatosis" refers to the occurrence of 2 or more spatially separated meningiomas without the features of neurofibromatosis. Meningiomatosis accounts for only less than 10% of all cases and is more prevalent in women. We report a rare case of a 53-year-old female patient complaining of a headache characterized by a throbbing pain in the right side of the head. Neurological examination was largely normal, with the exception of a slight weakening of the right extremity. Multiple brain masses, due to meningiomatosis, were revealed upon CT scan and MRI. Subsequent tissue biopsy confirmed the diagnosis of meningothelial meningiomas.

Relationship Between Quantitative Tumor Consistency and Pathological Factors in Intracranial Meningioma

BACKGROUND

The consistency of intracranial meningiomas is an important clinical factor because it affects the success of surgical resection. This study aimed at identifying and quantitatively measuring pathological factors that contribute to the consistency of meningiomas. Furthermore, we investigated the relationship between these factors and preoperative neuroradiological imaging.

METHODS

We analyzed 42 intracranial meningioma specimens, which had been removed at our institution between October 2012 and March 2018. Consistency was measured quantitatively after resection using an industrial stiffness meter. For pathological evaluation, we quantitatively measured the collagen-fiber content through binarization of images of Azan-Mallory-stained section. We assessed calcification and necrosis semi-quantitatively using images acquired of Hematoxylin and Eosin stained samples. The relationship between collagen-fiber content rate and imaging findings was examined.

RESULTS

The content of collagen fibers significantly positively correlated with meningioma consistency (p < 0.0001). Collagen-fiber content was significantly higher in low- and iso-intensity regions compared with high-intensity regions on the magnetic resonance T2-weighted images (p = 0.0148 and p = 0.0394, respectively). Calcification and necrosis showed no correlation with tumor consistency.

CONCLUSIONS

The quantitative hardness of intracranial meningiomas positively correlated with collagen-fiber content; thus, the amount of collagen fibers may be a factor that determines the hardness of intracranial meningiomas. Our results demonstrate that T2-weighted images reflect the collagen-fiber content and are useful for estimating tumor consistency preoperatively and non-invasively.

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.

Sclerosing meningioma with a large peritumoral cyst: Case report

Meningioma morphology is diverse. Although unlisted in the WHO classification, sclerosing meningioma is a rare variation featuring an extremely low signal intensity on MRI T2-weighted imaging. About 50 cases of sclerosing meningiomas, including spinal tumors, have been reported; however, cases with an accompanying large peritumoral cyst remain unreported. Here, we first report a rare case of sclerosing meningioma with a large peritumoral cyst and review relevant literature.

Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review

PURPOSE

Predicting the consistency of benign central nervous system (CNS) tumors prior to surgery helps to improve surgical outcomes. This review summarizes and analyzes the literature on using radiomics and/or machine learning (ML) for consistency prediction.

METHOD

The Medical Literature Analysis and Retrieval System Online (MEDLINE) database was screened for studies published in English from January 1st 2000. Data was extracted according to the PRISMA guidelines and quality of the studies was assessed in compliance with the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

RESULTS

Eight publications were included focusing on pituitary macroadenomas (n = 5), pituitary adenomas (n = 1), and meningiomas (n = 2) using a retrospective (n = 6), prospective (n = 1), and unknown (n = 1) study design with a total of 763 patients for the consistency prediction. The studies reported an area under the curve (AUC) of 0.71-0.99 for their respective best performing model regarding the consistency prediction. Of all studies, four articles validated their models internally whereas none validated their models externally. Two articles stated making data available on request with the remaining publications lacking information with regard to data availability.

CONCLUSIONS

The research on consistency prediction of CNS tumors is still at an early stage regarding the use of radiomics and different ML techniques. Best-practice procedures regarding radiomics and ML need to be followed more rigorously to facilitate the comparison between publications and, accordingly, the possible implementation into clinical practice in the future.

The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era

The evolution of endoscopic trans-sphenoidal surgery raises the question of the role of transcranial surgery for pituitary tumors, particularly with the effectiveness of adjunct irradiation. This narrative review aims to redefine the current indications for the transcranial approaches for giant pituitary adenomas in the endoscopic era. A critical appraisal of the personal series of the senior author (O.A.-M.) was performed to characterize the patient factors and the tumor's pathological anatomy features that endorse a cranial approach. Traditional indications for transcranial approaches include the absent pneumatization of the sphenoid sinus; kissing/ectatic internal carotid arteries; reduced dimensions of the sella; lateral invasion of the cavernous sinus lateral to the carotid artery; dumbbell-shaped tumors caused by severe diaphragm constriction; fibrous/calcified tumor consistency; wide supra-, para-, and retrosellar extension; arterial encasement; brain invasion; coexisting cerebral aneurysms; and separate coexisting pathologies of the sphenoid sinus, especially infections. Residual/recurrent tumors and postoperative pituitary apoplexy after trans-sphenoidal surgery require individualized considerations. Transcranial approaches still have a critical role in giant and complex pituitary adenomas with wide intracranial extension, brain parenchymal involvement, and the encasement of neurovascular structures.

Amide Proton Transfer Weighted MR Imaging for Predicting Meningioma Stiffness: A Feasibility Study

BACKGROUND

Stiffness of meningioma is an important factor affecting the surgical resection and the prognosis of patients.

PURPOSE

To examine the feasibility of APTw-magnetic resonance imaging (MRI) in evaluating meningioma stiffness.

STUDY TYPE

Retrospective.

POPULATION

Seventy-one patient with meningiomas, 39 were male and 32 were female; the mean age was 51 ± 10 years.

FIELD STRENGTH/SEQUENCE

3.0T; Turbo-spin-echo T₁ -weighted and Gd-T₁ -weighted sequence; Turbo-spin-echo T₂ -weighted sequence; 2D fat-suppressed, turbo-spin-echo APTw pulse sequence.

ASSESSMENT

The T₁ WI signal intensity score, T₂ WI signal intensity score, APTw_min , APTw_max , and APTw_mean values were compared between soft, medium stiff and stiff meningiomas or non-stiff meningiomas and stiff meningiomas group.

STATISTICAL TESTS

Chi-square test, one-way ANOVA analysis, independent-samples t-test, intra-class correlation coefficient, rank-sum test, receiver operating characteristic curve analysis. P < 0.05 was considered statistically significant in all tests.

RESULTS

APTw_min and APTw_mean in the stiff group were significantly lower than that in the non-stiff group (2.79% ± 0.42% vs. 1.90% ± 0.60% and 3.20% ± 0.31% vs. 2.55% ± 0.61%). APTw_min and APTw_mean in the stiff group were significantly lower than that in the medium stiff and soft groups (1.90% ± 0.60% vs. 2.69% ± 0.40% and 3.12% ± 0.32%, 2.55% ± 0.61% vs. 3.17% ± 0.33% and 3.39% ± 0.18%), APTw_min in the medium stiff group was significantly lower than in the soft group, there was no significant difference in APTw_mean between the medium stiff and soft groups (P = 0.190). APTw_min showed the best diagnostic performance for evaluating meningioma stiffness with an area under the curve of 0.913, when the APTw_min was lower than 2.4%, the meningioma was defined as a stiff tumor, the sensitivity, specificity, and accuracy were 87.1%, 87.5%, and 85.9%, respectively.

DATA CONCLUSION

APTw-MRI could be used to evaluate meningioma stiffness, with APTw_min having the best evaluative efficiency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 1.

Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

BACKGROUND

Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level.

PURPOSE

To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters.

MATERIALS AND METHODS

We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FA_IP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FA_IP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R²_OS) on the intra-tumor level and within-sample R² across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FA_IP, respectively.

RESULTS

Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R²_OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FA_IP (median R²_OS = 0.31, 0.20-0.42). Samples with low R²_OS for FA_IP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R² = 0.60) and FA_IP (R² = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FA_IP is high in the presence of elongated and aligned cell structures, but low otherwise.

CONCLUSION

Cell density and structure anisotropy account for variability in MD and FA_IP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

Treatment Strategies and Current Results of Petroclival Meningiomas

Petroclival meningiomas (PCMs) are complex skull-base tumors that continue to pose a formidable surgical challenge to neurosurgeons because of their deep-seated location/intimate relationship with the brainstem and neurovascular structures. The advent of stereotactic radiosurgery (SRS), along with the shifting of management goals from complete radiological cure to maximal preservation of the patient's quality of life (QOL), has further cluttered the topic of "optimal management" in PCMs. Not all patients with PCM need treatment ("watchful waiting"). However, many who reach the neurosurgeons with a symptomatic disease need surgery. The goal of the surgery in PCMs is a GTR, yet this can be achieved in only less than half of the patients with acceptable morbidity. The remainder of the patients are better treated by STR followed by SRS for residual tumor control or close follow-up. A small subset of patients with PCM may be best treated by primary SRS. In this chapter, we have tried to summarize the scientific evidence pertaining to the management of PCMs (including the senior author's series), particularly those regarding the available treatment strategies and current outcomes, and discuss the decision-making process to formulate an "optimal management" plan for individual PCMs.

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.

Can magnetic resonance imaging predict preoperative consistency and vascularity of intracranial meningioma?

Background

Meningiomas are considered the most common primary intracranial neoplasms. The surgical resection is the main curative therapy. Evaluation of meningioma consistency and vascularity is important before surgery to be aware about the difficulties that neurosurgeon will face during resection, the possibility of total resection and to determine which equipment will be suitable for surgery. The purpose of this study was to identify the relationship between the MRI predictors of meningioma consistency [utilizing tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios] as well as tumor vascularity (utilizing arterial spin labeling perfusion) in correlation with intraoperative findings. The study was carried out on 40 patients with MRI features of intracranial meningiomas. Non-contrast conventional MRI followed by arterial spin labeling MR perfusion and post contrast sequences were done for all cases. Final diagnosis of the cases was established by histopathological data while consistency and vascularity was confirmed by operative findings.

Results

According to surgical data, the studied cases of intracranial meningiomas were classified according to tumor consistency into 19 cases (47.5%) showing soft consistency, 14 cases (35%) showing intermediate consistency and 7 cases (17.5%) showing firm/hard consistency. TCTI ratio was the most significant MRI parameter in correlation with operative consistency of meningiomas, with soft lesions showing TCTI ranging from 1.75 to 2.87, intermediate consistency lesions TCTI ranging from 1.3 to 1.6, and firm lesions TCTI ranging from 0.9 to 1.2. According to intraoperative vascularity, cases were classified into 27 cases (67.5%) showing hypervascularity, 6 cases (15%) showing intermediate vascularity and 7 cases (17.5%) showing hypovascularity. Arterial spin labeling (ASL) was the most significant MRI parameter in correlation with operative vascularity of meningiomas, with hypervascular lesions showing normalized cerebral blood flow (n-CBF) ranging from 2.10 to 14.20, intermediately vascular lesions ranging from 1.50 to 1.60, and hypovascular lesions ranging from 0.70 to 0.90.

Conclusions

TCTI ratio showed good correlation with intraoperative meningioma consistency. ASL MR perfusion as a noninvasive technique is a reliable method to predict vascularity of meningioma in cases where IV contrast is contraindicated.

The otologic approach in the management of posterior petrous surface meningiomas

PURPOSE

Report our experience in the management of posterior petrous surface meningiomas (PPSMs), and identify features that affect hearing, facial nerve (FN) function, and control of the disease.

METHODS

Retrospective case series of 131 patients surgically managed for PPSMs. FN status, hearing and tumour radicality were assessed and compared between patients with tumours of different locations (Desgeorges classification) and internal auditory canal involvement (IAC).

RESULTS

At the time of surgery 74.8% of patients had a hearing loss. Hearing was mostly unserviceable in tumors attached to the meatus. Pure tone audiometry did not correlate to IAC extension, while speech discrimination scores were statistically worse when the tumor occupied the IAC (unpaired t test, p = 0.0152). Similarly, extrameatal tumors undergoing removal by otic preserving techniques maintained postoperative hearing, whereas hearing worsened significantly in tumors involving the IAC (paired t test, p = 0.048). The FN was affected preoperatively in 11.4% of cases. Postoperative FN palsy was significantly correlated to the IAC involvement (Fisher's exact test, p = 0.0013), while it was not correlated to tumor size. According to the Desgeorges classification, a postoperative FN palsy complicated the majority of anteriorly extending tumors and, two-fifths of meatus centred tumors. 75% of posterior located tumors had a postoperative FN grade I HB.

CONCLUSIONS

Since the involvement of the IAC by the tumor affects both hearing and FN function, the IAC is of primary importance in PPSMs and should be studied and addressed as much as the tumor location in the CPA.

Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type

•

Tensor-valued dMRI facilitates prediction of meningioma consistency, grade and type.

•

Tensor-valued dMRI corroborates findings of diffusion tensor and kurtosis imaging.

•

MK and MK_A is associated with firm and MD with variable meningioma consistency.

•

Variability of MK_I in the vicinity of the tumor is associated with meningioma grade.

•

MK_{A 50} and MK_{I 50} separates psammomatous meningiomas from other meningioma types.

Background

Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach.

Purpose

To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type.

Materials and methods

30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm². The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components—the anisotropic and isotropic kurtoses (MK_A and MK_I). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor (“whole-tumor”) and within brain tissue in the immediate periphery outside the tumor (“rim”) by histogram analysis.

Results

Lower 10th percentiles of MK and MK_A in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MK_{A 10} and p = 0.04 for MK₁₀) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MK_I in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MK_I maps we observed elevated rim-like structure that could be associated with grade. Higher median MK_A and lower median MK_I distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MK_{A 50} and p = 0.03 and p = 0.04 for MK_{I 50}).

Conclusion

Parameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.

Factors Associated With Abducens Nerve Palsy in Patients Undergoing Surgery for Petroclival Meningiomas

BACKGROUND

During the surgical resection of petroclival meningiomas, preserving the cranial nerves is crucial. The abducens nerve is particularly vulnerable during surgery. However, the preoperative risk factors and postoperative prognosis of abducens nerve palsy (ANP) are poorly understood.

METHODS

We retrospectively analyzed 70 patients who underwent surgery for petroclival meningiomas between May 2010 and December 2019, divided into gross-total resection (GTR) and subtotal resection (STR) groups. The relationship of preoperative clinical factors with the incidence and recovery of postoperative ANP was analyzed.

RESULTS

Postoperative ANP was observed in 23 patients (32.9%). Multivariable logistic regression revealed that the tumor-to-cerebellar peduncle T2 imaging intensity index (TCTI) (P < 0.001) and internal auditory canal invasion (P = 0.033) contributed to postoperative ANP. GTR was achieved in 37 patients (52.9%), and 10 (27.0%) of them showed ANP. STR was achieved in 33 patients (47.1%), and 13 (39.4%) of them showed ANP. Recovery from ANP took a median of 6.6 months (range, 4.5-20.3 months). At 6 months after the operation, recovery of the abducens nerve function was observed in 16 patients (69.0%); of whom, 4 (40.0%) were in the GTR group and 12 (92.3%) were in the STR group (P = 0.025).

CONCLUSIONS

TCTI and internal auditory canal invasion were the risk factors for postoperative ANP. Although intentional STR did not prevent ANP immediately after the operation, recovery of the abducens nerve function after surgery was observed more frequently in the STR group than in the GTR group.

Magnetic Resonance Elastography in Intracranial Neoplasms: A Scoping Review

BACKGROUND

Magnetic resonance elastography (MRE) allows noninvasive assessment of intracranial tumor mechanics and may thus be predictive of intraoperative conditions. Variations in the use of technical terms complicate reading of current literature, and there is need of a review using consolidated nomenclature.

OBJECTIVES

We present an overview of current literature on MRE relating to human intracranial neoplasms using standardized nomenclature suggested by the MRE guidelines committee. We then discuss the implications of the findings, and suggest approaches for future research.

METHOD

We performed a systematic literature search in PubMed, Embase, and Web of Science; the articles were screened for relevance and then subjected to full text review. Technical terms were consolidated.

RESULTS

We identified 12 studies on MRE in patients with intracranial tumors, including meningiomas, glial tumors including glioblastomas, vestibular schwannomas, hemangiopericytoma, central nervous system lymphoma, pituitary macroadenomas, and brain metastases. The studies had varying objectives that included prediction of intraoperative consistency, histological separation, prediction of adhesiveness, and exploration of the mechanobiology of tumor invasiveness and malignancy. The technical terms were translated using standardized nomenclature. The literature was highly heterogeneous in terms of image acquisition techniques, post-processing, and study design and was generally limited by small and variable cohorts.

CONCLUSIONS

MRE shows potential in predicting tumor consistency, adhesion, and mechanical homogeneity. Furthermore, MRE provides insight into malignant tumor behavior and its relation to tissue mechanics. MRE is still at a preclinical stage, but technical advances, improved understanding of soft tissue rheological impact, and larger samples are likely to enable future clinical introduction.

Management of cavernous sinus meningiomas: Consensus statement on behalf of the EANS skull base section

Introduction

The evolution of cavernous sinus meningiomas (CSMs) might be unpredictable and the efficacy of their treatments is challenging due to their indolent evolution, variations and fluctuations of symptoms, heterogeneity of classifications and lack of randomized controlled trials. Here, a dedicated task force provides a consensus statement on the overall management of CSMs.

Research question

To determine the best overall management of CSMs, depending on their clinical presentation, size, and evolution as well as patient characteristics.

Material and methods

Using the PRISMA 2020 guidelines, we included literature from January 2000 to December 2020. A total of 400 abstracts and 77 titles were kept for full-paper screening.

Results

The task force formulated 8 recommendations (Level C evidence). CSMs should be managed by a highly specialized multidisciplinary team. The initial evaluation of patients includes clinical, ophthalmological, endocrinological and radiological assessment. Treatment of CSM should involve experienced skull-base neurosurgeons or neuro-radiosurgeons, radiation oncologists, radiologists, ophthalmologists, and endocrinologists.

Discussion and conclusion

Radiosurgery is preferred as first-line treatment in small, enclosed, pauci-symptomatic lesions/in elderly patients, while large CSMs not amenable to resection or WHO grade II-III are candidates for radiotherapy. Microsurgery is an option in aggressive/rapidly progressing lesions in young patients presenting with oculomotor/visual/endocrinological impairment. Whenever surgery is offered, open cranial approaches are the current standard. There is limited experience reported about endoscopic endonasal approach for CSMs and the main indication is decompression of the cavernous sinus to improve symptoms. Whenever surgery is indicated, the current trend is to offer decompression followed by radiosurgery.

•

A thorough evaluation of cavernous sinus meningiomas by a multidisciplinary team is mandatory.

•

Microsurgery should be considered for aggressive lesions in young patients.

•

Extended endoscopic approaches can be effective when combined with radiotherapy.

•

Stereotaxic radiotherapy and stereotaxic radiosurgery offer excellent tumour control in small/asymptomatic lesions .

Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype

OBJECTIVE

To determine whether tumor shear stiffness, as measured by magnetic resonance elastography, corresponds with intratumoral consistency and histotype.

MATERIALS AND METHODS

A total of 88 patients with 89 meningiomas (grade 1, 74 typical [13 fibroblastic, 61 non-fibroblastic]; grade 2, 12 atypical; grade 3, 3 anaplastic) were prospectively studied, each undergoing preoperative MRE in conjunction with T1-, T2- and diffusion-weighted imaging. Contrast-enhanced T1-weighted sequences were also obtained. Tumor consistency was evaluated as heterogeneous or homogenous, and graded on a 5-point scale intraoperatively. MRE-determined shear stiffness was associated with tumor consistency by surgeon's evaluation and whole-slide histologic analyses.

RESULTS

Mean tumor stiffness overall was 3.81+/-1.74 kPa (range, 1.57-12.60 kPa), correlating well with intraoperative scoring (r = 0.748; p = 0.001). MRE performed well as a gauge of tumor consistency (AUC = 0.879, 95 % CI: 0.792-0.938) and heterogeneity (AUC = 0.773, 95 % CI: 0.618-0.813), significantly surpassing conventional MR techniques (DeLong test, all p < 0.001 after Bonferroni adjustment). Shear stiffness was independently correlated with both fibrous content (partial correlation coefficient = 0.752; p < 0.001) and tumor cellularity (partial correlation coefficient = 0.547; p < 0.001). MRE outperformed other imaging techniques in distinguishing fibroblastic meningiomas from other histotypes (AUC = 0.835 vs 0.513 ∼ 0.634; all p < 0.05), but showed limited ability to differentiate atypical or anaplastic meningiomas from typical meningiomas (AUC = 0.723 vs 0.616 ∼ 0.775; all p > 0.05). Small (<2.5 cm, n = 6) and intraventricular (n = 2) tumors displayed inconsistencies between MRE and surgeon's evaluation.

CONCLUSIONS

The results of this prospective study provide substantial evidence that preoperative evaluation of meningiomas with MRE can reliably characterize tumor stiffness and spatial heterogeneity to aid neurosurgical planning.

Surgical management of anterior clinoidal meningiomas: consensus statement on behalf of the EANS skull base section

BACKGROUND

The optimal management of clinoidal meningiomas (CMs) continues to be debated.

METHODS

We constituted a task force comprising the members of the EANS skull base committee along with international experts to derive recommendations for the management of these tumors. The data from the literature along with contemporary practice patterns were discussed within the task force to generate consensual recommendations.

RESULTS AND CONCLUSION

This article represents the consensus opinion of the task force regarding pre-operative evaluations, patient's counselling, surgical classification, and optimal surgical strategy. Although this analysis yielded only Class B evidence and expert opinions, it should guide practitioners in the management of patients with clinoidal meningiomas and might form the basis for future clinical trials.

Magnetic Resonance Fingerprinting for Preoperative Meningioma Consistency Prediction

RATIONALE AND OBJECTIVES

Preoperative meningioma consistency prediction is highly beneficial for surgical planning and prognostication. We aimed to use magnetic resonance fingerprinting (MRF)-derived T1 and T2 values to preoperatively predict meningioma consistency.

MATERIALS AND METHODS

A total of 51 patients with meningiomas were enrolled in this study. MRF, T1-weighted imaging, T2-weighted imaging, and diffusion-weighted imaging were performed in all patients before surgery using a 3T MRI scanner. MRF-derived T1 and T2 values, T1-weightd and T2-weighted signal intensities, as well as apparent diffusion coefficient value yield from diffusion-weighted imaging were compared between the soft, moderate and hard meningiomas. Receiver operating characteristic curve analyses were used to determine the diagnostic performance of T1, T2 value, and a combination of T1 and T2 values.

RESULTS

After Bonferroni corrections, quantitative T1 and T2 values yielded from MRF were significantly different between the soft, moderate and hard meningiomas (all p < 0.05). T2 signal intensity was significantly different between the soft and hard, soft and moderate meningiomas (both p < 0.05), whereas was not significantly different between the moderate and hard meningiomas. However, T1 signal intensity and apparent diffusion coefficient value had no significant differences between the soft, moderate and hard meningiomas (all p > 0.05). The combination of T1 and T2 values had greater areas under receiver operating characteristic curve curves compared to individual T1 or T2 value.

CONCLUSION

MRF may help to preoperatively differentiate between the soft, moderate and hard meningiomas and may be useful in guiding the surgical planning.

Usefulness of T2 Relaxation Time for Quantitative Prediction of Meningioma Consistency

BACKGROUND

Meningioma consistency is one of the most critical factors affecting the difficulty of surgery. Although many studies have attempted to predict meningioma consistency via magnetic resonance imaging findings, no definitive method has been established, because most have been based on qualitative evaluations. Therefore, the present study examined the potential of the T2 relaxation time (T2 value), a tissue-specific quantitative parameter, for assessment of meningioma consistency.

METHODS

Eighteen surgically treated meningiomas in 16 patients were included in the present study. Preoperatively, the T2 values of all meningiomas were calculated pixel by pixel, and a T2 value distribution map was generated. A total of 27 tumor specimens (multiple specimens were procured if heterogeneous) were taken from these meningiomas, with each localization identified intraoperatively using image guidance. The consistency of the specimens was measured with a durometer, originally a device for measuring the hardness of material such as elastic rubber, and their water content was subsequently measured using wet and dry measurements.

RESULTS

A significant correlation was found between the T2 values of the matched locations identified by image guidance intraoperatively and the consistency measured using the durometer (r = -0.722; P < 0.01) and the water content (r = 0.621; P = 0.01). In addition, the water content correlated significantly with the durometer consistency (r = -0.677; P < 0.01).

CONCLUSIONS

The T2 values could be a reliable quantitative predictor of meningioma consistency, and the T2 value distribution map, which elucidated the internal structure of the tumor in detail, could provide helpful information for surgical resection.

Consistency of pituitary adenomas: Amounts of collagen types I and III and the predictive value of T2WI MRI

Pituitary adenomas, the most common type of lesion in the sellar region, rank third among all brain tumors, with an incidence of 73-94 cases per 100,000 individuals. Due to its high resolution, MRI is highly efficient in brain imaging and has emerged as the most appropriate method for tumor consistency evaluation. The present study aimed to assess the levels of collagen types I and III in pituitary adenomas with different consistencies and to determine the value of T2-weighted imaging (T2WI) MRI for predicting tumor consistency. A total of 55 patients with pituitary adenomas were divided into the soft and firm tumor groups according to intraoperative tumor consistency. The ratio of the tumor to Pons' signal intensities on T2WI scans was determined. A receiver operating characteristic curve was plotted to assess the specificity and sensitivity of T2WI in predicting tumor consistency. Average optical density (AOD) values for collagen types I (0.046±0.008 vs. 0.052±0.012, P=0.033) and III (0.044±0.008 vs. 0.050±0.010, P=0.016) were significantly lower in the soft tumor group compared with those in the firm tumor group. There was no significant difference in the ratio of the tumor to Pons' signal intensities on T2WI scans. The area under the ROC curve was 0.595±0.078 (P=0.250). The maximum tumor diameter significantly differed between the soft and firm tumor groups (P=0.001). AOD values for collagen types I and III were significantly correlated with the maximum tumor diameter (P<0.001). The amounts of collagen types I and III were elevated in firm pituitary tumors compared with the soft ones. The ratio of tumor to Pons' signal intensities on T2WI scans was not able to accurately predict tumor consistency. The size of pituitary adenomas may be associated with the expression levels of collagen types I and III.

Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings

BACKGROUND

Preoperative assessment of the consistency of pituitary macroadenomas (PMA) might be needed for surgical planning.

PURPOSE

To investigate the diagnostic performance of radiomics models based on multiparametric magnetic resonance imaging (mpMRI) for preoperatively evaluating the tumor consistency of PMA.

STUDY TYPE

Retrospective.

POPULATION

One hundred and fifty-six PMA patients (soft consistency, N = 104 vs. hard consistency, N = 52), divided into training (N = 108) and test (N = 48) cohorts. The tumor consistency was determined on surgical findings.

FIELD STRENGTH/SEQUENCE

T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1CE), and T2-weighted imaging (T2WI) using spin-echo sequences with a 3.0-T scanner.

ASSESSMENT

An automated three-dimensional (3D) segmentation was performed to generate the volume of interest (VOI) on T2WI, then T1WI/T1CE were coregistered to T2WI. A total of 388 radiomic features were extracted on each VOI of mpMRI. The top-discriminative features were identified using the minimum-redundancy maximum-relevance method and 0.632+ bootstrapping. The radiomics models based on each sequence and their combinations were established via the random forest (RF) and support vector machine (SVM), and independently evaluated for their ability in distinguishing PMA consistency.

STATISTICAL TESTS

Mann-Whitney U-test and Chi-square test were used for comparison analysis. The area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and relative standard deviation (RSD) were calculated to evaluate each model's performance. ACC with P-value<0.05 was considered statistically significant.

RESULTS

Eleven mpMRI-based features exhibited statistically significant differences between soft and hard PMA in the training cohort. The radiomics model built on combined T1WI/T1CE/T2WI demonstrated the best performance among all the radiomics models with an AUC of 0.90 (95% confidence interval [CI]: 0.87-0.92), ACC of 0.87 (CI: 0.84-0.89), SEN of 0.83 (CI: 0.81-0.85), and SPE of 0.87 (CI: 0.85-0.99) in the test cohort.

DATA CONCLUSION

Radiomic features based on mpMRI have good performance in the presurgical evaluation of PMA consistency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Petroclival meningiomas: update of current treatment and consensus by the EANS skull base section

BACKGROUND

The optimal management of petroclival meningiomas (PCMs) continues to be debated along with several controversies that persist.

METHODS

A task force was created by the EANS skull base section along with its members and other renowned experts in the field to generate recommendations for the management of these tumors. To achieve this, the task force reviewed in detail the literature in this field and had formal discussions within the group.

RESULTS

The constituted task force dealt with the existing definitions and classifications, pre-operative radiological investigations, management of small and asymptomatic PCMs, radiosurgery, optimal surgical strategies, multimodal treatment, decision-making, and patient's counselling.

CONCLUSION

This article represents the consensually derived opinion of the task force with respect to the management of PCMs.

How to predict the consistency and vascularity of meningiomas by MRI: an institutional experience

OBJECTIVE

In surgery for meningiomas tumor location and extension is currently the only MRI characteristic used to predict the feasibility and difficulty of the resection. Key surgical tumor characteristics such as consistency and vascularity remain obscured until the tumor is exposed. We therefore aimed to identify MRI sequences able to predict these crucial meningioma features.

METHODS

We retrospectively reviewed our imaging database on cranial meningiomas and correlated MRI T2W, T1W, and FLAIR images with the consistency and vascularity reported by the surgeon in the operative notes. The reported consistency was classified into three grades [°I (soft) to °III (hard)]. Vascularity was grouped into little (°I) versus strong (°II). MRI signal intensity (SI) ratios were calculated with ROIs in the meningioma, the buccinator muscle and the frontal white matter.

RESULTS

Of the 172 reviewed patients, 44 met the strict inclusion criteria with respect to the quality of the OR notes. The included meningiomas were located at the convexity (11/44), falcine (3/44), skull base (14/44), and posterior fossa (16/44). Twenty-four meningiomas (54.5%) were classified as consistency grade (°)I, seven (15.9%) °II, and thirteen (29.5%) °III. The grade of vascularization was little in 12 and strong in 14. The higher the ratio on T2W images the softer (p = 0.020) and the more vascularized (p = 0.001) the tumor presented.

DISCUSSION

T2W MR images may be helpful to characterize meningiomas with regard to the expected consistency and grade of vascularization.

Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers

BACKGROUND

The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic resonance imaging and the tumor elasticity measured by intraoperative ultrasound elastography (IOUS-E) as a reference parameter.

METHODS

A retrospective analysis was performed on supratentorial meningiomas that were operated on between March 2018 and July 2020. Cases with IOUS-E studies were included. A semiquantitative analysis of elastograms was used to define the meningioma consistency. MRIs were preprocessed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression, Naive Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve and classification accuracy.

RESULTS

Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast, apparent diffusion coefficient map, and T2-weighted images. The combination of Information Gain and ReliefF filters with the Naive Bayes algorithm resulted in an area under the curve of 0.961 and classification accuracy of 94%.

CONCLUSIONS

We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative magnetic resonance imaging (T2-weighted, T1-weighted post-contrast, and apparent diffusion coefficient map).

Predicting the consistency of intracranial meningiomas using apparent diffusion coefficient maps derived from preoperative diffusion-weighted imaging

OBJECTIVE

The consistency of meningiomas is a critical factor affecting the difficulty of resection, operative complications, and operative time. The apparent diffusion coefficient (ADC) is derived from diffusion-weighted imaging (DWI) and is calculated using two optimized b values. While the results of comparisons between the standard ADC and the consistency of meningiomas vary, the shifted ADC has been reported to be strongly correlated with liver stiffness. The purpose of the present prospective cohort study was to determine whether preoperative standard and shifted ADC maps predict the consistency of intracranial meningiomas.

METHODS

Standard (b values 0 and 1000 sec/mm2) and shifted (b values 200 and 1500 sec/mm2) ADC maps were calculated using preoperative DWI in patients undergoing resection of intracranial meningiomas. Regions of interest (ROIs) were placed within the tumor on standard and shifted ADC maps and registered on the navigation system. Tumor tissue located at the registered ROI was resected through craniotomy, and its stiffness was measured using a durometer. The cutoff point lying closest to the upper left corner of a receiver operating characteristic (ROC) curve was determined for the detection of tumor stiffness such that an ultrasonic aspirator or scissors was always required for resection. Each tumor tissue sample with stiffness greater than or equal to or less than this cutoff point was defined as hard or soft tumor, respectively.

RESULTS

For 76 ROIs obtained from 25 patients studied, significant negative correlations were observed between stiffness and the standard ADC (ρ = -0.465, p < 0.01) and the shifted ADC (ρ = -0.490, p < 0.01). The area under the ROC curve for detecting hard tumor (stiffness ≥ 20.8 kPa) did not differ between the standard ADC (0.820) and the shifted ADC (0.847) (p = 0.39). The positive predictive value (PPV) for the combination of a low standard ADC and a low shifted ADC for detecting hard tumor was 89%. The PPV for the combination of a high standard ADC and a high shifted ADC for detecting soft tumor (stiffness < 20.8 kPa) was 81%.

CONCLUSIONS

A combination of standard and shifted ADC maps derived from preoperative DWI can be used to predict the consistency of intracranial meningiomas.

Features of tumor texture influence surgery and outcome in intracranial meningioma

Background

Texture-related factors such as consistency, vascularity, and adherence vary considerably in meningioma and are thought to be linked with surgical resectability and morbidity. However, data analyzing the true impact of meningioma texture on the surgical management is sparse.

Methods

Patients with intracranial meningioma treated between 08/2014 and 04/2018 at our institution were prospectively collected for demographics, clinical presentation, histology, and surgical treatment with related morbidity and extend of resection. Tumor characteristics were reported by the surgeon using a standardized questionnaire including items such as tumor consistency, homogeneity, vascularization, and adherence to surrounding neurovascular structure and analyzed for their impact surgical outcome parameters using univariate and logistic regression analyses.

Results

Tumor texture-related parameters of 300 patients (72.3% female) with meningioma were analyzed. Meningioma localizations were grouped into 3 different cohorts namely convexity, skull base, and posterior. Postoperative occurrence of a neurological deficit (transient 23.0%; permanent 6.1%) was associated with the duration of surgery (P = .001), size of tumor (P = .046), tumor vascularization (P = .015), and adherence to neurovascular structures (P = .002). Coherently, the duration of surgery (mean 230.99 ± 101.33 min) was associated with size of tumor (P < .0001), vascularization (P < .0001), and adherence (P < .0001). Similar associations were recapitulated in subgroup analyses of different tumor localizations. Noteworthy, tumor rigidity had no significant impact on time of surgery and neurological outcome.

Conclusions

Our analysis demonstrates that tumor texture has an impact on the surgical management of meningioma and provides data that tumor vascularization and adherence are significant factors influencing surgical outcome whereas the influence of tumor consistency has less impact than previously thought.

Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma

The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.

Microcystic Meningiomas: MRI-Pathologic Correlation

BACKGROUND AND PURPOSE

Microcystic meningiomas (MM) are a distinctive, rare subtype of Grade I meningiomas with limited radiological descriptions. We intend to identify unique imaging phenotypes and seek radiopathological correlations.

METHODS

Retrospective analysis of histopathologically proven MM was undertaken. Clinicodemographic profiles, imaging, and histopathological characteristics were recorded. Spearman rank correlations among radiological and pathological attributes were performed.

RESULTS

Twenty-eight cases were analyzed (mean age = 45.5 years; M:F = 1:1.54; mean volume = 50.1 mL; supratentorial n = 27). Most lesions were markedly T2 hyperintense (higher than peritumoral brain edema-a unique finding) (89.3%) and showed invariable diffusion restriction, severe peritumoral brain edema (edema index >2 in 64.3%), a "storiform" pattern on T2-weighted images (T2WI) (75%), reticular pattern on postcontrast T1 (78.6%)/diffusion-weighted images (DWI) (65.4%), hyperperfusion, T1 hypointensity (84.6%), and absence of blooming on susceptibility-weighted image (80.9%). Storiform/reticular morphology correlated with large cysts on histopathology (ρ = .56; P = .005753).  Lesion dimension positively correlated with reticular morphology on imaging (ρ = .59; P = .001173), higher flow voids (ρ = .65; P = .00027), and greater microcystic changes on histopathology (ρ = .51; P = .006778). Peritumoral brain edema was higher for lesions demonstrating greater angiomatous component (ρ = .46; P = .014451).

CONCLUSIONS

We have elucidated varied neuroimaging features and highlighted pathological substrates of crucial imaging findings of MM. MM ought to be considered as an imaging possibility in an extra-axial lesion with a marked hypodensity on noncontrast computed tomography, markedly T2-hyperintense/T1-hypointense signal, and a storiform/reticular pattern on T2W/GdT1w//DWI.

Development and clinical validation of a grading system for pituitary adenoma consistency

OBJECTIVE

Pituitary adenoma (PA) consistency, or texture, is an important intraoperative characteristic that may dictate operative dissection techniques and/or instruments used for tumor removal during endoscopic endonasal approaches (EEAs). The impact of PA consistency on surgical outcomes has yet to be elucidated.

METHODS

The authors developed an objective 5-point grading scale for PA consistency based on intraoperative characteristics, including ease of tumor debulking, manipulation, and instrument selection, ranging from cystic/hemorrhagic tumors (grade 1) to calcified tumors (grade 5). The proposed grading system was prospectively assessed in 306 consecutive patients who underwent an EEA for PAs, and who were subsequently analyzed for associations with surgical outcomes, including extent of resection (EOR) and complication profiles.

RESULTS

Institutional database review identified 306 patients who underwent intraoperative assessment of PA consistency, of which 96% were macroadenomas, 70% had suprasellar extension, and 44% had cavernous sinus invasion (CSI). There were 214 (69.9%) nonfunctional PAs and 92 functional PAs (31.1%). Distribution of scores included 15 grade 1 tumors (4.9%), 112 grade 2 tumors (36.6%), 125 grade 3 tumors (40.8%), 52 grade 4 tumors (17%), and 2 grade 5 tumors (0.7%). Compared to grade 1/2 and grade 3 PAs, grade 4/5 PAs were significantly larger (22.5 vs 26.6 vs 27.4 mm, p < 0.01), more likely to exhibit CSI (39% vs 42% vs 59%, p < 0.05), and trended toward nonfunctionality (67% vs 68% vs 82%, p = 0.086). Although there was no association between PA consistency and preoperative headaches or visual dysfunction, grade 4/5 PAs trended toward preoperative (p = 0.058) and postoperative panhypopituitarism (p = 0.066). Patients with preoperative visual dysfunction experienced greater improvement if they had a grade 1/2 PA (p < 0.05). Intraoperative CSF leaks were noted in 32% of cases and were more common with higher-consistency-grade tumors (p = 0.048), although this difference did not translate to postoperative CSF leaks. Gross-total resection (%) was more likely with lower PA consistency score as follows: grade 1/2 (60%), grade 3 (50%), grade 4/5 (44%; p = 0.045). Extracapsular techniques were almost exclusively performed in grade 4/5 PAs. Assignment of scores showed low variance and high reproducibility, with an intraclass correlation coefficient of 0.905 (95% CI 0.815-0.958), indicating excellent interrater reliability.

CONCLUSIONS

These findings demonstrate clinical validity of the proposed intraoperative grading scale with respect to PA subtype, neuroimaging features, EOR, and endocrine complications. Future studies will assess the relation of PA consistency to preoperative MRI findings to accurately predict consistency, thereby allowing the surgeon to tailor the exposure and prepare for varying resection strategies.

Neurosarcoidosis Presenting as Isolated Bilateral Cerebellopontine Angle Tumors: Case Report and Review of the Literature

OBJECTIVES

To describe a unique case of isolated bilateral sarcoidosis of the cerebellopontine angle as well as the related imaging in the case. To conduct a literature review of the published articles regarding sarcoidosis of the cerebellopontine angle.

DATA SOURCES

Representative case report from a single institution as well as PubMed and Scopus database searches.

METHODS

In addition to a retrospective review, all published case reports and case series of sarcoidosis involving the cerebellopontine angle from 1960 to July 2018 in the English language were reviewed. Demographic data, presenting symptoms, and outcomes were collected.

RESULTS

We identified 8 total cases with pertinent clinical information that were included.

CONCLUSIONS

Isolated neurosarcoidosis of the cerebellopontine angle is an exceptionally rare phenomenon that, on history and imaging, presents similar to more common retrocochlear pathologies. Surgery may be required in large lesions unresponsive to traditional medical therapy with immunosuppression.

Automated Meningioma Segmentation in Multiparametric MRI : Comparable Effectiveness of a Deep Learning Model and Manual Segmentation

PURPOSE

Volumetric assessment of meningiomas represents a valuable tool for treatment planning and evaluation of tumor growth as it enables a more precise assessment of tumor size than conventional diameter methods. This study established a dedicated meningioma deep learning model based on routine magnetic resonance imaging (MRI) data and evaluated its performance for automated tumor segmentation.

METHODS

The MRI datasets included T1-weighted/T2-weighted, T1-weighted contrast-enhanced (T1CE) and FLAIR of 126 patients with intracranial meningiomas (grade I: 97, grade II: 29). For automated segmentation, an established deep learning model architecture (3D deep convolutional neural network, DeepMedic, BioMedIA) operating on all four MR sequences was used. Segmentation included the following two components: (i) contrast-enhancing tumor volume in T1CE and (ii) total lesion volume (union of lesion volume in T1CE and FLAIR, including solid tumor parts and surrounding edema). Preprocessing of imaging data included registration, skull stripping, resampling, and normalization. After training of the deep learning model using manual segmentations by 2 independent readers from 70 patients (training group), the algorithm was evaluated on 56 patients (validation group) by comparing automated to ground truth manual segmentations, which were performed by 2 experienced readers in consensus.

RESULTS

Of the 56 meningiomas in the validation group 55 were detected by the deep learning model. In these patients the comparison of the deep learning model and manual segmentations revealed average dice coefficients of 0.91 ± 0.08 for contrast-enhancing tumor volume and 0.82 ± 0.12 for total lesion volume. In the training group, interreader variabilities of the 2 manual readers were 0.92 ± 0.07 for contrast-enhancing tumor and 0.88 ± 0.05 for total lesion volume.

CONCLUSION

Deep learning-based automated segmentation yielded high segmentation accuracy, comparable to manual interreader variability.

REVIEW: MR elastography of brain tumors

•

MR elastography allows non-invasive quantification of the shear modulus of tissue.

•

MRE correlates with intra-operative consistency of meningiomas, pituitary adenomas.

•

Reported shear modulus values are widely distributed and overlap.

•

Meningiomas were the stiffest tumor-type relative to normal appearing white matter.

•

Studies are needed to determine clinical applications of MRE in neuro-oncology.

MR elastography allows non-invasive quantification of the shear modulus of tissue, i.e. tissue stiffness and viscosity, information that offers the potential to guide presurgical planning for brain tumor resection. Here, we review brain tumor MRE studies with particular attention to clinical applications. Studies that investigated MRE in patients with intracranial tumors, both malignant and benign as well as primary and metastatic, were queried from the Pubmed/Medline database in August 2018. Reported tumor and normal appearing white matter stiffness values were extracted and compared as a function of tumor histopathological diagnosis and MRE vibration frequencies. Because different studies used different elastography hardware, pulse sequences, reconstruction inversion algorithms, and different symmetry assumptions about the mechanical properties of tissue, effort was directed to ensure that similar quantities were used when making inter-study comparisons. In addition, because different methodologies and processing pipelines will necessarily bias the results, when pooling data from different studies, whenever possible, tumor values were compared with the same subject's contralateral normal appearing white matter to minimize any study-dependent bias. The literature search yielded 10 studies with a total of 184 primary and metastatic brain tumor patients. The group mean tumor stiffness, as measured with MRE, correlated with intra-operatively assessed stiffness of meningiomas and pituitary adenomas. Pooled data analysis showed significant overlap between shear modulus values across brain tumor types. When adjusting for the same patient normal appearing white matter shear modulus values, meningiomas were the stiffest tumor-type. MRE is increasingly being examined for potential in brain tumor imaging and might have value for surgical planning. However, significant overlap of shear modulus values between a number of different tumor types limits applicability of MRE for diagnostic purposes. Thus, further rigorous studies are needed to determine specific clinical applications of MRE for surgical planning, disease monitoring and molecular stratification of brain tumors.

Can an Imaging Marker of Consistency Predict Intraoperative Experience and Clinical Outcomes for Vestibular Schwannomas? A Retrospective Review

Objective  The main purpose of this article is to determine if vestibular schwannoma consistency as determined by tissue intensity on T2-weighted magnetic resonance imagings (MRIs) is predictive of intraoperative experience and postoperative clinical outcomes. Study Design  Retrospective chart review. Setting  Tertiary referral center. Patients  Seventy-seven patients diagnosed with vestibular schwannomas who were treated with microsurgical resection. Intervention  Diagnostic. Main Outcome Measures  Intraoperative measures include totality of resection, surgical time and cranial nerve VII stimulation and postoperative measures include House-Brackmann grade and perioperative complications. Results  Tumor consistency determined via tissue intensity on MRI was only found to correlate with surgical time, with a softer tumor being associated with a longer surgical time ( p  < 0.0001). However, this was primarily driven by tumor volume with larger tumors being associated with longer surgical time based on multivariate analysis. None of the other intraoperative or postoperative measures considered were found to correlate with tumor consistency. Conclusions  Tumor consistency determined by MRI is not predictive of intraoperative experience or postoperative outcomes in vestibular schwannomas. Tumor volume is the strongest driver of these outcome measures as opposed to tumor consistency.

New insights into the genomic landscape of meningiomas identified FGFR3 in a subset of patients with favorable prognoses

Background: With a prevalence of 170 000 adults in the US alone, meningiomas are the most common primary intracranial tumors. The management of skull base meningiomas is challenging due to their complexity and proximity to crucial nearby structures. The identification of oncogenic mutations has provided further insights into the tumorigenesis of meningioma and the possibility of targeted therapy.

This study aimed to further investigate the association of mutational profiles with anatomical distribution, histological subtype, WHO grade, and recurrence in patients with meningioma.

Methods: Tissue samples were collected from 71 patients diagnosed with meningioma from 2008 to 2016. A total of 51 cases were skull based. Samples were subjected to targeted sequencing using a next generation customized cancer gene panel (n = 66 genes analyzed).

Results: We detected genomic alterations (GAs) in 68 tumors, averaging 1.56 ± 1.07 genomic alterations (GAs) per sample. NF2 was the most frequently altered gene (36/71 cases). Interestingly, we identified a number of mutations in non-NF2 genes, including a hotspot TERTp c.−124: G > A mutation that may be related to poor prognosis and FGFR3 mutations that may represent biomarkers of a favorable prognosis as reported in other cancers.

Conclusions: We demonstrate that comprehensive genomic profiling in our population can reveal a potential new prognostic biomarkers of skull base meningioma. These mutations can enhance diagnostic accuracy and clinical decision-making. Among our findings were the identification of a TERTp mutation and the first report of FGFR3 mutations that may represent biomarkers for the identification of skull base meningioma patients with a favorable prognosis.

Tensor-valued diffusion MRI in under 3 minutes: an initial survey of microscopic anisotropy and tissue heterogeneity in intracranial tumors

PURPOSE

To evaluate the feasibility of a 3-minutes protocol for assessment of the microscopic anisotropy and tissue heterogeneity based on tensor-valued diffusion MRI in a wide range of intracranial tumors.

METHODS

B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, adenomas, and metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MK_A ) and isotropic kurtosis (MK_I ), respectively. An extensive imaging protocol was compared with a 3-minutes protocol.

RESULTS

The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MK_A = 0.29 ± 0.06 vs. 0.45 ± 0.08, P = 0.003). Metastases had higher tissue heterogeneity (MK_I = 0.57 ± 0.07) than both the glioblastomas (0.44 ± 0.06, P < 0.001) and meningiomas (0.46 ± 0.06, P = 0.03).

CONCLUSION

Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.

Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading

OBJECTIVE

Meningioma grading is relevant to therapy decisions in complete or partial resection, observation, and radiotherapy because higher grades are associated with tumor growth and recurrence. The differentiation of low and intermediate grades is particularly challenging. This study attempts to apply radiomics-based shape and texture analysis on routine multiparametric magnetic resonance imaging (MRI) from different scanners and institutions for grading.

METHODS

We used MRI data (T1-weighted/T2-weighted, T1-weighted-contrast-enhanced [T1CE], fluid-attenuated inversion recovery [FLAIR], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC]) of grade I (n = 46) and grade II (n = 25) nontreated meningiomas with histologic workup. Two experienced radiologists performed manual tumor segmentations on FLAIR, T1CE, and ADC images in consensus. The MRI data were preprocessed through T1CE and T1-subtraction, coregistration, resampling, and normalization. A PyRadiomics package was used to generate 990 shape/texture features. Stepwise dimension reduction and robust radiomics feature selection were performed. Biopsy results were used as standard of reference.

RESULTS

Four statistically independent radiomics features were identified as showing the strongest predictive values for higher tumor grades: roundness-of-FLAIR-shape (area under curve [AUC], 0.80), cluster-shades-of-FLAIR/T1CE-gray-level (AUC, 0.80), DWI/ADC-gray-level-variability (AUC, 0.72), and FLAIR/T1CE-gray-level-energy (AUC, 0.76). In a multivariate logistic regression model, the combination of the features led to an AUC of 0.91 for the differentiation of grade I and grade II meningiomas.

CONCLUSIONS

Our results indicate that radiomics-based feature analysis applied on routine MRI is viable for meningioma grading, and a multivariate logistic regression model yielded strong classification performances. More advanced tumor stages are identifiable through certain shape parameters of the lesion, textural patterns in morphologic MRI sequences, and DWI/ADC variability.

Tumor to Cerebellar Peduncle T2-Weighted Imaging Intensity Ratio Fails to Predict Pituitary Adenoma Consistency

Object  The consistency of pituitary macroadenomas affects the complexity of surgical resection. On T2-weighted (T2W) imaging, the intensity ratio of the tumor to the cerebellar peduncle (tumor to cerebellar peduncle T2-weighted imaging intensity [TCTI] ratio) correlates with meningioma consistency. We aimed to determine the correlation of this radiographic finding with pituitary macroadenoma consistency and to determine whether it can be used for preoperative planning. Methods  We performed a retrospective evaluation of 196 patients with macroadenomas who underwent endoscopic transsphenoidal resection from January 2012 to June 2017. Macroadenoma consistency was determined by one senior neurosurgeon at the time of surgery. Axial and coronal T2W magnetic resonance imaging images were evaluated retrospectively, and adenoma size, Knosp grade, suprasellar extension and TCTI were calculated. Results  The mean TCTI ratio was 1.70 (95% confidence interval [CI]: 1.65-1.75). Intraoperatively, 140 (71.4%) adenomas were classified as soft and 48 (24.5%) as fibrous. Gross total resection was achieved in 66.7% of fibrous adenomas and in 86.4% of soft adenomas ( p  = 0.007). The mean ratio was 1.68 (95% CI: 1.62-1.74) for soft tumors and 1.76 (95%CI: 1.67-1.84) for fibrous tumors. There was no difference in the mean TCTI ratio between groups. Lactotroph and somatotroph adenomas had a lower mean TCTI ratio compared with other functioning and nonfunctioning adenomas with a mean TCTI of 1.52 compared with 1.77. Conclusions  In this retrospective cohort study, we found that the TCTI ratio does not correlate with tumor consistency. We also noted that the TCTI ratio is increased in prolactin and growth hormone-secreting adenomas.

Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI

Objectives

Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations.

Methods

We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE.

Results

The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE.

Conclusions

The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity.

Key Points

• Deep learning allows for accurate meningioma detection and segmentation

• Deep learning helps clinicians to assess patients with meningiomas

• Meningioma monitoring and treatment planning can be improved

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5595-8) contains supplementary material, which is available to authorized users.