Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

T2-Weighted Imaging and Apparent Diffusion Coefficient Histogram Parameters Predict Meningioma Consistency

RATIONALE AND OBJECTIVES

Preoperative prediction of meningioma consistency is of great clinical value for risk stratification and surgical approach selection. However, to date, objective quantitative criteria for predicting meningioma consistency have not been developed. This study aimed to investigate the predictive value of magnetic resonance imaging (MRI) T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) histogram parameters for meningioma consistency.

MATERIALS AND METHODS

We retrospectively analyzed the clinical, preoperative MRI, and pathological data of 103 patients with histopathologically confirmed meningiomas. Histogram parameters (mean, variance, skewness, kurtosis, Perc.01%, Perc.10%, Perc.50%, Perc.90%, and Perc.99%) were calculated automatically on the whole tumor using MaZda software. Chi-square test, Mann-Whitney's U test, or independent samples t-test was used to compare clinical, conventional MRI features, and histogram parameters between soft and hard meningiomas. Receiver operating characteristic curve and binary logistic regression analysis were employed to assess the predictive performance of T2WI and ADC histogram parameters.

RESULTS

Tumor enhancement was the only conventional MRI feature that was statistically different between soft and hard meningiomas. ADCmean, ADCp1, ADCp10, and ADCp50 among ADC histogram parameters, and T2mean, T2p1, T2p10, T2p50, T2p90, and T2p99 among T2WI histogram parameters showed statistically significant differences between soft and hard meningiomas (all P < 0.05). We found that all combined variables (combinedall) had the best accuracy in predicting meningioma consistency, with area under the curve, sensitivity, specificity, accuracy, positive predictive, and negative predictive values of 0.873 (0.804-0.941), 88.89%, 67.50%, 80.58%, 81.20%, and 79.40%, respectively. Among them, combinedT2 is the most beneficial for predicting meningioma consistency.

CONCLUSION

CombinedT2 demonstrated better predictive performance for meningioma consistency than combinedADC. T2WI and ADC histogram parameters may be imaging markers for predicting meningioma consistency.

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.

Modeling Meningiomas: Optimizing Treatment Approach

Preclinical meningioma models offer a setting to test molecular mechanisms of tumor development and targeted treatment options but historically have been challenging to generate. Few spontaneous tumor models in rodents have been established, but cell culture and in vivo rodent models have emerged along with artificial intelligence, radiomics, and neural networks to differentiate the clinical heterogeneity of meningiomas. We reviewed 127 studies using PRISMA guideline methodology, including laboratory and animal studies, that addressed preclinical modeling. Our evaluation identified that meningioma preclinical models provide valuable molecular insight into disease progression and effective chemotherapeutic and radiation approaches for specific tumor types.

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.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Benefits of Combined MRI Sequences in Meningioma Consistency Prediction: A Prospective Study of 287 Consecutive Patients

Objective  Consistency of meningiomas is one of the most important factors affecting the completeness of removal and major risks of meningioma surgery. This study used preoperative magnetic resonance imaging (MRI) sequences in single and in combination to predict meningioma consistency. Methods  The prospective study included 287 intracranial meningiomas operated on by five attending neurosurgeons at Chiang Mai University Hospital from July 2012 through June 2020. The intraoperative consistency was categorized in four grades according to the method of surgical removal and intensity of ultrasonic aspirator, then correlated with preoperative tumor signal intensity pattern on MRI including T1-weighted image, T2-weighted image (T2WI), fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted image (DWI), which were described as hypointensity, isointensity, and hyperintensity signals which were blindly interpreted by one neuroradiologist. Results  Among 287 patients, 29 were male and 258 female. The ages ranged from 22 to 83 years. A total of 189 tumors were situated in the supratentorial space and 98 were in the middle fossa and infratentorial locations. Note that 125 tumors were found to be of soft consistency (grades 1, 2) and 162 tumors of hard consistency (grades 3, 4). Hyperintensity signals on T2WI, FLAIR, and DWI were significantly associated with soft consistency of meningiomas (relative risk [RR] 2.02, 95% confidence interval [CI] 1.35-3.03, p  = 0.001, RR 2.19, 95% CI 1.43-3.35, p  < 0.001, and RR 1.47, 95% CI 1.02-2.11, p  = 0.037, respectively). Further, chance to be soft consistency significantly increased when two and three hyperintensity signals were combined (RR 2.75, 95% CI 1.62-4.65, p ≤ 0.001, RR 2.79, 95% CI 1.58-4.93, p  < 0.001, respectively). Hypointensity signals on T2WI, FLAIR, and DWI were significantly associated with hard consistency of meningiomas (RR 1.82, 95% CI 1.18-2.81, p  = 0.007, RR 1.80, 95% CI 1.15-2.83, p  = 0.010, RR 1.67, 95% CI 1.07-2.59, p  = 0.023, respectively) and chance to be hard consistency significantly increased when three hypointensity signals were combined (RR 1.82, 95% CI 1.11-2.97, p  = 0.017). Conclusion  T2WI, FLAIR, and DWI hyperintensity signals of the meningiomas was solely significantly associated with soft consistency and predictive value significantly increased when two and three hyperintensity signals were combined. Each of hypointensity signals on T2WI, FLAIR, and DWI was significantly associated with hard consistency of tumors and tendency to be hard consistency significantly increased when hypointensity was found in all three sequences.

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.

Predictive Factors of Favorable Visual Outcomes After Surgery of Tuberculum Sellae Meningiomas: A Multicenter Retrospective Cohort Study

BACKGROUND

Because of their proximity to the visual structures, tuberculum sellae meningiomas are frequently revealed by ophthalmologic impairment. The goal of surgery is gross total resection and improvement of visual function. The purpose of the present study was to identify the predictors of favorable visual outcomes after surgery of tuberculum sellae meningioma.

METHODS

We retrospectively collected tuberculum sellae meningiomas treated at 2 neurosurgical centers from 2010 to 2020. We collected the clinical, imaging and surgical data and analyzed their effects on the visual outcome. A favorable visual outcome was defined as an increase in visual acuity of ≥0.2 point and/or an increase of >25% of the visual field or complete recovery.

RESULTS

A total of 50 patients were included. At 4 months after surgery, 30 patients (60%) had experienced visual improvement. The predictors of a favorable visual outcome were a symptom duration of <6 months, preoperative visual acuity >0.5, a smaller tumor size, and tumor with T2-weighted/fluid attenuated inversion recovery hypersignal on magnetic resonance imaging. During surgery, a soft tumor and a clear brain-tumor interface were associated with favorable visual outcomes. Preoperative optic coherence tomography measurements of the retinal nerve fiber layer thickness >80 μM and ganglion cell complex thickness >70 μM were also associated with a better ophthalmologic outcome.

CONCLUSIONS

In tuberculum sellae meningiomas, rapid surgical treatment must be performed to optimize vision improvement. A hyperintense lesion on T2-weighted/fluid attenuated inversion recovery magnetic resonance imaging and minor vision impairment at the initial ophthalmologic presentation might give hope for a favorable outcome. Performing optic coherence tomography measurements before surgery could clarify patients' expectations regarding their recovery.

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

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

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

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

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

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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.

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.

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.

Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics

Objectives

The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.

Methods

A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada’s consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination.

Results

A total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts.

Conclusions

The presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.

Relationship between Shear Stiffness Measured by MR Elastography and Perfusion Metrics Measured by Perfusion CT of Meningiomas

BACKGROUND AND PURPOSE

When managing meningiomas, intraoperative tumor consistency and histologic subtype are indispensable factors influencing operative strategy. The purposes of this study were the following: 1) to investigate the correlation between stiffness assessed with MR elastography and perfusion metrics from perfusion CT, 2) to evaluate whether MR elastography and perfusion CT could predict intraoperative tumor consistency, and 3) to explore the predictive value of stiffness and perfusion metrics in distinguishing among histologic subtypes of meningioma.

MATERIALS AND METHODS

Mean tumor stiffness and relative perfusion metrics (blood flow, blood volume, and MTT) were calculated (relative to normal brain tissue) for 14 patients with meningiomas who underwent MR elastography and perfusion CT before surgery (cohort 1). Intraoperative tumor consistency was graded by a neurosurgeon in 18 patients (cohort 2, comprising the 14 patients from cohort 1 plus 4 additional patients). The correlation between tumor stiffness and perfusion metrics was evaluated in cohort 1, as was the ability of perfusion metrics to predict intraoperative tumor consistency and discriminate histologic subtypes. Cohort 2 was analyzed for the ability of stiffness to determine intraoperative tumor consistency and histologic subtypes.

RESULTS

The relative MTT was inversely correlated with stiffness (P = .006). Tumor stiffness was positively correlated with intraoperative tumor consistency (P = .01), while perfusion metrics were not. Relative MTT significantly discriminated transitional meningioma from meningothelial meningioma (P = .04), while stiffness did not significantly differentiate any histologic subtypes.

CONCLUSIONS

In meningioma, tumor stiffness may be useful to predict intraoperative tumor consistency, while relative MTT may potentially correlate with tumor stiffness and differentiate transitional meningioma from meningothelial meningioma.

Meningiomas: A review of general, histopathological, clinical and molecular characteristics

OBJECTIVES

In this review, the main histological and molecular characteristics of meningiomas will be addressed, as well as the aspects most related to clinical conditions, treatment, and survival of patients, enabling a better understanding of these tumors behavior.

METHODS

This study was conducted with the search for published studies available on NCBI, PubMed, MEDLINE, Scielo and Google Scholar. Relevant documents have been identified and 50 articles were selected.

RESULTS

The main points about meningiomas were characterized, as well as the histological presence of spontaneous necrosis in grade I and brain invasion as diagnostic criteria, their molecular origin related to deletion of chromosome 22 and mutations in theNF2 and TERT genes, in addition to their clinical characteristics. The preferential treatment remains the total resection of the tumor.

CONCLUSION

The information about meningiomas is well known and necessary, but it is expected that more work will emerge related to the behavior of these tumors, and that the scientific community will obtain more clarity about the best ways to conduct the patients treatment.

Differentiation Between Ischemic and Hemorrhagic Strokes - A Pilot Study with Transtemporal Investigation of Brain Parenchyma Elasticity Using Ultrasound Shear Wave Elastography

INTRODUCTION

 Ultrasound shear wave elastography is well established in diagnostics of several parenchymatous organs and is recommended by respective guidelines. So far, research about applications in relevant neurological conditions is missing, especially in adults. Here we aimed to examine the method for the differentiation of ischemic (IS) and hemorrhagic strokes (HS) and cerebral mass effects.

MATERIALS & METHODS

 50 patients with a confirmed diagnosis of HS or IS were enrolled in this prospective study. 2D shear wave elastography was performed on the ipsilateral and the contralateral side with a modified acoustic radiation force impulse (ARFI) technique (ElastPQ mode, Philips). Lesion volumetry was conducted based on computed tomography data for correlation with elastography results.

RESULTS

 Elastography measurements (EM) revealed a highly significant difference between IS and HS with mean values of 1.94 and 5.50 kPa, respectively (p < 0.00 001). Mean values of brain tissue on the non-affected side were almost identical (IS 3.38 (SD = 0.63); HS 3.35 (SD = 0.66); p = 0.91). With a sensitivity of 0.98 and a specificity of 0.99, a cut-off value of 3.52 kPa for discrimination could be calculated. There was a significant correlation of mass effect represented by midline shift and EM values on the contralateral side (Pearson correlation coefficient = 0.68, p < 0.0003).

CONCLUSION

 Ultrasound brain parenchyma elastography seems to be a reliable sonographic method for discriminating between large IS and HS and for detecting and tracking conditions of intracerebral mass effects.

Predicting meningioma consistency and brain-meningioma interface with intraoperative strain ultrasound elastography: a novel application to guide surgical strategy

OBJECTIVE

Providing new tools to improve surgical planning is considered a main goal in meningioma treatment. In this context, two factors are crucial in determining operating strategy: meningioma-brain interface and meningioma consistency. The use of intraoperative ultrasound (ioUS) elastosonography, a real-time imaging technique, has been introduced in general surgery to evaluate similar features in other pathological settings such as thyroid and prostate cancer. The aim of the present study was to evaluate ioUS elastosonography in the intraoperative prediction of key intracranial meningioma features and to evaluate its application in guiding surgical strategy.

METHODS

An institutional series of 36 meningiomas studied with ioUS elastosonography is reported. Elastographic data, intraoperative surgical findings, and corresponding preoperative MRI features were classified, applying a score from 0 to 2 to both meningioma consistency and meningioma-brain interface. Statistical analysis was performed to determine the degree of agreement between meningioma elastosonographic features and surgical findings, and whether intraoperative elastosonography was a better predictor than preoperative MRI in assessing meningioma consistency and slip-brain interface, using intraoperative findings as the gold standard.

RESULTS

A significantly high degree of reliability and agreement between ioUS elastographic scores and surgical finding scores was reported (intraclass correlation coefficient = 0.848, F = 12.147, p < 0.001). When analyzing both consistency and brain-tumor interface, ioUS elastography proved to have a rather elevated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive (LR+) and negative likelihood ratio (LR-). This consideration was true especially for meningiomas with a hard consistency (sensitivity = 0.92, specificity = 0.96, PPV = 0.92, NPV = 0.96, LR+ = 22.00, LR- = 0.09) and for those presenting with an adherent slip-brain interface (sensitivity = 0.76, specificity = 0.95, PPV = 0.93, NPV = 0.82, LR+ = 14.3, LR- = 0.25). Furthermore, predictions derived from ioUS elastography were found to be more accurate than MRI-derived predictions, as demonstrated by McNemar's test results in both consistency (p < 0.001) and interface (p < 0.001).

CONCLUSIONS

While external validation of the data is needed to transform ioUS elastography into a fully deployable clinical tool, this experience confirmed that it may be integrated into meningioma surgical planning, especially because of its rapidity and cost-effectiveness.

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).

Imaging and diagnostic advances for intracranial meningiomas

The archetypal imaging characteristics of meningiomas are among the most stereotypic of all central nervous system (CNS) tumors. In the era of plain film and ventriculography, imaging was only performed if a mass was suspected, and their results were more suggestive than definitive. Following more than a century of technological development, we can now rely on imaging to non-invasively diagnose meningioma with great confidence and precisely delineate the locations of these tumors relative to their surrounding structures to inform treatment planning. Asymptomatic meningiomas may be identified and their growth monitored over time; moreover, imaging routinely serves as an essential tool to survey tumor burden at various stages during the course of treatment, thereby providing guidance on their effectiveness or the need for further intervention. Modern radiological techniques are expanding the power of imaging from tumor detection and monitoring to include extraction of biologic information from advanced analysis of radiological parameters. These contemporary approaches have led to promising attempts to predict tumor grade and, in turn, contribute prognostic data. In this supplement article, we review important current and future aspects of imaging in the diagnosis and management of meningioma, including conventional and advanced imaging techniques using CT, MRI, and nuclear medicine.

The predictive value of preoperative apparent diffusion coefficient (ADC) for facial nerve outcomes after vestibular schwannoma resection: clinical study

OBJECT

Diffusion MRI has been used to predict intraoperative consistency of tumors. Apparent diffusion coefficient (ADC) has shown predictive value as an imaging biomarker in many CNS tumors but has not been studied in a large cohort of patients with vestibular schwannoma. In this study, we examine the utility of ADC as a predictive biomarker for intraoperative tumor characteristics and postoperative facial nerve outcome.

METHODS

A retrospective review of patients who underwent vestibular schwannoma resection at our institution from 2008 to 2018 yielded 87 patients, of which 72 met inclusion criteria. Operative reports and clinical records were reviewed for clinical data; MRI data were interpreted in a blinded fashion for qualitative and quantitative biomarkers, including tumor ADC.

RESULTS

Mean tumor ADC values did not predict intraoperative consistency or adherence (p = 0.63). Adherent tumors were associated with worse facial nerve outcomes (p = 0.003). Regression tree analysis identified 3 ADC categories with statistically different facial nerve outcomes. The categories identified were ADC < 1006.04 × 10^-6 mm^2/s; ADC 1006.04-1563.93 × 10^-6 mm^2/s and ADC ≥ 1563.94 × 10^-6 mm^2/s. Postoperative and final House-Brackmann (HB) scores were significantly higher in the intermediate ADC group (2.3, p = 0.0038). HB outcomes were similar between the group with ADC < 1006.04 × 10^-6 mm^2/s and ≥ 1563.94 × 10^-6 mm^2/s (1.3 vs 1.3).

CONCLUSIONS

Middle-range preoperative ADC in vestibular schwannoma suggests a less favorable postoperative HB score. Preoperative measurement of ADC in vestibular schwannoma may provide additional information regarding prognostication of facial nerve outcomes.

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.

REVIEW: MR elastography of brain tumors

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

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

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Reported shear modulus values are widely distributed and overlap.

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

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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.

Clinical potential of meningioma genomic insights: a practical review for neurosurgeons

Meningiomas are among the most common intracranial pathological conditions, accounting for 36% of intracranial lesions treated by neurosurgeons. Although the majority of these lesions are benign, the classical categorization of tumors by histological type or World Health Organization (WHO) grade has not fully captured the potential for meningioma progression and recurrence. Many targeted treatments have failed to generate a long-lasting effect on these tumors. Recently, several seminal studies evaluating the genomics of intracranial meningiomas have rapidly changed the understanding of the disease. The importance of NF2 (neurofibromin 2), TRAF7 (tumor necrosis factor [TNF] receptor-associated factor 7), KLF4 (Kruppel-like factor 4), AKT1, SMO (smoothened), PIK3CA (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha), and POLR2 (RNA polymerase II subunit A) demonstrates that there are at least 6 distinct mutational classes of meningiomas. In addition, 6 methylation classes of meningioma have been appreciated, enabling improved prediction of prognosis compared with traditional WHO grades. Genomic studies have shed light on the nature of recurrent meningioma, distinct intracranial locations and mutational patterns, and a potential embryonic cancer stem cell-like origin. However, despite these exciting findings, the clinical relevance of these findings remains elusive. The authors review the key findings from recent genomic studies in meningiomas, specifically focusing on how these findings relate to clinical insights for the practicing neurosurgeon.

High Expression of TGF-β1 Predicting Tumor Progression in Skull Base Chordomas

OBJECTIVE

To investigate the expression characteristics and prognostic value of transforming growth factor β1 (TGF-β1) in primary skull base chordomas (SBCs).

METHODS

The mRNA expression levels of TGF-β1 were measured in 57 frozen samples from patients with primary SBCs. Clinical data collection, follow-up, correlations, and survival analyses were performed.

RESULTS

In the series of 57 patients (29 men and 28 women) with primary SBCs, the mean value of TGF-β1 mRNA was 1.713 with a median of 0.904. Twenty-four SBCs were soft type and 33 were hard type. The Mann-Whitney U test revealed that the expression level of TGF-β1 mRNA in hard type SBCs was significantly higher than the expression level found in the soft type (P = 0.03). The independent-samples median test suggested that the expression level of TGF-β1 mRNA in female patients' SBCs was significantly higher than that in male patients' SBCs (P = 0.01). Expression differences of TGF-β1 were not seen among different pathological subtypes, tumor blood supply, or degree of resection. The Spearman rank correlation coefficient clarified that TGF-β1 mRNA levels were not correlated with tumor diameter, preoperative Karnofsky Performance Status (KPS), postoperative KPS, follow-up KPS, age, or intraoperative blood loss. The multivariate Cox analysis revealed that pathological subtype (P = 0.008), expression level of TGF-β1 mRNA (P = 0.01), and tumor texture (P = 0.03) were all independent prognostic factors for tumor progression.

CONCLUSIONS

SBCs in female patients and SBCs with hard texture were prone to have high TGF-β1 mRNA expression. High expression of TGF-β1, hard tumor texture, and conventional subtype were all independent risk factors for tumor progression.

Approach Selection and Surgical Planning in Posterior Cranial Fossa Meningiomas: How I Do It

Posterior cranial fossa meningiomas represent approximately 9% of all the intracranial meningiomas. Despite the recent reports of radiation therapy in the management of these tumors, surgical resection continues to be the first line of treatment method aiming the permanent meningioma eradication. The evolution of imaging studies improved the preoperative evaluation of meningiomas providing greater anatomical detail of small structures not previously visualized. Nonetheless, the preoperative radiological evaluation should go beyond the differential diagnosis of a posterior fossa tumor. Anatomo-radiological assessment of meningiomas is discussed in detail. Based on our clinical experience, literature review, and case illustration, we highlight important preoperative anatomo-radiological aspects of posterior fossa meningiomas and their implications in the surgical management of these tumors.

Differentiation Researches on the Meningioma Subtypes by Radiomics from Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study

BACKGROUND

Meningioma subtypes are one of the most common key points to the treatment and prognosis of patients. The purpose of this study was to investigate the differential diagnostic value of radiomics features on meningioma.

METHODS

A total of 241 patients with meningioma who had undergone tumor resection were randomly selected including 80 with meningothelial meningioma, 80 with fibrous meningioma, and 81 with transitional meningioma. These meningiomas were divided into 4 groups including: meningothelial versus fibrous (group 1), fibrous versus transitional (group 2), meningothelial versus transitional (group 3), and meningothelial versus fibrous versus transitional (group 4). All patients were examined using the same magnetic resonance scanner (GE 3.0 T) and the preoperative contrast-enhanced T1-weighted images were available. Radiomics features from the contrast-enhanced T1-weighted images of 241 patients were evaluated by 2 experienced radiology specialists.

RESULTS

A total of 385 radiomics features were extracted from the images of each patient. Several preprocessing methods were applied on the radiomics dataset to reduce the redundancy and highlight differences between different meningioma before the Fisher discrimination analysis was adopted and leave one out cross validation methods were used for the model validation. The differentiation accuracies of the Fisher discriminant analysis model for groups 1, 2, 3, and 4 were 99.4%, 98.8%, 100% and 100%, respectively; leave one out cross validation method was achieved for group 1, 2, 3, and 4 with the accuracies of 91.3%, 95.0%, 100%, and 94.2%, respectively.

CONCLUSIONS

Radiomics features and the combined Fisher discriminant analysis could provide satisfactory performance in the preoperative differential diagnosis of meningioma subtypes and enable the potential ability for clinical application.

Prospective clinical validation of a meningioma consistency grading scheme: association with surgical outcomes and extent of tumor resection

OBJECTIVE

The present study aims to assess the clinical utility of a previously validated intraoperative meningioma consistency grading scale and its association with extent of resection (EOR) and various surgical outcomes.

METHODS

The previously validated grading system was prospectively assessed in 127 consecutive patients undergoing open craniotomy for meningioma by multiple neurosurgeons at two high-volume academic hospitals from 2013 to 2016. Consistency grading scores ranging from 1 (soft) to 5 (firm/calcified) were retrospectively analyzed to test for association with surgical outcomes and EOR, categorized as gross-total resection (GTR) or subtotal resection, defined by postoperative MRI.

RESULTS

One hundred twenty-seven patients were included in the analysis with a tumor consistency distribution as follows: grade 1, 3.1%; grade 2, 14.2%; grade 3, 44.1%; grade 4, 32.3%; and grade 5, 6.3%. The mean tumor diameter was 3.6 ± 1.7 cm. Tumor consistency grades were grouped into soft (grades 1 and 2), average (grade 3), and firm (grades 4 and 5) groups for statistical analysis with distributions of 17.3%, 44.1%, and 38.6%, respectively. There was no association between meningioma consistency and maximal tumor diameter, or location. Mean duration of surgery was longer for tumors with higher consistency: grades 1 and 2, 186 minutes; grade 3, 219 minutes; and grades 4 and 5, 299 minutes (p = 0.000028). There was a trend toward higher perioperative complication rates for tumors of increased consistency: grades 1 and 2, 4.5%; grade 3, 7.0%; and grades 4 and 5, 20.8% (p = 0.047). The proportion of GTR for each consistency group was as follows: grades 1 and 2, 77%; grade 3, 68%; and grades 4 and 5, 43% (p = 0.0062).

CONCLUSIONS

In addition to other important meningioma characteristics such as invasiveness, tumor consistency is a key determinant of surgical outcomes, including operative duration and EOR. Future studies predicting tumor consistency based on preoperative neuroimaging will help considerably with preoperative planning for meningiomas.

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

Tumor consistency is a critical factor that influences operative strategy and patient counseling. Magnetic resonance imaging (MRI) describes the concentration of water within living tissues and as such, is hypothesized to predict aspects of their biomechanical behavior. In meningiomas, MRI signal intensity has been used to predict the consistency of the tumor and its histopathological subtype, though its predictive capacity is debated in the literature. We performed a systematic review of the PubMed database since 1990 concerning MRI appearance and tumor consistency to assess whether or not MRI can be used reliably to predict tumor firmness. The inclusion criteria were case series and clinical studies that described attempts to correlate preoperative MRI findings with tumor consistency. The relationship between the pre-operative imaging characteristics, intraoperative findings, and World Health Organization (WHO) histopathological subtype is described. While T2 signal intensity and MR elastography provide a useful predictive measure of tumor consistency, other techniques have not been validated. T1-weighted imaging was not found to offer any diagnostic or predictive value. A quantitative assessment of T2 signal intensity more reliably predicts consistency than inherently variable qualitative analyses. Preoperative knowledge of tumor firmness affords the neurosurgeon substantial benefit when planning surgical techniques. Based upon our review of the literature, we currently recommend the use of T2-weighted MRI for predicting consistency, which has been shown to correlate well with analysis of tumor histological subtype. Development of standard measures of tumor consistency, standard MRI quantification metrics, and further exploration of MRI technique may improve the predictive ability of neuroimaging for meningiomas.

Quality of Life After Craniovertebral Junction Meningioma Resection: Shaping the Real Neurologic and Functional Expectancies About These Surgeries in a Contemporary Large Multicenter Experience

OBJECTIVE

Craniovertebral junction (CVJ) meningiomas are one of the most surgically complex conditions in neuro-oncologic surgery. The aim of this work is to correlate our data with clinical outcome to outline factors leading to a worse functional prognosis.

METHODS

We analyzed sex, age, clinical presentation, topography, surgical approach, Simpson grade resection, postoperative lower cranial nerve deficits, consistency, histology, site of origin, presence of a capsule, and radiologic and clinical follow-up at 1, 6, and 12 months of 61 patients affected by CVJ meningiomas, operated on in our institution from 1992 to 2014.

RESULTS

78.7% of patients were women (mean age, 52.85 years); the onset symptom was pain in 65.5% of cases. The mean preoperative Nurick grade of the sample was 3.78; the most frequent histologic type was endotheliomatous (42.8%). We treated 22 patients with a posterior median approach (5 with lateral and 17 with posterolateral axial topography); in 39 cases (30 anterolateral and 9 anterior) we performed a posterolateral approach. Gross total removal was achieved in 85.2% of cases. We recorded a final follow-up step overall neurologic improvement in the cohort (average preoperative Nurick grade, 3.81, and at 12 months, 2.13). Twenty-nine patients presented with lower cranial nerve deficit (permanent or transient) and no statistically significant association was found between surgical approach and temporary or permanent postoperative complications.

CONCLUSIONS

We selected, in our experience, some predictors of worse outcome: preoperative sphincter impairment, absence of a capsule, cranial site of origin, a poor preoperative functional status, and firm consistency of the tumor.

Transtemporal Investigation of Brain Parenchyma Elasticity Using 2-D Shear Wave Elastography: Definition of Age-Matched Normal Values

The goal of our research was to assess the possibility of reliable investigation of brain tissue stiffness using ultrasonographic brain parenchyma elastography with an intact temporal bone. We enrolled 108 patients after exclusion of intracranial pathology or healthy volunteers. All patients were subdivided by age into groups: 20-40, 40-60 and >60 y. For statistical analysis, the χ² test and t-test were used. The mean values, regardless of age and other parameters, were 3.34 kPa (SD = 0.59) on the left side and 3.33 kPa (SD = 0.58) on the right side. We found no correlation between the values, body mass index (r = 0.07, p = 0.48) and sex (t = -0.11, p = 0.91), but we observed a highly significant correlation between the values and age (r = 0.43, p <0.0001). We found ultrasonographic brain parenchyma elastography to be a valid, reproducible and investigator-independent method that reliably determines brain parenchyma stiffness. Normal values should serve as a reference for studies on various intracranial lesions.