Use of preoperative magnetic resonance imaging T1 and T2 sequences to determine intraoperative meningioma consistency

Differentiation of the Intradural Extramedullary Spinal Tumors, Schwannomas, and Meningiomas Utilizing the Contrast Ratio as a Quantitative Magnetic Resonance Imaging Method

BACKGROUND

Schwannomas and meningiomas are the most common intradural extramedullary spinal tumors; however, differentiating between them using magnetic resonance imaging (MRI) is a frequent challenge. In this study, we aimed to investigate the use of the contrast ratio (CR) as a quantitative MRI method in the differentiation of schwannomas and meningiomas.

METHODS

We analyzed the data of patients with intradural extramedullary spinal tumors who underwent surgery and were diagnosed with either schwannomas or meningiomas by histopathological analysis. Regions of interest were set for the entire spinal tumor on T2-weighted sagittal MRI. To obtain the CR values of spinal tumors (CRtumor), we used the signal intensity (SI) values of the tumor (SItumor) and spinal cord (SIcord) according to the following formula: [CRtumor = (SItumor-SIcord)/(SItumor+SIcord)].

RESULTS

The study included 50 patients (23 males and 27 females) with a mean age of 61.5 years old (11-85 years old). Histopathological analysis revealed that 33 and 17 patients were diagnosed with schwannomas and meningiomas, respectively. The mean CR values of the schwannomas and meningiomas were 0.3040 ± 0.1386 and 0.0173 ± 0.1929, respectively. The CR value of the schwannomas was statistically significantly higher than that of meningiomas (P < 0.01). The cutoff CR value obtained from the receiver operating characteristic curve was 0.143, with a specificity and sensitivity of 90.9% and 88.2%, respectively. Furthermore, the value for the area under the receiver operating characteristic curve was 0.925 (95% confidence interval: 0.852-0.998).

CONCLUSIONS

The evaluation of CRs by using MRI to distinguish between schwannomas and meningiomas is a beneficial quantitative tool.

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.

Preoperative Magnetic Resonance Elastography (MRE) of Skull Base Tumours: A Review

Conventional magnetic resonance imaging (MRI) can detect tumors consistency, but it can't predict tumor stiffness or adherence of the tumor to nearby structures. Magnetic resonance elastography (MRE) is a known non-invasive MRI based imaging technique used to assess the viscoelasticity of the tissues particularly liver fibrosis. This study discussed the importance of preoperative MRE in skull base tumors and the future implications of this new imaging modality. We did review of the English literature (by searching PubMed) regarding the use of MRE in preoperative assessment of skull base tumours stiffness and adherence to surrounding tissues. Recent research demonstrated that MRE can detect the stiffness and adherence of skull base tumors to surrounding structures by recording the spread of mechanical waves in the different tissues. In addition to non-radiation exposure, this technique is fast and can be incorporated into the conventional (MRI) study. MRE can palpate skull base tumours by imaging, allowing the stiffness of the tumour to be assessed. Preoperative assessment of brain tumours consistency, stiffness, and adherence to surrounding tissues is critical to avoid injury of important nearby structures and better preoperative patient counselling regarding surgical approach (endoscopic or open), operative time, and suspected surgical complications. However, the accuracy of MRE is less in small and highly vascular tumors. Also, MRE can't accurately detect tumour-brain adherence, but the new modality (slip-interface imaging) can. Hence, adding MRE to the conventional MRI study may help in preoperative diagnosis and treatment of skull base tumours.

Uncertainty-guided transformer for brain tumor segmentation

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality. Given these concerns, we exploit an uncertainty-guided U-shaped transformer with multiple heads to construct drop-out format masks for robust training. Specifically, our drop-out masks are composed of boundary mask, prior probability mask, and conditional probability mask, which can help our approach focus more on uncertainty regions. Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods, achieving average dice coefficients of [Formula: see text] and Hausdorff distance of 4.91 on the BraTS2021 dataset. Our code is freely available at https://github.com/chaineypung/BTS-UGT.

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.

DE-UFormer: U-shaped dual encoder architectures for brain tumor segmentation

Objective. In brain tumor segmentation tasks, the convolutional neural network (CNN) or transformer is usually acted as the encoder since the encoder is necessary to be used. On one hand, the convolution operation of CNN has advantages of extracting local information although its performance of obtaining global expressions is bad. On the other hand, the attention mechanism of the transformer is good at establishing remote dependencies while it is lacking in the ability to extract high-precision local information. Either high precision local information or global contextual information is crucial in brain tumor segmentation tasks. The aim of this paper is to propose a brain tumor segmentation model that can simultaneously extract and fuse high-precision local and global contextual information.Approach. We propose a network model DE-Uformer with dual encoders to obtain local features and global representations using both CNN encoder and Transformer encoder. On the basis of this, we further propose the nested encoder-aware feature fusion (NEaFF) module for effective deep fusion of the information under each dimension. It may establishe remote dependencies of features under a single encoder via the spatial attention Transformer. Meanwhile ,it also investigates how features extracted from two encoders are related with the cross-encoder attention transformer.Main results. The proposed algorithm segmentation have been performed on BraTS2020 dataset and private meningioma dataset. Results show that it is significantly better than current state-of-the-art brain tumor segmentation methods.Significance. The method proposed in this paper greatly improves the accuracy of brain tumor segmentation. This advancement helps healthcare professionals perform a more comprehensive analysis and assessment of brain tumors, thereby improving diagnostic accuracy and reliability. This fully automated brain model segmentation model with high accuracy is of great significance for critical decisions made by physicians in selecting treatment strategies and preoperative planning.

Comparison of Deformation Patterns Excited in the Human Brain In Vivo by Harmonic and Impulsive Skull Motion

Noninvasive measurements of brain deformation in human participants in vivo are needed to develop models of brain biomechanics and understand traumatic brain injury (TBI). Tagged magnetic resonance imaging (tagged MRI) and magnetic resonance elastography (MRE) are two techniques to study human brain deformation; these techniques differ in the type of motion and difficulty of implementation. In this study, oscillatory strain fields in the human brain caused by impulsive head acceleration and measured by tagged MRI were compared quantitatively to strain fields measured by MRE during harmonic head motion at 10 and 50 Hz. Strain fields were compared by registering to a common anatomical template, then computing correlations between the registered strain fields. Correlations were computed between tagged MRI strain fields in six participants and MRE strain fields at 10 Hz and 50 Hz in six different participants. Correlations among strain fields within the same experiment type were compared statistically to correlations from different experiment types. Strain fields from harmonic head motion at 10 Hz imaged by MRE were qualitatively and quantitatively similar to modes excited by impulsive head motion, imaged by tagged MRI. Notably, correlations between strain fields from 10 Hz MRE and tagged MRI did not differ significantly from correlations between strain fields from tagged MRI. These results suggest that low-frequency modes of oscillation dominate the response of the brain during impact. Thus, low-frequency MRE, which is simpler and more widely available than tagged MRI, can be used to illuminate the brain's response to head impact.

Supratentorial Meningioma Consistency Prediction Utilizing Tumor to Cerebellar Peduncle Intensity on T1 and T2-Weighted and Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging Sequences

OBJECTIVE

Predicting meningioma consistency with preoperative imaging is critical for surgery planning. Preoperative T1 and T2-weighted and fluid attenuated inversion recovery magnetic resonance imaging (MRI) findings of supratentorial meningioma tumors were studied and compared with intraoperative supratentorial meningioma tumor consistency based on the Cavitron ultrasound surgical aspirator (CUSA) and ZADA grading scales in this cohort to predict the tumor consistency before surgery.

METHODS

MRI from 78 consecutive patients who underwent supratentorial meningioma tumor resection between 2018 and 2021 were evaluated preoperatively. An intraoperative tumor consistency grade was applied to these lesions prospectively by the operating surgeon based on CUSA and ZADA grading scales. Tumor/cerebellar peduncle T2-weighted intensity, tumor/cerebellar peduncle T1-weighted intensity (TCT1I), and tumor/cerebellar peduncle fluid attenuated inversion recovery intensity (TCFI) ratios were calculated. Tumor consistency grades and MRI intensity ratios were correlated using one-way ANOVA.

RESULTS

Of the 78 patients, 52 (66.7%) were female and 26 (33.3%) were male. Tumor volume correlated with tumor consistency grades on both CUSA (P = 0.005) and ZADA (P = 0.024) grading scales. Also patients age correlated with tumor consistency according to ZADA grading scale (P = 0.024). TCT1I (P = 0.009) and TCFI (P < 0.005) ratios correlated significantly with tumor consistency grade according to CUSA. Similarly, TCT1I (P = 0.0032) and TCFI (P = 0.001) ratios was significantly associated with tumor consistency according to ZADA grading scales.

CONCLUSIONS

Our findings suggest that higher tumor/cerebellar peduncle T2-weighted intensity and TCFI ratios correlate with softer tumors, while higher TCT1I ratios reveal firmer tumors. These data can assist the surgeon predict the supratentorial meningioma consistency before surgery.

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.

Spinal meningiomas, from biology to management - A literature review

Meningiomas arise from arachnoidal cap cells of the meninges, constituting the most common type of central nervous system tumors, and are considered benign tumors in most cases. Their incidence increases with age, and they mainly affect females, constituting 25-46% of primary spinal tumors. Spinal meningiomas could be detected incidentally or be unraveled by various neurological symptoms (e.g., back pain, sphincter dysfunction, sensorimotor deficits). The gold standard diagnostic modality for spinal meningiomas is Magnetic resonance imaging (MRI) which permits their classification into four categories based on their radiological appearance. According to the World Health Organization (WHO) classification, the majority of spinal meningiomas are grade 1. Nevertheless, they can be of higher grade (grades 2 and 3) with atypical or malignant histology and a more aggressive course. To date, surgery is the best treatment where the big majority of meningiomas can be cured. Advances in surgical techniques (ultrasonic dissection, microsurgery, intraoperative monitoring) increase the complete resection rate. Operated patients have a satisfactory prognosis, even in those with poor preoperative neurological status. Adjuvant therapy has a growing role in treating spinal meningiomas, mainly in the case of subtotal resection and tumor recurrence. The current paper reviews the fundamental epidemiological and clinical aspects of spinal meningiomas, their histological and genetic characteristics, and their management, including the various surgical novelties and techniques.

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.

Preoperative Assessment and Prediction of Consistency of Intracranial Meningioma Utilizing the Apparent Diffusion Coefficient Values

Objectives Consistency of meningioma is important for preoperative planning, surgical resection, and predicting surgical outcomes. We prospectively evaluated the utility of the apparent diffusion coefficient (ADC) values to assess the consistency of meningioma.

Methods Preoperative magnetic resonance imaging (MRI) was performed on 23 patients with meningioma before undergoing surgical resection and the average/mean of ADC minimum (ADCmin), maximum (ADCmax), and mean (ADCmean) values were calculated. Intraoperatively, the meningiomas were characterized as firm or soft and correlated with ADC values.

Results ADCmin, ADCmax, and ADCmean values of soft and firm meningiomas were significantly different with a p-value of < 0.05. ADCmin value of < 691.3 × 10−6 mm2/s had 80% sensitivity and 84.6% specificity for identifying firm from the soft tumors with the area under the curve (AUC) = 0.862, p-value of 0.004, positive predictive value (PPV) 80, and negative predictive value (NPV) 84.6. ADCmax value of < 933.6 × 10−6 mm2/s had 70% sensitivity and 84.6% specificity for identifying firm from the soft tumors with AUC = 0.823, p-value of 0.009, PPV 77.8, and NPV 78.6. ADCmean value of < 840.8 × 10−6 mm2/s had 90% sensitivity and 76.9% specificity for identifying firm from the soft tumors with AUC = 0.900, p-value of 0.001, PPV 75, and NPV 90.9.

Conclusion Diffusion-weighted MRI using ADC minimum, ADC maximum, and ADC mean values can be used to differentiate firm from soft meningiomas. Meningiomas with hard consistency showed relatively low ADC values.

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer

Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder–decoder structure and skip-connection have been frequently used. However, CNNs have the drawback of being unable to learn global and remote semantic information well. On the other hand, the transformer has recently found success in natural language processing and computer vision as a result of its usage of a self-attention mechanism for global information modeling. For demanding prediction tasks, such as 3D medical picture segmentation, local and global characteristics are critical. We propose SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, convolutional neural network, and encoder–decoder structure to define the 3D brain tumor semantic segmentation job as a sequence-to-sequence prediction challenge in this research. To extract contextual data, the 3D Swin Transformer is utilized as the network’s encoder and decoder, and convolutional operations are employed for upsampling and downsampling. Finally, we achieve segmentation results using an improved Transformer module that we built for increasing detail feature extraction. Extensive experimental results on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets reveal that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images.

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.

Predicting pituitary adenoma consistency with preoperative magnetic resonance elastography

OBJECTIVE

Pituitary adenoma is one of the most common primary intracranial neoplasms. Most of these tumors are soft, but up to 17% may have a firmer consistency. Therefore, knowing the tumor consistency in the preoperative setting could be helpful. Multiple imaging methods have been proposed to predict tumor consistency, but the results are controversial. This study aimed to evaluate the efficacy of MR elastography (MRE) in predicting tumor consistency and its potential use in a series of patients with pituitary adenomas.

METHODS

Thirty-eight patients with pituitary adenomas (≥ 2.5 cm) were prospectively evaluated with MRI and MRE before surgery. Absolute MRE stiffness values and relative MRE stiffness ratios, as well as the relative ratio of T1 signal, T2 signal, and diffusion-weighted imaging apparent diffusion coefficient (ADC) values were determined prospectively by calculating the ratio of those values in the tumor to adjacent left temporal white matter. Tumors were classified into three groups according to surgical consistency (soft, intermediate, and firm). Statistical analysis was used to identify the predictive value of the different radiological parameters in determining pituitary adenoma consistency.

RESULTS

The authors included 32 (84.21%) nonfunctional and 6 (15.79%) functional adenomas. The mean maximum tumor diameter was 3.7 cm, and the mean preoperative tumor volume was 16.4 cm3. Cavernous sinus invasion was present in 20 patients (52.63%). A gross-total resection was possible in 9 (23.68%) patients. The entire cohort's mean absolute tumor stiffness value was 1.8 kPa (range 1.1-3.7 kPa), whereas the mean tumor stiffness ratio was 0.66 (range 0.37-1.6). Intraoperative tumor consistency was significantly correlated with absolute and relative tumor stiffness (p = 0.0087 and 0.007, respectively). Tumor consistency alone was not a significant factor for predicting gross-total resection. Patients with intermediate and firm tumors had more complications compared to patients with soft tumors (50.00% vs 12.50%, p = 0.02) and also had longer operative times (p = 0.0002).

CONCLUSIONS

Whereas other MRI sequences have proven to be unreliable in determining tumor consistency, MRE has been shown to be a reliable tool for predicting adenoma consistency. Preoperative knowledge of tumor consistency could be potentially useful for surgical planning, counseling about potential surgical risks, and estimating the length of operative time.

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.

Magnetic Resonance Elastography of the Brain

Magnetic resonance elastography (MRE) is an emerging noninvasive technique, an alternative to palpation for quantitative assessment of biomechanical properties of tissue. In MRE, tissue stiffness information is obtained by a 3-step process, propagating mechanical waves in the tissues, measuring the wave propagation using modified magnetic resonance (MR) pulse sequences, and generating the quantitative stiffness maps from the MR images. MRE is clinically used in patients with liver diseases, whereas its applications in other organs are still being investigated. At present, the pediatric studies are in the initial stage and preliminary results promise to provide additional information about tissue characteristics.

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.

Endoscopic Transnasal Resection of Midline Skull Base Meningiomas: Tumor Consistency and Surgical Outcomes

Background  The endoscopic transnasal approach (ETA) has proven to be of great value in the resection of midline skull base meningiomas when compared with traditional approaches. Our objective was to assess tumor consistency in relation to surgical outcomes for midline meningiomas (MMs) resected using ETA. Methods  Radiological preoperative features, including the tumor to cerebellar peduncle T2-weighted magnetic resonance imaging (MRI) ratio (TCTI), were evaluated. The intraoperative consistency assessment was performed by the surgeon, which determined if the tumor was soft (resectable by suction) or firm (required a cavitation ultrasonic aspirator). Surgical resection and postoperative complications were evaluated in relation to tumor consistency. Results  Twenty patients were evaluated; 6 were classified as firm and 14 were classified as soft. The mean TCTI ratio was 1.7 and the median was 1.7 (range: 1.3-2.4). Three firm tumors had a ratio of <1.6. All soft tumors had a ratio of ≥1.6 with three outliers. Additionally, 66.7% of patients with firm tumors had complications compared with 35.7% of patients with soft tumors. Only 33.3% of firm tumors underwent gross total resection (GTR) in comparison to 79.0% of tumors with a soft consistency. Conclusion  In our analysis, we found that tumor consistency was significantly related to short-term surgical outcomes in MMs resected using the ETA. The TCTI ratio was found to be the most reliable predictor with a sensitivity of 76.9% and a specificity of 40.0%. Our findings suggest that traditional cranial approaches should be considered as the first surgical option for managing firm MMs.

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.

Harnessing brain waves: a review of brain magnetic resonance elastography for clinicians and scientists entering the field

Brain magnetic resonance elastography (MRE) is an imaging technique capable of accurately and non-invasively measuring the mechanical properties of the living human brain. Recent studies have shown that MRE has potential to provide clinically useful information in patients with intracranial tumors, demyelinating disease, neurodegenerative disease, elevated intracranial pressure, and altered functional states. The objectives of this review are: (1) to give a general overview of the types of measurements that have been obtained with brain MRE in patient populations, (2) to survey the tools currently being used to make these measurements possible, and (3) to highlight brain MRE-based quantitative biomarkers that have the highest potential of being adopted into clinical use within the next 5 to 10 years. The specifics of MRE methodology strategies are described, from wave generation to material parameter estimations. The potential clinical role of MRE for characterizing and planning surgical resection of intracranial tumors and assessing diffuse changes in brain stiffness resulting from diffuse neurological diseases and altered intracranial pressure are described. In addition, the emerging technique of functional MRE, the role of artificial intelligence in MRE, and promising applications of MRE in general neuroscience research are presented.

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.

Investigating Effective Factors on Estimated Hemorrhage Intraoperative in Brain Meningioma Surgery

Introduction

The primary and definitive diagnosis of meningioma is based on histological assessment; however, employing imaging methods, like Magnetic Resonance Imaging (MRI) is very helpful to describe lesion's characteristics. Accordingly, we decided to study the effect of imaging factors, like MRI data on the volume of hemorrhage (estimated blood loss) during meningioma surgery.

Methods

This was a cross-sectional, retrospective, and analytical study. The eligible patients were those with meningioma who were candidates for surgery. A total of 40 patients with meningioma were selected and assessed. The preoperative imaging findings were recorded, then estimated blood loss during the surgery was determined.

Results

A reverse association was revealed between the degree of proximity to the nearest sinus and the rate of bleeding. Furthermore, the size of the mass was positively associated with the rate of bleeding; however, there was no significant correlation between the volume of bleeding and other parameters, including the degree of edema, the volume of mass, the site of the tumor in the brain, and the histological subtype of the tumor. The mean time of operation was strongly correlated with blood loss. The rate of bleeding was more expected in hypertensive versus normotensive patients.

Conclusion

Bleeding in various volumes could be a frequent finding in intracranial meningioma surgery. Overall, tumor size, the duration of surgery, a history of hypertension, and distance to the nearest sinuses were the main determinants for the severity of hemorrhage in patients undergoing meningioma surgery.

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.

Multi-parametric qualitative and quantitative MRI assessment as predictor of histological grading in previously treated meningiomas

PURPOSE

Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.

METHODS

Seventy-three patients with 74 histologically proven and previously treated meningiomas were retrospectively enrolled (42 WHO I, 24 WHO II, 8 WHO III) and studied with MRI including T2 TSE, FLAIR, Gradient Echo, DWI, and pre- and post-contrast T1 sequences. Lesion masks were segmented on post-contrast T1 sequences and rigidly registered to ADC maps to extract quantitative parameters from conventional DWI and intravoxel incoherent motion model assessing tumor perfusion. Two expert neuroradiologists assessed morphological features of meningiomas with semi-quantitative scores.

RESULTS

Univariate analysis showed different distributions (p < 0.05) of quantitative diffusion parameters (Wilcoxon rank-sum test) and morphological features (Pearson's chi-square; Fisher's exact test) among meningiomas grouped in low-grade (WHO I) and higher grade forms (WHO II/III); the only exception consisted of the tumor-brain interface. A multivariate logistic regression, combining all parameters showing statistical significance in the univariate analysis, allowed discrimination between the groups of meningiomas with high sensitivity (0.968) and specificity (0.925). Heterogeneous contrast enhancement and low ADC were the best independent predictors of atypia and anaplasia.

CONCLUSION

Our multi-parametric MRI assessment showed high sensitivity and specificity in predicting histological grading of meningiomas. Such an assessment may be clinically useful in characterizing lesions without histological diagnosis. Key points • When surgery and biopsy are not feasible, parameters obtained from both conventional and diffusion-weighted MRI can predict atypia and anaplasia in meningiomas with high sensitivity and specificity. • Low ADC values and heterogeneous contrast enhancement are the best predictors of higher grade meningioma.

Automatic detection of the meningioma tumor firmness in MRI images

Meningioma is among the most common primary tumors of the brain. The firmness of Meningioma is a critical factor that influences operative strategy and patient counseling. Conventional methods to predict the tumor firmness rely on the correlation between the consistency of Meningioma and their preoperative MRI findings such as the signal intensity ratio between the tumor and the normal grey matter of the brain. Machine learning techniques have not been investigated yet to address the Meningioma firmness detection problem. The main purpose of this research is to couple supervised learning algorithms with typical descriptors for developing a computer-aided detection (CAD) of the Meningioma tumor firmness in MRI images. Specifically, Local Binary Patterns (LBP), Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are extracted from real labeled MRI-T2 weighted images and fed into classifiers, namely support vector machine (SVM) and k-nearest neighbor (KNN) algorithm to learn association between the visual properties of the region of interest and the pre-defined firm and soft classes. The learned model is then used to classify unlabeled MRI-T2 weighted images. This paper represents a baseline comparison of different features used in CAD system that intends to accurately recognize the Meningioma tumor firmness. The proposed system was implemented and assessed using a clinical dataset. Using LBP feature yielded the best performance with 95% of F-score, 87% of balanced accuracy and 0.87 of the area under ROC curve (AUC) when coupled with KNN classifier, respectively.

Modern day imaging of meningiomas

Meningiomas are the most common primary tumors of the central nervous system and as such they are often encountered at neuroimaging. Fortunately, meningiomas are readily diagnosed with anatomic computed tomography and magnetic resonance imaging. While conventional imaging is the mainstay for initial diagnosis and delineating tumor for treatment planning and posttreatment follow-up, the last couple of decades have given rise to advanced physiologic and metabolic imaging techniques that serve as powerful tools in the management of meningioma. These modern approaches are allowing imaging to expand its utility to include extraction of biologic and potentially prognostic information that will ultimately improve care for meningioma patients.

Vascularity of meningiomas

Information on the vascular supply to meningiomas is critical to the neurosurgeon. Most meningiomas are supplied by the external carotid artery, though many get pial contribution as well. Angiogenesis is critical for these neoplasms to grow. Vascular endothelial growth factor (VEGF) has been a popular target of research to decrease angiogenesis. Peritumoral brain edema (PTBE) is occasionally seen in meningiomas, which makes surgical resection more challenging. The exact cause of PTBE remains unclear, but a number of factors have been postulated to contribute. Assessment of the vascularity of meningiomas is best carried out with angiography, but noninvasive techniques are improving, diminishing the need for more invasive imaging. Embolization of tumors can be performed to minimize perioperative blood loss and potentially lower surgical morbidity. However, it has not been shown to improve outcomes, and procedural risks exist. Higher grade tumors commonly have higher vascularity. Higher vascular meningiomas are more likely to recur and have higher levels of VEGF. The vascularity of meningiomas remains a topic of interest and is the focus of many research projects.

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.

Differentiating spinal intradural-extramedullary schwannoma from meningioma using MRI T2 weighted images

OBJECTIVE:

Prior studies advocate the subjective visual differences between meningioma and schwannoma on T₂ weighted images, however objective measurement of signal intensity differences may be useful in certain cases. The aim of this study was to investigate whether an objective evaluation of SIs on T₂ weighted images would be useful to differentiate spinal schwannomas from meningiomas.

METHODS:

The patients with spinal MRIs demonstrating path proven and subsequently treated intradural extramedullary spinal tumors were selected between April 2008 and May 2017. Regions of interest (ROIs) were measured in the tumor and subcutaneous fat on the same image, and we calculated the SI ratio between tumor and fat ROIs.

RESULTS:

Twenty patients each with meningioma and schwannoma were enrolled. The SI ratios of schwannomas were significantly higher than those of meningiomas (both researcher 1 and 2: p = 0.002). The areas under the curve by researchers 1 and 2 were 0.780. The cutoff value of SI ratio by both of researchers 1 and 2 to differentiate between schwannomas from meningiomas was 0.420 (sensitivity: 80.0%, specificity: 70.0-75.0%).

CONCLUSION:

The SI ratio, calculated from the SIs of the tumor and fat on T₂ weighted images, is useful for differentiating spinal schwannomas from meningiomas to obtain an accurate diagnosis.

ADVANCES IN KNOWLEDGE:

Signal intensity ratio of the spinal tumor and fat on T₂ weighted images is useful for differentiating schwannomas from meningiomas to obtain an accurate diagnosis.

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.

Meningioma Consistency: Correlation Between Magnetic Resonance Imaging Characteristics, Operative Findings, and Histopathological Features

INTRODUCTION

Intracranial meningiomas account for 30% of all primary intracranial tumors. Surgical resection remains the mainstay of the treatment for meningiomas. The magnetic resonance of intracranial meningiomas has been largely discussed in many reports of the radiological and neurosurgical literature. To date, a few studies have been attempted to differentiate the tumor characteristics of meningiomas based on magnetic resonance imaging (MRI) studies.

OBJECTIVE

The objective of the study is to evaluate the relationship between MRI signal characteristics of intracranial meningiomas and consistency of tumor using objective measures.

MATERIALS AND METHODS

A prospective study included all the patients who were admitted for surgery with an MRI finding suggestive of meningioma. All patients were subjected to routine radiological investigations. Surgical resection was performed for patients eligible for surgery using cavitron ultrasonic aspirator (CUSA). The relationship and correlation between the radiological, intraoperative measurements and the histopathological diagnosis were studied. The tumor consistency was measured using mean CUSA level. Intensity on T2, fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI) was measured using circular regions of interest (ROI) on the MRI. Multiple ROIs were placed initially on the lesions avoiding the obvious blood vessels, if any, then on the brain cortex to avoid the vasogenic edema. The mean ROI (mROI) results from the lesion were subtracted from the mean ROI from the brain cortex for each lesion to achieve normalized ratio. The results of lesion mROI-cortex mROI were compared to the operative and histopathology results using Pearson's correlation test and linear regression test.

RESULTS

The total number of patients was seventy. The mean age of the patients was 51 ± 14.8, with 72% of them being females and 28% males. There was a strong statistically significant (P = 0.046) and (P = 0.003) correlation between mean CUSA and FLAIR mROI difference or T2 mROI difference, respectively. On the other hand, there was an inversely proportional relationship between mean CUSA and FLAIR mROI difference and mean CUSA and T2 mROI difference. The value of the regression test (r) shows that there was a slight linear relationship between FLAIR mROI difference or T2 mROI difference and mean CUSA values, in which the mean CUSA value = 50.1 + (-0.088) × FLAIR mROI difference (r = -0.273, P = 0.046) or mean CUSA value = 50.8 + (-0.055) × T2 mROI difference (r = 0.4, P = 0.003). There was no statistical significance in the relation between CUSA values and tumor histological subtypes, DWI values, age, or gender.

CONCLUSION

This study presents a new objective method to measure the consistency of intracranial meningiomas based on a simple algorithmic formula. Such information will aid in planning surgery and assessing the resectability of the tumor. To date, this is the first objective measurement of meningioma consistency based on MRI studies and objective intraoperative evaluation.

MR elastography of the brain and its application in neurological diseases

Magnetic resonance elastography (MRE) is an imaging technique for noninvasively and quantitatively assessing tissue stiffness, akin to palpation. MRE is further able assess the mechanical properties of tissues that cannot be reached by hand including the brain. The technique is a three-step process beginning with the introduction of shear waves into the tissue of interest by applying an external vibration. Next, the resulting motion is imaged using a phase-contrast MR pulse sequence with motion encoding gradients that are synchronized to the vibration. Finally, the measured displacement images are mathematically inverted to compute a map of the estimated stiffness. In the brain, the technique has demonstrated strong test-retest repeatability with typical errors of 1% for measuring global stiffness, 2% for measuring stiffness in the lobes of the brain, and 3-7% for measuring stiffness in subcortical gray matter. In healthy volunteers, multiple studies have confirmed that stiffness decreases with age, while more recent studies have demonstrated a strong relationship between viscoelasticity and behavioral performance. Furthermore, several studies have demonstrated the sensitivity of brain stiffness to neurodegeneration, as stiffness has been shown to decrease in multiple sclerosis and in several forms of dementia. Moreover, the spatial pattern of stiffness changes varies among these different classes of dementia. Finally, MRE is a promising tool for the preoperative assessment of intracranial tumors, as it can measure both tumor consistency and adherence to surrounding tissues. These factors are important predictors of surgical difficulty. In brief, MRE demonstrates potential value in a number of neurological diseases. However, significant opportunity remains to further refine the technique and better understand the underlying physiology.

Meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T2-weighted magnetic resonance imaging sequences: TCTI ratio

OBJECTIVE

Meningioma consistency, firmness or softness as it relates to resectability, affects the difficulty of surgery and, to some degree, the extent of resection. Preoperative knowledge of tumor consistency would affect preoperative planning and instrumentation. Several methods of prediction have been proposed, but the majority lack objectivity and reproducibility or generalizability to other surgeons. In a previous pilot study of 20 patients the authors proposed a new method of prediction based on tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios in comparison with objective intraoperative findings. In the present study they sought validation of this method.

METHODS

Magnetic resonance images from 100 consecutive patients undergoing craniotomy for meningioma resection were evaluated preoperatively. During surgery a consistency grade was prospectively applied to lesions by the operating surgeon, as determined by suction and/or cavitron ultrasonic surgical aspirator (CUSA) intensity. Consistency grades were A, soft; B, intermediate; and C, fibrous. Using T2-weighted MRI sequences, TCTI ratios were calculated. Analysis of the TCTI ratios and intraoperative tumor consistency was completed with ANOVA and receiver operating characteristic curves.

RESULTS

Of the 100 tumors evaluated, 50 were classified as soft, 29 as intermediate, and 21 as firm. The median TCTI ratio for firm tumors was 0.88; for intermediate tumors, 1.5; and for soft tumors, 1.84. One-way ANOVA comparing TCTI ratios for these groups was statistically significant (p < 0.0001). A single cutoff TCTI value of 1.41 for soft versus firm tumors was found to be 81.9% sensitive and 84.8% specific.

CONCLUSIONS

The authors propose this T2-based method of tumor consistency prediction with correlation to objective intraoperative consistency. This method is quantifiable and reproducible, which expands its usability. Additionally, it places tumor consistency on a graded continuum in a clinically meaningful way that could affect preoperative surgical planning.

Petroclival meningiomas: Magnetic resonance imaging factors predict tumor resectability and clinical outcome

OBJECTIVE

Despite advances in skull base surgery, surgical removal of petroclival meningiomas (PCM) still poses a considerable neurosurgical challenge with regard to postoperative morbidity and the patients' long-term outcome. Knowledge of imaging features for PCM that might help to predict common risk factors encountered with tumor resection preoperatively is limited. The aim of this study was to clarify whether MRI features of PCM might predict tumor resectability and clinical outcome.

METHODS

A retrospective analysis of 18 cases of PCM treated surgically in our department between 2007 and 2013 was performed. Following radiological tumor features were compared to the extent of tumor resection and the patients' outcome: a) tumor diameter, b) calcification, c) tumor margin towards the brainstem, d) presence of an arachnoidal cleavage plane, e) brainstem edema, f) brainstem compression and g) tumor signal intensity on T2WI.

RESULTS

There was an excellent correlation between tumor resectability and preoperative findings with regard to the presence or absence of an arachnoidal cleavage plane and an irregular tumor margin towards the brainstem. Additionally, the presence of brainstem edema was significantly related to surgical morbidity, whereas a high tumor intensity on T2WI correlated significantly with soft tumor consistency and/or vascularity encountered during surgery.

CONCLUSION

As demonstrated in our series, PCM with an irregular tumor margin and absence of an arachnoidal plane towards the brainstem should be considered a high-risk group. In these cases, especially when additional brainstem edema is present, limited resection of tumor may be aspired to avoid postoperative morbidity.

Higher-Resolution Magnetic Resonance Elastography in Meningiomas to Determine Intratumoral Consistency

BACKGROUND

Magnetic resonance elastography (MRE) analyzes shear wave movement through tissue to determine stiffness. In a prior study, measurements with first-generation brain MRE techniques correlated with intraoperative observations of overall meningioma stiffness.

OBJECTIVE

To evaluate the diagnostic accuracy of a higher-resolution MRE technique to preoperatively detect intratumoral variations compared with surgeon assessment.

METHODS

Fifteen meningiomas in 14 patients underwent MRE. Tumors with regions of distinctly different stiffness were considered heterogeneous. Intratumoral portions were considered hard if there was a significant area ≥6 kPa. A 5-point scale graded intraoperative consistency. A durometer semiquantitatively measured surgical specimen hardness. Statistics included χ, sensitivity, specificity, positive and negative predicative values, and Spearman rank correlation coefficient.

RESULTS

For MRE and surgery, 9 (60%) and 7 (47%) tumors were homogeneous, 6 (40%) and 8 (53%) tumors were heterogeneous, 6 (40%) and 10 (67%) tumors had hard portions, and 14 (93%) and 12 (80%) tumors had soft portions, respectively. MRE sensitivity, specificity, and positive and negative predictive values were as follows: for heterogeneity, 75%, 100%, 100%, and 87%; for hardness, 60%, 100%, 100%, and 56%; and for softness, 100%, 33%, 86%, and 100%. Overall, 10 tumors (67%) matched well with MRE and intraoperative consistency and correlated between intraoperative observations (P = .02) and durometer readings (P = .03). Tumor size ≤3.5 cm or vascular tumors were more likely to be inconsistent (P < .05).

CONCLUSION

MRE was excellent at ruling in heterogeneity with hard portions but less effective in ruling out heterogeneity and hard portions, particularly in tumors more vascular or <3.5 cm. MRE is the first technology capable of prospectively evaluating intratumoral stiffness and, with further refinement, will likely prove useful in preoperative planning.

Prediction of hard meningiomas: quantitative evaluation based on the magnetic resonance signal intensity

BACKGROUND

From a surgical perspective, presurgical prediction of meningioma consistency is beneficial.

PURPOSE

To quantitatively analyze the correlation between the magnetic resonance (MR) signal intensity (SI) or apparent diffusion coefficient (ADC) and meningioma consistency and to determine which MR sequence could help predicting hard meningiomas.

MATERIAL AND METHODS

This study included 43 patients with meningiomas who underwent preoperative MR imaging (MRI), including T1-weighted (T1W) imaging, T2-weighted (T2W) imaging, fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), contrast-enhanced (CE)-T1W imaging, and CE-fast imaging employing steady-state acquisition (FIESTA). A neurosurgeon evaluated the tumor consistency using a visual analog scale (VAS) with the anchors "soft" (score = 0) and "hard" (score = 10). The SI ratio (tumor to cerebral cortex SI) and ADC value were compared with the tumor consistency. The sensitivity, specificity, and accuracy for predicting hard meningiomas (VAS score ≥8; 9 of 43 patients) were calculated using cutoff values for the SI ratio that were obtained in a receiver operating characteristic curve analysis.

RESULTS

A significant negative correlation was observed between the tumor consistency and the SI ratio on T2W imaging, FLAIR, and CE-FIESTA (P < 0.05) but not on T1W imaging, CE-T1W imaging, and the ADC value. The sensitivity, specificity, and accuracy for predicting hard meningiomas were 89%, 79%, and 81% with T2W imaging; 89%, 76%, and 79% with FLAIR; and 100%, 74%, and 79% with CE-FIESTA, respectively.

CONCLUSION

Our results suggest that a quantitative assessment using conventional T2W imaging or FLAIR may be a simple and useful method for predicting hard meningiomas.

New Software for Preoperative Diagnostics of Meningeal Tumor Histologic Types

OBJECTIVE

Meningeal tumors are neoplasms with different histologic manifestations of both benign and malignant types that determine the prognosis of tumor recurrence and its consistency. The risk of surgical treatment depends on the location, size, and consistency of the tumor. Magnetic resonance imaging (MRI) sequences can be used to identify the features of tumors, but these MRI characteristics are not well understood. The present study describes an advanced mathematical algorithm to analyze MRI data and distinguish histologic types of meningeal tumors before surgery.

METHODS

Forty-eight patients underwent surgical removal of meningeal brain tumor. All patients had preoperative MRI with a 1.5-T scanner. One radiologist and 2 neurosurgeons evaluated MRI histogram peaks of the whole tumor volume using the advanced computer algorithm.

RESULTS

Three specialists received the following mean value of histogram peaks: 15.99 ± 0.23 (± standard error of the mean [SEM]) for meningoteliomatous meningiomas; 21.24 ± 0.3 (±SEM) for fibroplastic meningiomas; 19.0 ± 0.28 (±SEM) for transitional meningiomas; 10.7 ± 0.27 (±SEM) for anatypical, anaplastic meningiomas, 11.03 ± 0.51 (±SEM) for primary intracranial fibrosarcomas and 25.72 ± 0.29 (±SEM) for meningeal hemangiopericytomas. A one-way analysis of variance test proved the difference between group means: F = 70.138, P < 0.01. The Tukey test and the Games-Howell test indicated that the difference between the tumor groups was significant. Mean deviation in agreement index between specialists was 0.98 ± 0.007 (±SEM).

CONCLUSIONS

The advanced algorithm proved high specificity, sensitivity, and interoperator repeatability.

Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

This article provides an overview of the neuroimaging literature focused on preoperative prediction of meningioma consistency. A validated, noninvasive neuroimaging method to predict tumor consistency can provide valuable information regarding neurosurgical planning and patient counseling. Most of the neuroimaging literature indicates conventional MRI using T2-weighted imaging may be helpful to predict meningioma consistency; however, further rigorous validation is necessary. Much less is known about advanced MRI techniques, such as diffusion MRI, MR elastography (MRE), and MR spectroscopy. Of these methods, MRE and diffusion tensor imaging appear particularly promising.