Meningiomas: correlation between MRI characteristics and operative findings including consistency

Utility of texture analysis for objective quantitative ex vivo assessment of meningioma consistency: method proposal and validation

BACKGROUND

Tumor consistency is considered to be a critical factor for the surgical removal of meningiomas and its preoperative assessment is intensively studied. A significant drawback in the research of predictive methods is the lack of a clear shared definition of tumor consistency, with most authors resorting to subjective binary classification labeling the samples as "soft" and "hard." This classification is highly observer-dependent and its discrete nature fails to capture the fine nuances in tumor consistency. To compensate for these shortcomings, we examined the utility of texture analysis to provide an objective observer-independent continuous measure of meningioma consistency.

METHODS

A total of 169 texturometric measurements were conducted using the Brookfield CT3 Texture Analyzer on meningioma samples from five patients immediately after the removal and on the first, second, and seventh postoperative day. The relationship between measured stiffness and time from sample extraction, subjectively assessed consistency grade and histopathological features (amount of collagen and reticulin fibers, presence of psammoma bodies, predominant microscopic morphology) was analyzed.

RESULTS

The stiffness measurements exhibited significantly lower variance within a sample than among samples (p = 0.0225) and significant increase with a higher objectively assessed consistency grade (p = 0.0161, p = 0.0055). A significant negative correlation was found between the measured stiffness and the time from sample extraction (p < 0.01). A significant monotonic relationship was revealed between stiffness values and amount of collagen I and reticulin fibers; there were no statistically significant differences between histological phenotypes in regard to presence of psammoma bodies and predominant microscopic morphology.

CONCLUSIONS

We conclude that the values yielded by texture analysis are highly representative of an intrinsic consistency-related quality of the sample despite the influence of intra-sample heterogeneity and that our proposed method can be used to conduct quantitative studies on the role of 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.

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.

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.

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.

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.

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).

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.

The Predictive Value of Conventional Magnetic Resonance Imaging Sequences on Operative Findings and Histopathology of Intracranial Meningiomas: A Prospective Study

Background

Previous studies have tried to relate magnetic resonance (MR) characteristics of meningiomas with its intraoperative features and histopathology with varying results. This is a prospective study to assess the independent predictive value of conventional MR signals [T1, T2, and fluid attenuated inversion recovery (FLAIR)] in relation to consistency, vascularity, operative plane, Simpson excision, and histopathology of intracranial meningiomas in a multivariate model.

Materials and Methods

Seventy patients underwent T1, T2, FLAIR, postcontrast sequences followed by excision of their meningiomas. Tumor signal intensity in various sequences was studied in relation to the above-mentioned variables. Multivariate analysis to find their independent association was performed.

Results

T1 images showed no correlation with any of the variables. FLAIR hypointensity and inhomogeneous enhancement had significant association with tumor hardness. FLAIR hypointense tumors were associated with low vascularity. FLAIR hypointensity, skull base location, and recurrence were significantly related to the subpial or mixed operative plane. The meningioma-brain interface on T2 sequence was significantly related to the operative plane. Only skull base location had significant impact on the extent of excision. T2 and FLAIR hypointensity had significant association with fibroblastic or psammomatous meningiomas. On multivariate analysis, FLAIR hypointensity and skull base location had a significant independent relationship with suboptimal operative plane, while skull base location had association with the extent of excision. Among MRI sequences, FLAIR hypointensity showed high specificity (94%) in predicting the suboptimal operative plane.

Conclusions

FLAIR hypointensity of meningiomas appears to have a significant independent association with the suboptimal operative plane with high specificity.

Pituitary adenoma consistency: Direct correlation of ultrahigh field 7T MRI with histopathological analysis

PURPOSE

Tumor consistency is a critical factor in surgical planning that influences ease of resection and risk of operative morbidity. The ability of MRI to predict tumor consistency tumor consistency has been shown to increase with higher field strength. The present study examined the utility of 7 T (7 T) MRI in predicting the tumor consistency of pituitary adenomas.

METHOD

Fifteen patients with pituitary adenomas were preoperatively scanned at 7 T MRI. Regions of interest were drawn around lesions for voxel-based signal intensity (SI) analysis. The percentage of tumor voxels with intensity higher than local gray matter was calculated on T2-weighted imaging. A single neurosurgeon rated tumor firmness for all patients. Histopathological analysis was performed. Radiological tumor features were correlated with intraoperative tumor consistency measurements and histopathology.

RESULTS

Tumors rated as 'soft' intraoperatively were hyperintense to local gray matter on T2-weighted imaging. 'Firm' tumors were hypointense to local gray matter. There was no significant difference in SI ratio between soft and firm tumors (p = 0.098). Soft tumors had a significantly higher percentage of tumor voxels greater than local gray matter compared to firm tumors (p = 0.035, Cohen's D-effect size = 1.208). Soft tumors had higher vascularity than firm tumors, p = 0.015.

CONCLUSIONS

The signal and contrast advantage conferred by 7 T MRI may provide valuable preoperative information regarding pituitary tumor consistency and physiology. The use of granular, voxel-based analysis maximizes the potential afforded by the high resolution of 7 T imaging, and may be a valuable method of predicting consistency of pituitary adenoma.

Companion animal models of neurological disease

Clinical translation of novel therapeutics that improve the survival and quality of life of patients with neurological disease remains a challenge, with many investigational drug and device candidates failing in advanced stage clinical trials. Naturally occurring inherited and acquired neurological diseases, such as epilepsy, inborn errors of metabolism, brain tumors, spinal cord injury, and stroke occur frequently in companion animals, and many of these share epidemiologic, pathophysiologic and clinical features with their human counterparts. As companion animals have a relatively abbreviated lifespan and genetic background, are immunocompetent, share their environment with human caregivers, and can be clinically managed using techniques and tools similar to those used in humans, they have tremendous potential for increasing the predictive value of preclinical drug and device studies. Here, we review comparative features of spontaneous neurological diseases in companion animals with an emphasis on neuroimaging methods and features, illustrate their historical use in translational studies, and discuss inherent limitations associated with each disease model. Integration of companion animals with naturally occurring disease into preclinical studies can complement and expand the knowledge gained from studies in other animal models, accelerate or improve the manner in which research is translated to the human clinic, and ultimately generate discoveries that will benefit the health of humans and animals.

Pretreatment texture analysis of routine MR images and shape analysis of the diffusion tensor for prediction of volumetric response after radiosurgery for meningioma

OBJECTIVEThe goal of this study was to identify parameters from routine T1- and T2-weighted MR sequences and diffusion tensor imaging (DTI) that best predict the volumetric changes in a meningioma after treatment with Gamma Knife radiosurgery (GKRS).METHODSIn 32 patients with meningioma, routine MRI and DTI data were measured before GKRS. A total of 78 parameters derived from first-level texture analysis of the pretreatment MR images, including calculation of the mean, SD, 2.5th and 97.5th percentiles, and kurtosis and skewness of data in histograms on a voxel-wise basis, were correlated with lesion volume change after a mean follow-up period of 3 years (range 19.5-63.3 months).RESULTSSeveral DTI-derived parameters correlated significantly with a meningioma volume change. The parameter that best predicted the results of GKRS was the 2.5th percentile value of the smallest eigenvalue (L3) of the diffusion tensor (correlation coefficient 0.739, p ≤ 0.001), whereas among the non-DTI parameters, only the SD of T2-weighted images correlated significantly with a tumor volume change (correlation coefficient 0.505, p ≤ 0.05, after correction for family-wise errors using false-detection-rate correction).CONCLUSIONSDTI-derived data had a higher correlation to shrinkage of meningioma volume after GKRS than data from T1- and T2-weighted image sequences. However, if only routine MR images are available, the SD of T2-weighted images can be used to predict control or possible progression of a meningioma after GKRS.

Petroclival meningiomas: radiological features essential for surgeons

Petroclival meningiomas (PCMs) have always been a challenge for surgeons because of their difficult anatomical location. The role of radiology in providing precise indications regarding the tumour site and aggressiveness plays a major part in guiding the subsequent therapeutic process. The purpose of this review is to provide a set of the main radiological features helpful in the management of PCMs towards the most correct therapeutic approach.

We aim to offer a radiological overview to allow the patient to be directed to surgery with the least possible risk of complications.

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.

Subtotal Resection of Cervical Dumbbell Schwannomas: Radiographic Predictors for Surgical Considerations

OBJECTIVE

Currently, radiologic predictors for the resectability of cervical dumbbell schwannomas remain unknown. To identify radiologic predictors for resectability, we retrospectively reviewed data from 72 patients.

METHODS

From January 1995 to June 2017, 72 patients who underwent surgical treatment for cervical dumbbell schwannomas were enrolled. We focused on the relationship between preoperative magnetic resonance imaging (MRI) features and the extent of tumor removal. The MRI features evaluated were tumor size, tumor level, Eden classification, degree of vertebral artery (VA) involvement, and signal intensity (SI) on T2-weighted images (WIs).

RESULTS

Among the 72 patients, gross total resection (GTR) and subtotal resection (STR) were achieved in 37 (51.4%) and 35 (48.6%) patients, respectively. Mean maximal tumor size (P = 0.011), mean size of foraminal and extraforaminal portion (P = 0.017), tumor level (P < 0.001), VA involvement (P < 0.001), and SI on T2-WIs (P = 0.006) were significantly different between the GTR and STR groups. Univariate analyses demonstrated that maximal tumor size (odds ratio [OR]: 0.93, P = 0.012), high cervical level (OR: 11.37, P < 0.001), pushed VA (OR: 0.11, P = 0.002), encased VA (OR: 0.02, P < 0.001), and hyper-SI on T2-WIs (OR: 12.46, P = 0.020) were significant predictors for GTR. In the multivariate analysis, only high cervical level (OR: 5.48, P = 0.033) and encased VA (OR: 0.07, P = 0.014) were significant predictors for GTR.

CONCLUSIONS

The resectability of cervical dumbbell schwannomas may be predicted by MRI features, including tumor size, tumor level, and degree of VA involvement.

Preoperative Evaluation of Tumor Adhesion to Adjacent Brain Tissue in Patients with Meningioma with BSMI Method and Its Comparison with the Width of Edema Around Tumor

Background

This study aims to investigate the ability of BSMI, to preoperative evaluation of tumor adhesion to adjacent brain tissue in patients with meningioma and comparing this method to the width of edema around tumor, using surgery findings as the reference standard.

Methods

Thirty patients with meningioma brain tumor who underwent surgery at Loghman hospital were selected for the study between November 2016 and January 2018. The level of edema according to the classification of Ide et al., (u1995) was compared with the surgical findings with blinded results, and neurosurgeons made a qualitative assessment of tumor adhesion at the time of resection. The ability of BSMI and level of edema to predict the surgical assessment of adhesion was tested using the Fisher Exact Test.

Results

BSMI method was conducted on patients with meningioma brain tumor, which judged 22 (73.3%) patients as adhesion (+) and 8 (26.66%) patients as adhesion (-). In this case, there was a significant relationship between BSMI judgment and surgical findings (p-value<0.0001). The sensitivity, specificity, precision and accuracy was high, at 91.30%, 85.71%, 95.45% and 90%, respectively. Using T2-Weighted SPACE sequence, of the 30 patients, 13 (43.3%) were judged as adhesion (+) and 17 (56.7%) as adhesion (-) from edema, whereas surgical findings evaluated 23 (76.7%) as adhesion (+) and 7 (23.3%) as adhesion (-).The sensitivity was moderate but the specificity was high, at 52.17% and 85.71%, respectively. Other criteria such as precision and accuracy were 62.31% and 60%, respectively.

Conclusions

BSMI evaluated adhesion of the tumor to the adjacent brain tissue with high-accuracy prior to surgery. This method was more effective than Edema method in evaluating adhesion between meningioma and the brain.

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.

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.

Exploring the biomechanical properties of brain malignancies and their pathologic determinants in vivo with magnetic resonance elastography

Malignant tumors are typically associated with altered rigidity relative to normal host tissue. Magnetic resonance elastography (MRE) enables the noninvasive quantitation of the mechanical properties of deep-seated tissue following application of an external vibrational mechanical stress to that tissue. In this preclinical study, we used MRE to quantify (kPa) the elasticity modulus Gd and viscosity modulus Gl of three intracranially implanted glioma and breast metastatic tumor models. In all these brain tumors, we found a notable softness characterized by lower elasticity and viscosity than normal brain parenchyma, enabling their detection on Gd and Gl parametric maps. The most circumscribed tumor (U-87 MG glioma) was the stiffest, whereas the most infiltrative tumor (MDA-MB-231 metastatic breast carcinoma) was the softest. Tumor cell density and microvessel density correlated significantly and positively with elasticity and viscosity, whereas there was no association with the extent of collagen deposition or myelin fiber entrapment. In conclusion, although malignant tumors tend to exhibit increased rigidity, intracranial tumors presented as remarkably softer than normal brain parenchyma. Our findings reinforce the case for MRE use in diagnosing and staging brain malignancies, based on the association of different tumor phenotypes with different mechanical properties.

Predicting Consistency of Meningioma by Magnetic Resonance Imaging

Objective Meningioma consistency is important because it affects the difficulty of surgery. To predict preoperative consistency, several methods have been proposed; however, they lack objectivity and reproducibility. We propose a new method for prediction based on tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios. Design The magnetic resonance (MR) images of 20 consecutive patients were evaluated preoperatively. An intraoperative consistency scale was applied to these lesions prospectively by the operating surgeon based on Cavitron Ultrasonic Surgical Aspirator (Valleylab, Boulder, Colorado, United States) intensity. Tumors were classified as A, very soft; B, soft/intermediate; or C, fibrous. Using T2-weighted MR sequence, the TCTI ratio was calculated. Tumor consistency grades and TCTI ratios were then correlated. Results Of the 20 tumors evaluated prospectively, 7 were classified as very soft, 9 as soft/intermediate, and 4 as fibrous. TCTI ratios for fibrous tumors were all ≤ 1; very soft tumors were ≥ 1.8, except for one outlier of 1.66; and soft/intermediate tumors were > 1 to < 1.8. Conclusion We propose a method using quantifiable region-of-interest TCTIs as a uniform and reproducible way to predict tumor consistency. The intraoperative consistency was graded in an objective and clinically significant way and could lead to more efficient tumor resection.

Diffusion tensor magnetic resonance imaging for predicting the consistency of intracranial meningiomas

BACKGROUND

The ability of preoperative MRI-sequences to predict the consistency of intracranial meningiomas has not yet been clearly defined. We aim to demonstrate that diffusion tensor imaging (DTI) improves the prediction of intracranial meningiomas consistency.

METHODS

We prospectively studied 110 meningioma patients operated on in a single center from March 1st to the 25th of May 2012. Demographic data, location and size of the tumor, peritumoral edema, T1WI, T2WI, proton density weighted (PDWI), fluid-attenuated inversion recover (FLAIR) sequences, and arterial spin labeling (ASL) perfusion were studied and compared with the gray matter signal to predict meningioma consistency. Diffusion tensor imaging (DTI) with fractional anisotropy (FA) and mean diffusivity (MD) maps were included in the preoperative MRI. Meningioma consistency was evaluated by the operating surgeon who was unaware of the neuroradiological findings.

RESULTS

In univariate analysis, meningioma size (diameter > 2 cm) and supratentorial or sphenoidal wing location were more frequently associated with hard-consistency meningiomas (p < 0.05). In addition, isointense signal on MD maps (p = 0.009), hyperintense signal on FA maps, and FA value > 0.3 (p = 0.00001) were associated with hard-consistency tumors. Age and sex, T1WI, T2WI, PDWI, FLAIR, or ASL perfusion sequences and peritumoral edema were not significantly associated with meningioma consistency. In logistic regression analysis, the most accurate model (AUC: 0.9459) for predicting a hard-consistency meningioma shows that an isointense signal in MD-maps, a hyperintense signal in FA-maps, and an FA value of more than 0.3 have a significant predictive value.

CONCLUSIONS

FA value and MD and FA maps are useful for prediction of meningioma consistency and, therefore, may be considered in the preoperative routine MRI examination of all patients with intracranial meningiomas.

A proposed grading system for standardizing tumor consistency of intracranial meningiomas

Object

Tumor consistency plays an important and underrecognized role in the surgeon's ability to resect meningiomas, especially with evolving trends toward minimally invasive and keyhole surgical approaches. Aside from descriptors such as “hard” or “soft,” no objective criteria exist for grading, studying, and conveying the consistency of meningiomas.

Methods

The authors designed a practical 5-point scale for intraoperative grading of meningiomas based on the surgeon's ability to internally debulk the tumor and on the subsequent resistance to folding of the tumor capsule. Tumor consistency grades and features are as follows: 1) extremely soft tumor, internal debulking with suction only; 2) soft tumor, internal debulking mostly with suction, and remaining fibrous strands resected with easily folded capsule; 3) average consistency, tumor cannot be freely suctioned and requires mechanical debulking, and the capsule then folds with relative ease; 4) firm tumor, high degree of mechanical debulking required, and capsule remains difficult to fold; and 5) extremely firm, calcified tumor, approaches density of bone, and capsule does not fold. Additional grading categories included tumor heterogeneity (with minimum and maximum consistency scores) and a 3-point vascularity score. This grading system was prospectively assessed in 50 consecutive patients undergoing craniotomy for meningioma resection by 2 surgeons in an independent fashion. Grading scores were subjected to a linear weighted kappa analysis for interuser reliability.

Results

Fifty patients (100 scores) were included in the analysis. The mean maximal tumor diameter was 4.3 cm. The distribution of overall tumor consistency scores was as follows: Grade 1, 4%; Grade 2, 9%; Grade 3, 43%; Grade 4, 44%; and Grade 5, 0%. Regions of Grade 5 consistency were reported only focally in 14% of heterogeneous tumors. Tumors were designated as homogeneous in 68% and heterogeneous in 32% of grades. The kappa analysis score for overall tumor consistency grade was 0.87 (SE 0.06, 95% CI 0.76–0.99), with 90% user agreement. Kappa analysis scores for minimum and maximum grades of tumor regions were 0.69 (agreement 72%) and 0.75 (agreement 78%), respectively. The kappa analysis score for tumor vascularity grading was 0.56 (agreement 76%). Overall consistency did not correlate with patient age, tumor location, or tumor size. A higher tumor vascularity grade was associated with a larger tumor diameter (p = 0.045) and with skull base location (p = 0.02).

Conclusions

The proposed grading system provides a reliable, practical, and objective assessment of meningioma consistency and facilitates communication among providers. This system also accounts for heterogeneity in tumor consistency. With the proposed scale, meningioma consistency can be standardized as groundwork for future studies relating to surgical outcomes, predictability of consistency and vascularity using neuroimaging techniques, and effectiveness of various surgical instruments.

A magnetic resonance imaging technique to evaluate tumor-brain adhesion in meningioma: brain-surface motion imaging

OBJECTIVE

We examined the effectiveness of a newly developed magnetic resonance imaging (MRI) technique, brain surface motion imaging (BSMI), in the preoperative evaluation of tumor-brain adhesion in meningioma surgery.

METHODS

Cine phase-contrast MRI was used to measure cerebrospinal fluid (CSF) pulsations and heart rates at 2 different time points to create a subtraction image in meningioma patients who underwent BSMI. With no tumor-brain adhesion, a gap was observed in the tumor-brain movements, resulting in an outline of the tumor in BSMI. If adhesion was evident, no outline was observed. Cases were evaluated as exact if the presence or absence of edema in T2-weighted MRI, BSMI findings, and intraoperative findings all matched; as effected when only BSMI findings and intraoperative images matched; and as false when BSMI findings and intraoperative findings did not match.

RESULTS

BSMI judged 27 patients as adhesion (+) and 33 as adhesion (-), whereas surgical findings evaluated 22 as adhesion (+) and 38 as adhesion (-). The sensitivity and specificity were both high, at 95.5% and 84.2%, respectively. Forty-one of 60 patients were evaluated as exact, 12 as effected, and 7 as false. World Health Organization tumor grade assessment of effected subjects included 16.7% in grade 1 and 36.4% in grade 2.

CONCLUSIONS

BSMI was shown to be effective in evaluating adhesion between the meningioma and the brain, allowing safe and effective removal planning to be carried out preoperatively.

Preoperative assessment of meningioma stiffness using magnetic resonance elastography

OBJECT

The object of this study was to determine the potential of magnetic resonance elastography (MRE) to preoperatively assess the stiffness of meningiomas.

METHODS

Thirteen patients with meningiomas underwent 3D brain MRE examination to measure stiffness in the tumor as well as in surrounding brain tissue. Blinded to the MRE results, neurosurgeons made a qualitative assessment of tumor stiffness at the time of resection. The ability of MRE to predict the surgical assessment of stiffness was tested using a Spearman rank correlation.

RESULTS

One case was excluded due to a small tumor size. In the remaining 12 cases, both tumor stiffness alone (p = 0.023) and the ratio of tumor stiffness to surrounding brain tissue stiffness (p = 0.0032) significantly correlated with the surgeons' qualitative assessment of tumor stiffness. Results of the MRE examination provided a stronger correlation with the surgical assessment of stiffness compared with traditional T1- and T2-weighted imaging (p = 0.089), particularly when considering meningiomas of intermediate stiffness.

CONCLUSIONS

In this cohort, preoperative MRE predicted tumor consistency at the time of surgery. Tumor stiffness as measured using MRE outperformed conventional MRI because tumor appearance on T1- and T2-weighted images could only accurately predict the softest and hardest meningiomas.

Predictors of meningioma consistency: A study in 243 consecutive cases

BACKGROUND

Meningioma is a common neoplasm primarily arising in the central nervous system. Its consistency is considered to be one of the critical prognostic factors for determining surgical resectability. The present study endeavored to investigate predictive factors associated with the tumor consistency.

METHODS

Two hundred and forty-three consecutive participants who underwent resective surgery of meningioma were examined. The authors designed an objective grading system for meningioma consistency and utilized it for assessing consistency among all cases. We focused on the relationship between preoperative tumor characteristics on neuroimaging studies and the consistency.

RESULTS

The tumor attributes on T2-weighted image (T2WI) and fluid attenuated inversion recovery (FLAIR) image were significantly correlated with the tumor consistency (p = 0.004 and 0.045, respectively). The hypointense tumors on both MRI sequences tended to be hard, whereas the tumors showing hypersignal intensity were associated with soft consistency. There was no correlation between the consistency and age, gender, duration of neurologic symptoms, tumor location, size, calcification, cystic portion, en plague appearance, tumor-brain contact interface expressed by cerebrospinal fluid (CSF) cleft, perilesional vasogenic edema, bony status, features on T1-weighted image (T1WI) and pattern of contrast enhancement. In multiple logistic regression analysis, the tumor characteristics on T2WI and FLAIR image were independent factors significantly correlated with the tumor consistency (p = 0.005 and 0.041, respectively). The tumor consistency was also correlated with operative radicalness as evaluated by the Simpson criteria.

CONCLUSIONS

Signal intensity on T2WI and FLAIR image can be used for insinuating meningioma consistency. Presurgical prediction of the consistency is highly valuable in operative planning, particularly in arduous cases.

Head, neck, and brain tumor embolization guidelines

Background

Management of vascular tumors of the head, neck, and brain is often complex and requires a multidisciplinary approach. Peri-operative embolization of vascular tumors may help to reduce intra-operative bleeding and operative times and have thus become an integral part of the management of these tumors. Advances in catheter and non-catheter based techniques in conjunction with the growing field of neurointerventional surgery is likely to expand the number of peri-operative embolizations performed. The goal of this article is to provide consensus reporting standards and guidelines for embolization treatment of vascular head, neck, and brain tumors.

Summary

This article was produced by a writing group comprised of members of the Society of Neurointerventional Surgery. A computerized literature search using the National Library of Medicine database (Pubmed) was conducted for relevant articles published between 1 January 1990 and 31 December 2010. The article summarizes the effectiveness and safety of peri-operative vascular tumor embolization. In addition, this document provides consensus definitions and reporting standards as well as guidelines not intended to represent the standard of care, but rather to provide uniformity in subsequent trials and studies involving embolization of vascular head and neck as well as brain tumors.

Conclusions

Peri-operative embolization of vascular head, neck, and brain tumors is an effective and safe adjuvant to surgical resection. Major complications reported in the literature are rare when these procedures are performed by operators with appropriate training and knowledge of the relevant vascular and surgical anatomy. These standards may help to standardize reporting and publication in future studies.

Determination of grade and subtype of meningiomas by using histogram analysis of diffusion-tensor imaging metrics

PURPOSE

To determine whether histogram analysis of diffusion-tensor (DT) magnetic resonance (MR) imaging metrics, including tensor shape measurements, can help determine the grades and subtypes of meningiomas.

MATERIALS AND METHODS

The institutional review board approved this HIPAA-compliant study. Nine atypical, three anaplastic, and 39 typical meningiomas were retrospectively studied. The 39 typical meningiomas included one secretory meningioma and 11 fibroblastic, 11 transitional, 14 meningothelial, and two angiomatous meningiomas. DT imaging metrics, including fractional anisotropy, mean diffusivity, linear anisotropy coefficient, planar anisotropy coefficient (CP), spherical anisotropy coefficient (CS), and eigenvalue skewness (SK), as well as normalized signal intensity from contrast-enhanced T1- and T2-weighted images, were measured from the enhancing region of the tumor. Mean, variance, skewness, and kurtosis were extracted from the histograms. A two-level decision tree was designed, and a multivariate logistic regression analysis was used at each level to determine the best model for classification.

RESULTS

Histogram skewness of SK and kurtosis of SK were significantly higher in atypical and anaplastic meningiomas than in typical meningiomas (P<.01). Among typical meningiomas, significant differences in histogram measures of CP and CS between fibroblastic meningiomas and other subtypes were observed (P<.01). The best model for differentiating atypical and anaplastic meningiomas from typical meningiomas consisted of mean and skewness of SK and kurtosis of T1 signal intensity, with an area under the receiver operating characteristic curve (AUC) of 0.946. The best model for differentiating fibroblastic meningiomas from other subtypes consisted of skewness of T2 signal intensity and kurtosis of CP (AUC, 0.970).

CONCLUSION

Histogram analysis of DT imaging metrics can help determine the grades and subtypes of meningiomas, which can better assist in surgical planning.

Defining the “edge of the envelope”: patient selection in treating complex sellar-based neoplasms via transsphenoidal versus open craniotomy

Object

Endonasal approaches have become the gold standard intervention for many anterior and middle skull base tumors. The authors aimed to define some of the existing limitations of these approaches by reviewing their experience with complex sellar region tumors that were initially considered for both transsphenoidal and open skull base approaches and were thus deemed tumors at “the edge of the envelope.”

Methods

Between April 2008 and April 2010, 250 transsphenoidal operations were performed at Brigham and Women's Hospital. All cases were retrospectively reviewed to identify patients with complex sellar region tumors that were initially considered for, or soon thereafter required, an open craniotomy as the definitive treatment. The anatomical tumor characteristics that posed limitations to performing safe and effective endonasal skull base operations were reviewed.

Results

Thirteen cases exemplifying some of the existing limitations to achieving optimal surgical outcomes via transsphenoidal-based approaches are presented. The following 8 factors are separately discussed that repeatedly limited the extent of resection, increased the risk of the operation, and contributed to perioperative complications: significant suprasellar extension, lateral extension, retrosellar extension, brain invasion with edema, firm tumor consistency, involvement or vasospasm of the arteries of the circle of Willis, and encasement of the optic apparatus or invasion of the optic foramina.

Conclusions

Although the ability to approach and resect complex tumors using endonasal skull base techniques has evolved dramatically in recent years, several inherent tumor characteristics mandate extensive preoperative consideration. In selected cases these characteristics may lend support to selecting an open craniotomy as the initial operation.

¹H-MRS of intracranial meningiomas: what it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor?

OBJECTIVE

The main goal of the present study was evaluation of proton magnetic resonance spectroscopy (¹H-MRS) in diagnosis of histopathologically aggressive intracranial meningiomas.

METHODS

Single-voxel ¹H-MRS of 100 intracranial meningiomas was performed before their surgical resection. Investigated metabolites included mobile lipids, lactate, alanine, N-acetylaspartate (NAA), and choline-containing compounds (Cho). According to criteria of World Health Organization (WHO) 82 meningiomas were assigned histopathological grade I, 11 grade II, and 7 grade III. The MIB-1 index varied from 0% to 27.3% (median, 1.6%). In 43 cases tight adhesion of the tumor to the pia mater or brain tissue was macroscopically identified at surgery. The consistency of 49 meningiomas was characterized as soft, 26 as hard, and 25 as mixed.

RESULTS

No one metabolic parameter had statistically significant association with histopathological grade and subtype, invasive growth, and consistency of meningioma. Univariate statistical analysis revealed greater ¹H-MRS-detected Cho content (P=0.0444) and lower normalized NAA/Cho ratio (P=0.0203) in tumors with MIB-1 index 5% and more. However, both parameters lost their statistical significance during evaluation in the multivariate model along with other clinical and radiological variables. It was revealed that non-benign histopathology of meningioma (WHO grade II/III) is mainly predicted by irregular shape (P=0.0076) and large size (P=0.0316), increased proliferative activity by irregular shape (P=0.0056), and macroscopically invasive growth by prominent peritumoral edema (P=0.0021).

CONCLUSION

While ¹H-MRS may be potentially used for the identification of meningiomas with high proliferative activity, it, seemingly, could not add substantial diagnostic information to other radiological predictors of malignancy in these tumors.

Dynamic contrast-enhanced magnetic resonance imaging of canine brain tumors

We evaluated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in canine brain tumors. Magnetic resonance data sets were collected on seven canine intracranial tumors with a 3 T magnet using a T1-weighted fast spin echo fluid attenuated inversion recovery sequence after an IV bolus injection (0.2 mmol/kg) of Gd-DTPA. The tumors were confirmed histopathologically as adenocarcinoma (n=1), ependymoma (n=1), meningioma (n=3), oligodendroglioma (n=1), and pituitary macroadenoma (n=1) The data were analyzed using a two-compartment pharmacokinetic model for estimation of three enhancement parameters, E(R) (rate of enhancement), Kel (rate of elimination), and Kep (rate constant), and a model-free phenomenologic parameter initial area under the Gd concentration curve (IAUGC) defined over the first 90s postenhancement. Pearson's correlations were calculated between parameters of the two methods. The IAUGC has a relatively strong association with the rate of enhancement E(R), with r ranges from 0.4 to 0.9, but it was weakly associated with Kep and Kel. To determine whether any two tumors differed significantly, the Kohnlmogorov-Smirnov test was used. The results showed that there were statistical differences (P < 0.05) between distributions of the enhancement pattern of each tumor. These kinetic parameters may characterize the perfusion and vascular permeability of the tumors and the IAUGC may reflect blood flow, vascular permeability, and the fraction of interstitial space. The kinetic parameters and the IAUGC derived from DCE-MRI present complementary information and they may be appropriate to noninvasively differentiate canine brain tumors although a larger prospective study is necessary.

Role of diffusion tensor imaging in differentiating subtypes of meningiomas

PURPOSE

Meningiomas are the most common extraaxial intracranial type of tumor, and their management and prognosis depend on their grade and histology. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are two new imaging techniques that have proved helpful in elucidating the microarchitecture of brain tumors. The aim of the present study was to assess the role of diffusion and diffusion tensor metrics in the identification and classification of meningioma grades and subtypes.

METHODS AND MATERIALS

A total of 21 consecutive patients with meningioma were included in this retrospective study, of whom 16 had benign meningiomas (three fibroblastic, 11 transitional/mixed, two meningothelial) and five had atypical meningiomas. Tumor mean diffusivity (Dav), fractional anisotropy (FA), linear anisotropy (CL), planar anisotropy (CP), spherical anisotropy (CS) and eigenvalues (e1, e2, e3) were measured in all cases, and differences in diffusion tensor metrics between atypical, fibroblastic and other benign (transitional, meningothelial) meningiomas were statistically analyzed using the Mann-Whitney test.

RESULTS

No statistically significant differences were found among the mean Dav values for atypical, fibroblastic and other benign meningiomas. Both atypical and fibroblastic meningiomas showed significantly higher mean FA values and lower mean CS values compared with other meningiomas (P<0.01), but no statistically significant difference in these values between each other. Atypical meningiomas showed higher CL values compared with fibroblastic and other benign meningiomas but, again, the difference was not statistically significant. Both atypical and fibroblastic meningiomas showed statistically significantly higher CP values and lower e3 values compared with transitional meningiomas (P<0.01).

CONCLUSION

These results suggest that diffusion tensor metrics may be helpful in the differentiation of atypical, fibroblastic and other benign meningiomas.

Brain surface motion imaging to predict adhesions between meningiomas and the brain surface

INTRODUCTION

"Brain surface motion imaging" (BSMI) is the subtraction of pulse-gated, 3D, heavily T2-weighted image of two different phases of cerebrospinal fluid (CSF) pulsation, which enables the assessment of the dynamics of brain surface pulsatile motion. The purpose of this study was to evaluate the feasibility of this imaging method for providing presurgical information about adhesions between meningiomas and the brain surface.

METHODS

Eighteen cases with surgically resected meningioma in whom BSMI was presurgically obtained were studied. BSMI consisted of two sets of pulse-gated, 3D, heavily T2-weighted, fast spin echo scans. Images of the systolic phase and the diastolic phase were obtained, and subtraction was performed with 3D motion correction. We analyzed the presence of band-like texture surrounding the tumor and judged the degree of motion discrepancy as "total," "partial," or "none." The correlation between BSMI and surgical findings was evaluated. For cases with partial adhesions, agreements in the locations of the adhesions were also evaluated.

RESULTS

On presurgical BSMI, no motion discrepancy was seen in eight cases, partial in six cases, and total in four cases. These presurgical predictions about adhesions and surgical findings agreed in 13 cases (72.2%). The locations of adhesions agreed in five of six cases with partial adhesions.

CONCLUSION

In the current study, BSMI could predict brain and meningioma adhesions correctly in 72.2% of cases, and adhesion location could also be predicted. This imaging method appears to provide presurgical information about brain/meningioma adhesions.

ABC Surgical Risk Scale for skull base meningioma: a new scoring system for predicting the extent of tumor removal and neurological outcome. Clinical article

OBJECT

Surgery for skull base meningiomas (SBMs) can lead to complications because these lesions are difficult to approach and can involve cranial nerves and arteries. The authors propose a scoring system to evaluate the relative risks and benefits of surgical treatment of SBMs.

METHODS

The authors used a 2-step process to construct their scale. First, they derived significant predictive variables from retrospective data on 132 SBM cases treated surgically (primary surgeries only) between May 2000 and December 2005. Next, they validated the predictive accuracy of their scoring system in 60 consecutive cases treated surgically between January 1995 and April 2000, including both primary and repeated surgeries. Finally, they investigated the effect of the surgery on the patients' preoperative symptoms for consecutive cases treated surgically between January 1995 and December 2005, including both primary surgeries and retreatments.

RESULTS

Five items that predicted surgical risk were identified: 1) tumor attachment size; 2) arterial involvement; 3) brainstem contact; 4) central cavity location; and 5) cranial nerve group involvement. The authors named their scoring system the ABC Surgical Risk Scale, after the initial letters of these items. Each factor was assigned a score of 0-2 points, and an additional point was added for previous surgical treatment or for radiation, giving a possible total score of 12 points. On average, the scoring system allocated 2 points for gross-total resections, 6.1 points for near-total resections, and 9 points for subtotal resections, with significant differences between groups. For cases scoring >or= 8 points, the percentage of cases showing neurological deterioration postoperatively exceeded the percentage showing improvement.

CONCLUSIONS

The authors conclude that this scoring system can be used to predict the extent of tumor removal and that the scores reflect the surgical risk.

Recurrence and regrowth of benign meningiomas

The World Health Organization (WHO) grading system for meningioma is helpful for predicting aggressive subtypes. However, even benign meningiomas sometimes show relatively rapid growth and may recur after total removal. We attempted to find histopathological features that would be valuable for predicting recurrence or regrowth of WHO grade I meningiomas. We investigated 135 benign meningiomas, of which 120 were totally removed (Simpson's grade I-III). The median follow-up period was 9.7 years (1-21 years). The recurrence rate in the patients with total removal was 7.5% at 10 years and 9.3% at 20 years. The univariate analysis revealed that MIB-1 index (>or=2%), existence of mitosis, absence of calcification, and paucity of fibrosis significantly correlated with recurrence. On the other hand, the histological features of sheet-like growth, prominent nucleoli, and necrosis did not correlate with recurrence, because they were relatively rare in grade I tumors. Multivariate analysis revealed that high MIB-1 index and absence of calcification significantly correlated with recurrence. The patients with recurrent or residual tumors did not always receive adjuvant treatment. Including subtotally treated tumors, the retreatment rate was 9.8% at 10 years and 25.6% at 20 years. MIB-1 index and Simpson's grade significantly correlated with retreatment in both univariate and multivariate analyses.

Prediction of meningioma consistency using fractional anisotropy value measured by magnetic resonance imaging

OBJECT

Preoperative planning for meningiomas requires information about tumor consistency as well as location and size. In the present study the authors aimed to determine whether the fractional anisotropy (FA) value calculated on the basis of preoperative magnetic resonance (MR) diffusion tensor (DT) imaging could predict meningioma consistency.

METHODS

In 29 patients with intracranial meningiomas, MR DT imaging was performed preoperatively, and the FA values of the tumors were calculated. Tumor consistency was intraoperatively determined as hard or soft, and the histological diagnosis of the tumor was established.

RESULTS

Of the 29 tumors, 11 were classified as hard and 18 as soft. The FA values of fibroblastic meningiomas were significantly higher than those of meningothelial meningiomas (p = 0.002). The FA values of hard tumors were significantly higher than those of soft tumors (p = 0.0003). Logistic regression analysis demonstrated that the FA value was a significant independent predictor of tumor consistency (p = 0.007).

CONCLUSIONS

The FA value calculated from preoperative MR DT imaging predicts meningioma consistency.

Differentiation of fibroblastic meningiomas from other benign subtypes using diffusion tensor imaging

PURPOSE

To differentiate fibroblastic meningiomas, usually considered to be of a hard consistency, from other benign subtypes using diffusion tensor imaging (DTI).

MATERIALS AND METHODS

From DTI data sets of 30 patients with benign meningiomas, we calculated diffusion tensors and mean diffusivity (MD) and fractional anisotropy (FA) maps as well as barycentric maps representing the geometrical shape of the tensors. The findings were compared to postoperative histology. The study was approved by the local ethics committee, and informed consent was given by the patients.

RESULTS

According to one-way analysis of variance (ANOVA), FA was the best parameter to differentiate between the subtypes (F=32.2; p<0.0001). Regarding tensor shape, endothelial meningiomas were represented by spherical tensors (80%) corresponding to isotropic diffusion, whereas the fibroblastic meningiomas showed a high percentage (43%) of nonspherical tensors, indicating planar or longitudinal diffusion. The difference was highly significant (F=28.4; p<0.0001) and may be due to the fascicular arrangement of long spindle-shaped tumor cells and the high content of intra- and interfascicular fibers as shown in the histology. In addition, a capsule-like rim of the in-plane diffusion surrounded most meningiomas irrespective of their histological type.

CONCLUSION

If these results correlate to the intraoperative findings of meningioma consistency, DTI-based measurement of FA and analysis of the shape of the diffusion tensor is a promising method to differentiate between fibroblastic and other subtypes of benign meningiomas in order to get information about their "hard" or "soft" consistency prior to removal.

Meningeoma da goteira olfatória

Meningeomas da goteira olfatória representam 8-18% dos meningeomas intracranianos e geralmente são diagnosticados quando alcançam grande tamanho. Relatamos uma série de 13 casos consecutivos de meningeomas de goteira olfativa operados nos Hospitais São Paulo (UNIFESP) e Professor Edmundo Vasconcelos no período de 1995 a 2003, estudados retrospectivamente quanto ao quadro clínico, resultados cirúrgicos e complicações. Os pacientes foram submetidos a ressecção cirúrgica destes tumores pela via subfrontal, em 9 casos a ressecção foi completa incluindo dura-máter e osso infiltrado por tumor e em 4 a dura-máter foi somente coagulada. Um paciente morreu devido a infarto cerebral e três pacientes evoluíram com fístulas liquóricas. Não houve recorrência sintomática no período que variou de 11 meses a 8 anos. Com as técnicas microcirúrgicas atuais, estes tumores podem ser removidos com baixa morbidade.