Meningioma Consistency Research Articles

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management.

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

Prediction of consistency of intracranial meningiomas based on conventional MRI examination

Prediction of consistency of intracranial meningiomas based on conventional MRI examination

Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI.

Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a noninvasive prediction model for meningioma consistency utilizing MR elastography and DTI. Ninety-four patients (52 ± 22 years old, 69 women, 25 men) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1WI, T2WI, DTI, and MR elastography. Combined MR elastography-DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated by using a receiver operating characteristic curve. Further validation was conducted in 27 stereotactic biopsies by using indentation tests and underlying mechanism was investigated by histologic analysis. Among all the imaging modalities, MR elastography demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.699-0.929). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.784-0.971), surpassing any technique alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MR elastography-DTI model was suitable for predicting intratumor consistency. Histologic analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content. The MR elastography-DTI combined model is effective in noninvasive prediction of meningioma consistency.

Application of Т1, Т2‑mapping technique for evaluation of intracranial meningioma consistency

Background. Meningiomas are the most common extracerebral intracranial neoplasms. Radicality of meningioma resection largely depends on its consistency, size, and closeness to important anatomical structures. Preoperative prognosis of meningioma density plays an important role in selection of surgical access and operative inventory, general radicality of tumor resection, and neurological outcome after surgery.Aim. To determine predictors of intracranial meningioma consistency using magnetic resonance imaging and T1, T2‑mapping technique.Materials and methods. The study included 96 patients with primary meningiomas who underwent surgery at the Federal Neurosurgical Center (Novosibirsk) between 2018 and 2021. Magnetic resonance images were analyzed using calculation of the ratio between signal intensity on T1‑weighted and T2‑weighted images with subsequent group and correlation statistical analysis for comparison of T1, T2‑mapping results with clinical, histological and intraoperative data.Results. Statistically significant increase in the signal intensity and standard deviation from the mean intensity on T1, T2‑maps of psammomatous meningiomas compared to all other subtypes was observed. Additionally, positive correlation between signal intensity form meningiomas on T1, T2‑maps and intraoperative data on tumor consistency was found.Conclusion. Images obtained using T, T2‑mapping technique are as informative as traditional T2‑weighted images for evaluation of intracranial meningioma consistency. Additionally, they allow to obtain absolute intensity values. Further prospective studies are necessary for confirmation of the obtained results.

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

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

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

PurposeThis study aims to combine deep learning features with radiomics features for the computer-assisted preoperative assessment of meningioma consistency. Methods202 patients with surgery and pathological diagnosis of meningiomas at our institution between December 2016 and December 2018 were retrospectively included in the study. The T2-fluid attenuated inversion recovery (T2-Flair) images were evaluated to classify meningioma as soft or hard by professional neurosurgeons based on Zada’s consistency grading system. All the patients were split randomly into a training cohort (n = 162) and a testing cohort (n = 40). A convolutional neural network (CNN) model was proposed to extract deep learning features. These deep learning features were combined with radiomics features. After multiple feature selections, selected features were used to construct classification models using four classifiers. AUC was used to evaluate the performance of each classifier. A signature was further constructed by using the least absolute shrinkage and selection operator (LASSO). A nomogram based on the signature was created for predicting meningioma consistency. ResultsThe logistic regression classifier constructed using 17 radiomics features and 9 deep learning features provided the best performance with a precision of 0.855, a recall of 0.854, an F1-score of 0.852 and an AUC of 0.943 (95 % CI, 0.873–1.000) in the testing cohort. The C-index of the nomogram was 0.822 (95 % CI, 0.758–0.885) in the training cohort and 0.943 (95 % CI, 0.873–1.000) in the testing cohort with good calibration. Decision curve analysis further confirmed the clinical usefulness of the nomogram for predicting meningioma consistency. ConclusionsThe proposed method for assessing meningioma consistency based on the fusion of deep learning features and radiomics features is potentially clinically valuable. It can be used to assist physicians in the preoperative determination of tumor consistency.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BackgroundThe consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.MethodsForty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student’s t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.ResultsDDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).ConclusionHistogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

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

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. A total of 169 texturometric measurements were conductedusing 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 andtime from sample extraction, subjectively assessedconsistency grade and histopathological features (amountof collagen and reticulin fibers, presence of psammoma bodies, predominant microscopicmorphology) wasanalyzed. 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. 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.

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.

Virtual magnetic resonance elastography predicts the intraoperative consistency of meningiomas

Background and purposeVirtual magnetic resonance elastography (vMRE) is an experimental imaging modality designed to non-invasively predict the haptic properties of tissues. The modality is sensitive to tissue stiffness and fibrosis. Information about meningioma consistency prior to resection is of great interest in neurological surgery as the surgical plan and outcome may be affected by the tumor's stiffness. In this study, we assessed the ability of vMRE to predict the intraoperative consistency and mechanical heterogeneity of intracranial meningiomas. Materials and methodsWe included 12 patients scheduled for meningioma resection, of which one patient was found to have a solitary fibrous tumor on histological examination. All participants underwent preoperative vMRE and intraoperative consistency grading. Results and ConclusionsIntraoperative qualitative consistency correlated positively with vMRE-based consistency assessment (odds ratio 5.63, 95% CI 1.12–28.30, p = 0.04) at b1000. Mechanically homogenous tumors had significantly lower ∆ mean stiffness than heterogeneous tumors (8.13 vs 18.07 kPa, p = 0.01). This study thus demonstrates a possible clinical application of vMRE in predicting the intraoperative consistency and mechanical heterogeneity of meningiomas.

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

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. 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. 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. 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.

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

Abstract 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 &lt; 0.05. ADCmin value of &lt; 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 &lt; 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 &lt; 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.

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

BackgroundMeningiomas 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.ResultsAccording 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.ConclusionsTCTI 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.

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

BackgroundPreoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach. PurposeTo investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type. Materials and methods30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm2. The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components—the anisotropic and isotropic kurtoses (MKA and MKI). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor (“whole-tumor”) and within brain tissue in the immediate periphery outside the tumor (“rim”) by histogram analysis. ResultsLower 10th percentiles of MK and MKA in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MKA 10 and p = 0.04 for MK10) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MKI in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MKI maps we observed elevated rim-like structure that could be associated with grade. Higher median MKA and lower median MKI distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MKA 50 and p = 0.03 and p = 0.04 for MKI 50). ConclusionParameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.

Correlations Among Consistency, Computed Tomography Values, and Histopathological Subtypes of Spinal Meningioma.

The consistency of spinal meningiomas is important to consider when performing tumor removal surgery. This study evaluated the correlations between spinal meningioma consistency and both preoperative computed tomography (CT) values and histopathological subtypes. Fifteen consecutive patients who underwent surgical resection of spinal meningioma at our institution were identified, and preoperative CT values and the signal intensity of T2-weighted magnetic resonance images of the tumor were determined retrospectively. The consistency of the spinal meningioma was defined based on the ultrasonic surgical aspirator output during tumor debulking. Patients were assigned to 2 groups: a soft group (n=4) and a hard group (n=11). The T2 signal intensity was significantly higher in the soft group than in the hard group (p=0.001). While the CT values were considerably higher in the hard group, the difference was not significant (p=0.19). Regarding the histopathological subtypes, psammomatous meningioma exhibited significantly higher CT values than meningothelial meningioma (p=0.019); however, there was a higher frequency of hard tumors in meningothelial meningioma cases than in psammomatous meningioma cases. Although neither robust correlations between tumor consistency and CT values nor a relationship between tumor consistency and histopathological subtype has been established, these results might help with the perioperative manegement of spinal tumors.

Magnetic Resonance Fingerprinting for Preoperative Meningioma Consistency Prediction

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

Usefulness of T2 Relaxation Time for Quantitative Prediction of Meningioma Consistency

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. 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. 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). 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.

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

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

Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics.

ObjectivesThe aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.MethodsA total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada’s consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination.ResultsA total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts.ConclusionsThe presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.

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

ABSTRACT 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.