MRI predictors for brain invasion in meningiomas

Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study

OBJECTIVE

To develop a nomogram based on tumor and peritumoral edema (PE) radiomics features extracted from preoperative multiparameter MRI for predicting brain invasion (BI) in atypical meningioma (AM).

METHODS

In this retrospective study, according to the 2021 WHO classification criteria, a total of 469 patients with pathologically confirmed AM from three medical centres were enrolled and divided into training (n = 273), internal validation (n = 117) and external validation (n = 79) cohorts. BI was diagnosed based on the histopathological examination. Preoperative contrast-enhanced T1-weighted MR images (T1C) and T2-weighted MR images (T2) for extracting meningioma features and T2-fluid attenuated inversion recovery (FLAIR) sequences for extracting meningioma and PE features were obtained. The multiple logistic regression was applied to develop separate multiparameter radiomics models for comparison. A nomogram was developed by combining radiomics features and clinical risk factors, and the clinical usefulness of the nomogram was verified using decision curve analysis.

RESULTS

Among the clinical factors, PE volume and PE/tumor volume ratio are the risk of BI in AM. The combined nomogram based on multiparameter MRI radiomics features of meningioma and PE and clinical indicators achieved the best performance in predicting BI in AM, with area under the curve values of 0.862 (95% CI, 0.819-0.905) in the training cohort, 0.834 (95% CI, 0.780-0.908) in the internal validation cohort and 0.867 (95% CI, 0.785-0.950) in the external validation cohort, respectively.

CONCLUSIONS

The nomogram based on tumor and PE radiomics features extracted from preoperative multiparameter MRI and clinical factors can predict the risk of BI in patients with AM.

MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion

The aim of this study was to use multimodal imaging (contrast-enhanced T1-weighted (T1C), T2-weighted (T2), and diffusion-weighted imaging (DWI)) to develop a radiomics model for preoperatively predicting venous sinus invasion in meningiomas. This prediction would assist in selecting the appropriate surgical approach and forecasting the prognosis of meningiomas. A retrospective analysis was conducted on 331 participants who had been pathologically diagnosed with meningiomas. For each participant, 3948 radiomics features were acquired from the T1C, T2, and DWI images. Minimum redundancy maximum correlation, rank sum test, and multi-factor recursive elimination were used to extract the most significant features of different models. Then, multivariate logistic regression was used to build classification models to predict meningioma venous sinus invasion. The diagnostic capabilities were assessed using receiver operating characteristic (ROC) analysis. In addition, a nomogram was constructed by incorporating clinical and radiological characteristics and a radiomics signature. To assess the clinical usefulness of the nomogram, a decision curve analysis (DCA) was performed. Tumor shape, boundary, and enhancement features were independent predictors of meningioma venous sinus invasion (p = 0.013, p = 0.013, p = 0.005, respectively). Eleven (T2:1, T1C:4, DWI:6) of the 3948 radiomics features were screened for strong association with meningioma sinus invasion. The areas under the ROC curves for the training and external test sets were 0.946 and 0.874, respectively. The clinicoradiomic model showed excellent predictive performance for invasive meningioma, which may help to guide surgical approaches and predict prognosis.

Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans

BACKGROUND

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses.

NEW METHOD

The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images.

RESULTS

QUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy.

COMPARISON WITH EXISTING METHODS

QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis.

CONCLUSION

QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

Meningiomas with CNS invasion

CNS invasion has been included as an independent criterion for the diagnosis of a high-grade (WHO and CNS grade 2 and 3) meningioma in the 2016 and more recently in the 2021 WHO classification. However, the prognostic role of brain invasion has recently been questioned. Also, surgical treatment for brain invasive meningiomas may pose specific challenges. We conducted a systematic review of the 2016-2022 literature on brain invasive meningiomas in Pubmed, Scopus, Web of Science and the Cochrane Library. The prognostic relevance of brain invasion as a stand-alone criterion is still unclear. Additional and larger studies using robust definitions of histological brain invasion and addressing the issue of sampling errors are clearly warranted. Although the necessity of molecular profiling in meningioma grading, prognostication and decision making in the future is obvious, specific markers for brain invasion are lacking for the time being. Advanced neuroimaging may predict CNS invasion preoperatively. The extent of resection (e.g., the Simpson grading) is an important predictor of tumor recurrence especially in higher grade meningiomas, but also - although likely to a lesser degree - in benign tumors, and therefore also in brain invasive meningiomas with and without other histological features of atypia or malignancy. Hence, surgery for brain invasive meningiomas should follow the principles of maximal but safe resections. There are some data to suggest that safety and functional outcomes in such cases may benefit from the armamentarium of surgical adjuncts commonly used for surgery of eloquent gliomas such as intraoperative monitoring, awake craniotomy, DTI tractography and further advanced intraoperative brain tumor visualization.

Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas

OBJECTIVES

Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model.

METHODS

Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation.

RESULTS

Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set.

CONCLUSION

The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas.

KEY POINTS

• Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.

Type IV collagen-derived angiogenesis inhibitor: canstatin low expressing in brain-invasive meningiomas using liquid chromatography-mass spectrometry (LC-MS/MS)

PURPOSE

Brain invasion in meningiomas is considered an indicator of more aggressive behavior and worse prognosis. But the precise definition and the prognostic role of brain invasion remains unsolved duo to lacking a standardized workflow of surgical sampling and the histopathological detection. Searching for molecular biomarker expression correlating with brain invasion, could contribute to establish a molecular pathological diagnosis without problems of subjective interobserver variation and deeply understand the mechanism of brain invasion and develop innovative therapeutic strategies.

METHODS

We utilized liquid chromatography tandem mass spectrometry to quantify protein abundances between non-invasive meningiomas (n = 21) and brain-invasive meningiomas (n = 21) spanning World Health Organization grades I and III. After proteomic discrepancies were analyzed, the 14 most up-regulated or down-regulated proteins were recorded. Immunohistochemical staining for glial fibrillary acidic protein and most likely brain invasion-related proteins was performed in both groups.

RESULTS

A total of 6498 unique proteins were identified in non-invasive and brain-invasive meningiomas. Canstatin expression in the non-invasive group was 2.1-fold that of the brain-invasive group. The immunohistochemical staining showed canstatin expressed in both groups, and the non-invasive group showed stronger staining for canstatin in the tumor mass (p = 0.0132) than the brain-invasive group, which showed moderate intensity.

CONCLUSION

This study demonstrated the low expression of canstatin in meningiomas with brain invasion, a finding that provide a basis for understanding the mechanism of brain invasion of meningiomas and may contribute to establish molecular pathological diagnosis and identify novel therapeutic targets for personalized care.

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.

Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?

Simple Summary

Meningioma is still the most common adult tumor of the CNS, most of which are slow-growing, benign tumors and could even be accidentally diagnosed; nonetheless, they sometimes show more aggressive behavior with higher recurrence rates and relatively reduced overall survival. Assuming this, in recent years, scientific research has been accelerated, looking for new insights and applications that could improve preoperative investigation, tailor surgical planning, and strongly impact meningioma patients’ prognosis. Many fields have been developed, and the detection of brain invasion has firmly gained its potential role, leading to the revised version of WHO for CNS tumors in 2016 as a further criterion for defining atypia. Further studies are still ongoing to assess a widely accepted application of BI evaluation in intracranial meningioma management.

Abstract

Several recent studies are providing increasing insights into reliable markers to improve the diagnostic and prognostic assessment of meningioma patients. The evidence of brain invasion (BI) signs and its associated variables has been focused on, and currently, scientific research is investing in the study of key aspects, different methods, and approaches to recognize and evaluate BI. This paradigm shift may have significant repercussions for the diagnostic, prognostic, and therapeutic approach to higher-grade meningioma, as long as the evidence of BI may influence patients’ prognosis and inclusion in clinical trials and indirectly impact adjuvant therapy. We intended to review the current knowledge about the impact of BI in meningioma in the most updated literature and explore the most recent implications on both clinical practice and trials and future directions. According to the PRISMA guidelines, systematic research in the most updated platform was performed in order to provide a complete overview of characteristics, preoperative applications, and potential implications of BI in meningiomas. Nineteen articles were included in the present paper and analyzed according to specific research areas. The detection of brain invasion could represent a crucial factor in meningioma patients’ management, and research is flourishing and promising.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features

Background

Brain invasion in meningioma has independent associations with increased risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study aimed to construct a model for predicting brain invasion in WHO grade II meningioma by using preoperative MRI.

Methods

One hundred seventy-three patients with brain invasion and 111 patients without brain invasion were included. Three mainstream features, namely, traditional semantic features and radiomics features from tumor and tumor-to-brain interface regions, were acquired. Predictive models correspondingly constructed on each feature set or joint feature set were constructed.

Results

Traditional semantic findings, e.g., peritumoral edema and other four features, had comparable performance in predicting brain invasion with each radiomics feature set. By taking advantage of semantic features and radiomics features from tumoral and tumor-to-brain interface regions, an integrated nomogram that quantifies the risk factor of each selected feature was constructed and had the best performance in predicting brain invasion (area under the curve values were 0.905 in the training set and 0.895 in the test set).

Conclusions

This study provided a clinically available and promising approach to predict brain invasion in WHO grade II meningiomas by using preoperative MRI.

Brain Invasion in Meningioma-A Prognostic Potential Worth Exploring

Most meningiomas are slow growing tumors arising from the arachnoid cap cells and can be cured by surgical resection or radiation therapy in selected cases. However, recurrent and aggressive cases are also quite common and challenging to treat due to no established treatment alternatives. Assessment of the risk of recurrence is therefore of utmost importance and several prognostic clinical and molecular markers have been established. Additionally, the identification of invasive growth of meningioma cells into CNS tissue was demonstrated to lead to a higher risk of recurrence and was therefore integrated into the WHO classification of CNS tumors. However, the evidence for its prognostic impact has been questioned in subsequent studies and its exclusion from the next WHO classification proposed. We were recently able to show the prognostic impact of CNS invasion in a large comprehensive retrospective meningioma cohort including other established prognostic factors. In this review we discuss the growing experiences that have been gained on this matter, with a focus on the currently nonuniform histopathological assessment, imaging characteristics and intraoperative sampling as well as the overall outlook on the future role of this potential prognostic factor.