Prognostic Characterization of Higher-Grade Meningiomas: A Histopathological Score to Predict Progression and Outcome

Atypical meningioma: Histopathological, genetic, and epigenetic features to predict recurrence risk

Grading assessed according to World Health Organization (WHO) criteria is a major prognostic factor for determining the risk of recurrence in patients with meningiomas and establishing the most appropriate therapeutic strategy after surgery. However, the main issue is to predict the recurrence risk of WHO grade 2 meningioma and, more specifically, of the atypical subtype. Indeed, owing to a reported recurrence rate of 50%, either radiotherapy or observation is currently considered an option after gross total surgical resection of atypical meningiomas. These heterogeneous clinical outcomes are likely related to the broad histopathological diagnostic criteria for this subtype, and whether meningiomas only present as brain invasion should be classified as atypical remains controversial. Over the last few years, several studies have shown that DNA methylation profiling, next-generation sequencing, and transcriptomics can better stratify meningiomas for their recurrence risk than histology. The main limitations to the widespread use of these approaches to classify meningiomas are their high cost and the need for sophisticated technologies. However, all studies concurred that atypical meningiomas without chromosome 1p deletion display a low recurrence risk, suggesting that the assessment of this cytogenetic alteration could represent an easy and quick method to determine which patients could benefit from adjuvant treatment after surgery. In addition, prognostically unfavorable molecular groups can be distinguished using specific immunostainings, although further validation is required.

A prognostic model for tumor recurrence and progression after meningioma surgery: preselection for further molecular work-up

Purpose

The selection of patients for further therapy after meningioma surgery remains a challenge. Progress has been made in this setting in selecting patients that are more likely to have an aggressive disease course by using molecular tests such as gene panel sequencing and DNA methylation profiling. The aim of this study was to create a preselection tool warranting further molecular work-up.

Methods

All patients undergoing surgery for resection or biopsy of a cranial meningioma from January 2013 until December 2018 at the University Hospital Zurich with available tumor histology were included. Various prospectively collected clinical, radiological, histological and immunohistochemical variables were analyzed and used to train a logistic regression model to predict tumor recurrence or progression. Regression coefficients were used to generate a scoring system grading every patient into low, intermediate, and high-risk group for tumor progression or recurrence.

Results

Out of a total of 13 variables preselected for this study, previous meningioma surgery, Simpson grade, progesterone receptor staining as well as presence of necrosis and patternless growth on histopathological analysis of 378 patients were included into the final model. Discrimination showed an AUC of 0.81 (95% CI 0.73 - 0.88), the model was well-calibrated. Recurrence-free survival was significantly decreased in patients in intermediate and high-risk score groups (p-value < 0.001).

Conclusion

The proposed prediction model showed good discrimination and calibration. This prediction model is based on easily obtainable information and can be used as an adjunct for patient selection for further molecular work-up in a tertiary hospital setting.

Contrast enhanced magnetic resonance imaging-based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study

Background

The aim of this study was to develop and validate a radiomics nomogram for preoperative prediction of Ki-67 proliferative index (Ki-67 PI) expression in patients with meningioma.

Methods

A total of 280 patients from 2 independent hospital centers were enrolled. Patients from center I were randomly divided into a training cohort of 168 patients and a test cohort of 72 patients, and 40 patients from center II served as an external validation cohort. Interoperator reproducibility test, Z-score standardization, analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were used to select radiomics features, which were extracted from contrast-enhanced T1-weighted imaging (CE-T1WI) imaging. The radiomics signature for predicting Ki-67 PI expression was developed and validated using 4 classifiers including logistic regression (LR), decision tree (DT), support vector machine (SVM), and adaptive boost (AdaBoost). Finally, combined radiological characteristics with radiomics signature were used to establish the nomogram to predict the risk of high Ki-67 PI expression in patients with meningioma.

Results

Fourteen radiomics features were used to construct the radiomics signature. The radiomics nomogram that incorporated the radiomics signature and radiological characteristics showed excellent discrimination in the training, test, and validation cohorts with areas under the curve of 0.817 (95% CI: 0.753-0.881), 0.822 (95% CI: 0.727-0.916), and 0.845 (95% CI: 0.708-0.982), respectively. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.

Conclusions

The proposed contrast enhanced magnetic resonance imaging (MRI)-based radiomics nomogram could be an effective tool to predict the risk of Ki-67 high expression in patients with meningioma.

A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma

PURPOSE

This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based deep learning radiomics model (DLRM) in differentiating low- and high-grade meningiomas.

METHODS

A total of 132 patients with pathologically confirmed meningiomas were consecutively enrolled (105 in the training cohort and 27 in the test cohort). Radiomics features and deep learning features were extracted from T1 weighted images (T1WI) (both axial and sagittal) and the maximum slice of the axial tumor lesion, respectively. Then, the synthetic minority oversampling technique (SMOTE) was utilized to balance the sample numbers. The optimal discriminative features were selected for model building. LightGBM algorithm was used to develop DLRM by a combination of radiomics features and deep learning features. For comparison, a radiomics model (RM) and a deep learning model (DLM) were constructed using a similar method as well. Differentiating efficacy was determined by using the receiver operating characteristic (ROC) analysis.

RESULTS

A total of 15 features were selected to construct the DLRM with SMOTE, which showed good discrimination performance in both the training and test cohorts. The DLRM outperformed RM and DLM for differentiating low- and high-grade meningiomas (training AUC: 0.988 vs. 0.980 vs. 0.892; test AUC: 0.935 vs. 0.918 vs. 0.718). The accuracy, sensitivity, and specificity of the DLRM with SMOTE were 0.926, 0.900, and 0.924 in the test cohort, respectively.

CONCLUSION

The DLRM with SMOTE based on enhanced T1WI images has favorable performance for noninvasively individualized prediction of meningioma grades, which exhibited favorable clinical usefulness superior over the radiomics features.

Prognostic significance of brain invasion in meningiomas: systematic review and meta-analysis

The WHO 2016 classification introduced brain invasion as a standalone criterion for grade II meningioma (GIIM). We systematically reviewed studies published after 2000 and performed a PRISMA-compliant meta-analysis of the hazard ratios (HRs) for progression-free survival (PFS) between brain-invasive and noninvasive meningiomas. In five studies that included both benign and higher-grade meningiomas, brain invasion was a significant risk factor for recurrence (HR = 2.45, p = 0.0004). However, in 3 studies comparing "brain-invasive meningioma with otherwise benign histology (BIOB)" with grade I meningioma, brain invasion was not a significant predictor of PFS (HR = 1.49, p = 0.23). Among GIIM per the WHO 2000 criteria, brain invasion was a significant predictor of shorter PFS than noninvasive GIIM (HR = 3.40, p = 0.001) but not per the WHO 2016 criteria (HR 1.13, p = 0.54), as the latter includes BIOB. Meta-regression analysis of seven studies of grade II meningioma showed that more frequent BIOB was associated with lower HRs (p < 0.0001). Hence, there is no rationale for brain invasion as a standalone criterion for grade II meningioma, although almost all studies were retrospective and exhibited highly heterogeneous HRs due to differences in brain-tumor interface data availability.

Infarction with associated pseudosarcomatous changes mimics anaplasia in otherwise grade I meningiomas

We describe a morphologically distinct pattern of tumor infarction and associated sarcoma-like changes, mimicking focal anaplasia, in otherwise WHO grade I meningiomas. The described cases (n = 9) all demonstrated a discrete spindle-cell (pseudosarcomatous) component with brisk mitotic activity (12-14 mitoses/10 HPF), elevated Ki-67 (mean 75.5 ± 25.0%, quantified), absence of PR, SSTR2A, or EMA expression, and potential SMA expression (50%). Despite these high-grade features, all nine patients remained free of progression or recurrence post resection (follow-up mean: 49.8 months). In contrast, among a comparison (control) cohort of consecutive WHO grade II and III meningiomas (n = 16), as expected, progression rate was high (68.8%, P = 0.002, Fisher's exact, average time to progression = 25 months, follow-up mean: 39.8 months). While necrosis was a frequent feature among atypical/anaplastic meningiomas (12/16, 75%), and elevated mitoses and proliferative index were present consistent with histologic grade, a well-defined zonal pattern with pseudosarcomatous component was not present among these tumors. DNA methylation-based analysis readily distinguished meningiomas by copy number profiles and DNA-based methylation meningioma random forest classification analysis (meningioma v2.4 classifier developed at University of Heidelberg); all pseudosarcomatous cases analyzed (4/9) matched with high level calibrated classifier score to "MC benign-1", with isolated loss of chromosome 22q identified as the sole copy number alteration. In contrast, multiple chromosomal losses were detected among the comparison cohort and classifier results demonstrated good concordance with histologic grade. Our findings suggest that pseudosarcomatous alterations represent reactive changes to central meningioma infarction, rather than focal anaplasia, and further support the use of DNA methylation-based analysis as a useful adjunct for predicting meningioma behavior. These indolent tumors should be distinguished from their atypical and anaplastic counterparts.