Risk Factors for High-Grade Meningioma in Brain and Spine: Systematic Review and Meta-analysis

Clinical, molecular, and genetic features of spinal meningiomas

Spinal meningiomas comprise 25%-46% of all primary spinal tumors. While the majority are benign and slow-growing, when left untreated, they can result in significant neurological decline. Emerging clinical, imaging, and molecular data have begun to reveal spinal meningiomas as distinct tumor subtypes compared to their intracranial counterparts. Moreover, recent studies indicate molecular and genetic subtype heterogeneity of spinal meningiomas both within and across the classically defined WHO grades. In the current review, we focus on recent advances highlighting the epidemiological, pathological, molecular/genetic, and clinical characteristics of spinal meningiomas. Furthermore, we explore patient and tumor-specific factors that predict prognosis and postoperative outcomes. We highlight areas that require further investigation, specifically efforts aimed at linking unique molecular, genetic, and imaging characteristics to distinct clinical presentations to better predict and manage patient outcomes.

Variability in the Arterial Supply of Intracranial Meningiomas: An Anatomic Study

BACKGROUND AND OBJECTIVES

Intracranial meningiomas are a diverse group of tumors, which vary by grade, genetic composition, location, and vasculature. Expanding the understanding of the supply of skull base (SBMs) and non-skull base meningiomas (NSBMs) will serve to further inform resection strategies. We sought to delineate the vascular supply of a series of intracranial meningiomas by tumor location.

METHODS

A retrospective study of intracranial meningiomas that were studied using preoperative digital subtraction angiograms before surgical resection at a tertiary referral center was performed. Patient, tumor, radiologic, and treatment data were collected, and regression models were developed.

RESULTS

One hundred sixty-five patients met inclusion criteria. The mean age was 57.1 years (SD: 12.6). The mean tumor diameter was 4.9 cm (SD: 1.5). One hundred twenty-six were World Health Organization Grade I, 37 Grade II, and 2 Grade III. Arterial feeders were tabulated by Al-Mefty's anatomic designations. SBMs were more likely to derive arterial supply from the anterior circulation, whereas NSBMs were supplied by external carotid branches. NSBMs were larger (5.61 cm vs 4.45 cm, P = <.001), were more often presented with seizure (20% vs 8%, P = .03), were higher grade ( P = <.001) had more frequent peritumoral brain edema (84.6% vs 66%, P = .04), and had more bilateral feeders (47.7% vs 28%, P = .01) compared with SBMs. More arterial feeders were significantly associated with lower tumor grade ( P = .023, OR = 0.59). Higher tumor grade (Grade II/III) was associated with fewer arterial feeders ( P = .017, RR = 0.74).

CONCLUSION

Meningioma location is associated with specific vascular supply patterns, grade, and patient outcomes. This information suggests that grade I tumors, especially larger tumors, are more likely to have diverse vascular supply patterns, including internal carotid branches. This study may inform preoperative embolization and surgical considerations, particularly for large skull base tumors.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning

OBJECTIVES

To establish a robust interpretable multiparametric deep learning (DL) model for automatic noninvasive grading of meningiomas along with segmentation.

METHODS

In total, 257 patients with pathologically confirmed meningiomas (162 low-grade, 95 high-grade) who underwent a preoperative brain MRI, including T2-weighted (T2) and contrast-enhanced T1-weighted images (T1C), were included in the institutional training set. A two-stage DL grading model was constructed for segmentation and classification based on multiparametric three-dimensional U-net and ResNet. The models were validated in the external validation set consisting of 61 patients with meningiomas (46 low-grade, 15 high-grade). Relevance-weighted Class Activation Mapping (RCAM) method was used to interpret the DL features contributing to the prediction of the DL grading model.

RESULTS

On external validation, the combined T1C and T2 model showed a Dice coefficient of 0.910 in segmentation and the highest performance for meningioma grading compared to the T2 or T1C only models, with an area under the curve (AUC) of 0.770 (95% confidence interval: 0.644-0.895) and accuracy, sensitivity, and specificity of 72.1%, 73.3%, and 71.7%, respectively. The AUC and accuracy of the combined DL grading model were higher than those of the human readers (AUCs of 0.675-0.690 and accuracies of 65.6-68.9%, respectively). The RCAM of the DL grading model showed activated maps at the surface regions of meningiomas indicating that the model recognized the features at the tumor margin for grading.

CONCLUSIONS

An interpretable multiparametric DL model combining T1C and T2 can enable fully automatic grading of meningiomas along with segmentation.

KEY POINTS

• The multiparametric DL model showed robustness in grading and segmentation on external validation. • The diagnostic performance of the combined DL grading model was higher than that of the human readers. • The RCAM interpreted that DL grading model recognized the meaningful features at the tumor margin for grading.

Prevalence and Influence of Frailty on Hospital Outcomes After Surgical Resection of Spinal Meningiomas

OBJECTIVE

Frailty has been shown to affect patient outcomes after medical and surgical interventions. The Hospital Frailty Risk Score (HFRS) is a growing metric used to assess patient frailty using International Classification of Diseases, Tenth Revision codes. The goal of this study was to investigate the impact of frailty, assessed by HFRS, on health care resource utilization and outcomes in patients undergoing surgery for spinal meningiomas.

METHODS

A retrospective cohort study was performed using the 2016-2019 National Inpatient Sample database. Adult patients with benign or malignant spinal meningiomas, identified using International Classification of Diseases, Tenth Revision, Clinical Modification codes, were stratified by HFRS: low frailty (HFRS <5) and intermediate-high frailty (HFRS ≥5). Patient demographics, hospital characteristics, comorbidities, procedural variables, adverse events, length of stay (LOS), discharge disposition, and cost of admission were assessed. Multivariate regression analysis was used to identify predictors of increased LOS, discharge disposition, and cost.

RESULTS

Of the 3345 patients, 530 (15.8%) had intermediate-high frailty. The intermediate-high cohort was significantly older (P < 0.001). More patients in the intermediate-high cohort had ≥3 comorbidities (P < 0.001). In addition, a greater proportion of patients in the intermediate-high cohort experienced ≥1 perioperative adverse events (P < 0.001). Intermediate-high patients experienced greater mean LOS (P < 0.001) and accrued greater costs (P < 0.001). A greater proportion of intermediate-high patients had nonroutine discharges (P < 0.001). On multivariate analysis, increased HFRS (≥5) was independently associated with extended LOS (adjusted odds ratio [aOR], 3.04; P < 0.001), nonroutine discharge (aOR, 1.98; P = 0.006), and increased costs (aOR, 2.39; P = 0.004).

CONCLUSIONS

Frailty may be associated with increased health care resource utilization in patients undergoing surgery for spinal meningiomas.

Imaging score for differentiation of meningioma grade

PURPOSE

We sought to establish a comprehensive imaging score indicating the likelihood of higher WHO grade meningiomas pre-operatively.

METHODS

All surgical intracranial meningioma patients at our institution between 2014 and 2018 underwent retrospective chart review. Preoperative MRI sequences were reviewed, and imaging features were included in the score based on statistical and clinical significance. Point values for each significant feature were assigned based on the beta coefficients obtained from multivariate analysis. The imaging score was calculated by adding up the points, for a total score of 0 to 5. The predictive ability of the score to identify higher-grade meningiomas was evaluated.

RESULTS

Ninety patients, 50% of whom had a postoperative diagnosis of WHO grade II meningioma, were included. The mean age for the population was 59.9 years and 70% were female. Tumor volume ≥ 36.0 cc was assigned 2 points, presence of irregular tumor borders was assigned 2 points, and presence of peritumoral edema was assigned 1 point. The probability of having a WHO grade II meningioma was 0% with a score of 0, 25.0% with a score of 1, 38.5% with a score of 2, 65.4% with a score of 3, and 83.3% with a score of 4 or greater. A threshold of ≥ 3 points achieved a recall of 0.80, precision of 0.73, F1-score of 0.77, accuracy of 0.76, and AUC of 0.82.

CONCLUSION

The proposed imaging scoring system had good predictive capability for WHO grade II meningiomas with good discrimination and calibration. External validation is needed.

Prognosis and histology of sporadic synchronous and metachronous meningiomas and comparative analyses with singular lesions

Synchronous or metachronous growth of multiple tumors (≥ 2) is found in up to 20% of meningioma patients. However, biological as well as histological features and prognosis are largely unexplored. Clinical and histological characteristics were retrospectively investigated in 95 patients harboring 226 multiple meningiomas (MMs) and compared with 135 cases of singular meningiomas (SM) using uni- and multivariate analyses. In MM, tumors occurred synchronously and metachronously in 62% and 38%, respectively. WHO grade was intra-individually constant in all but two MMs, and histological subtype varied in 13% of grade 1 tumors. MM occurred more commonly in convexity/parasagittal locations, while SM were more frequent at the skull base (p < .001). In univariate analyses, gross total resection (p = .014) and high-grade histology in MM were associated with a prolonged time to progression (p < .001). Most clinical characteristics and rates of high-grade histology were similar in both groups (p ≥ .05, each). Multivariate analyses showed synchronous/metachronous meningioma growth (HR 4.50, 95% CI 2.26-8.96; p < .001) as an independent predictor for progression. Compared to SM, risk of progression was similar in cases with two (HR 1.56, 95% CI .76-3.19; p = .224), but exponentially raised in patients with 3-4 (HR 3.25, 1.22-1.62; p = .018) and ≥ 5 tumors (HR 13.80, 4.06-46.96; p < .001). Clinical and histological characteristics and risk factors for progression do not relevantly differ between SM and MM. Although largely constant, histology and WHO grade occasionally intra-individually vary in MM. A distinctly higher risk of disease progression in MM as compared to SM might reflect different underlying molecular alterations.

The applicability of established clinical and histopathological risk factors for tumor recurrence during long-term postoperative care in meningioma patients

Risk factors to predict late-onset tumor recurrence in meningioma patients are urgently needed to schedule control intervals during long-term follow-up. We therefore analyzed the value of established risk factors for postoperative meningioma recurrence for the prediction of long-term prognosis. Correlations of clinical and histopathological variables with tumor relapse after 3, 5, and 10 years following microsurgery were analyzed in uni- and multivariate analyses, and compared to findings in the entire cohort. In the entire cohort (N = 1218), skull base location (HR: 1.51, 95%CI 1.05–2.16; p = .026), Simpson ≥ IV resections (HR: 2.41, 95%CI 1.52–3.84; p < .001), high-grade histology (HR: 3.70, 95%CI 2.50–5.47; p < .001), and male gender (HR: 1.46, 95%CI 1.01–2.11; p = .042) were independent risk factors for recurrence. Skull base location (HR: 1.92, 95%CI 1.17–3.17; p = .010 and HR: 2.02, 95%CI 1.04–3.95; p = .038) and high-grade histology (HR: 1.87, 95%CI 1.04–3.38; p = .038 and HR: 2.29, 95%CI 1.07–4.01; p = .034) but not subtotal resection (HR: 1.53, 95%CI .68–3.45; p = .303 and HR: 1.75, 95%CI .52–5.96; p = .369) remained correlated with recurrence after a recurrence-free follow-up of ≥ 3 and ≥ 5 years, respectively. Postoperative tumor volume was related with recurrence in general (p < .001) but not beyond a follow-up of ≥ 3 years (p > .05). In 147 patients with a follow-up of ≥ 10 years, ten recurrences occurred and were not correlated with any of the analyzed variables. Skull base tumor location and high-grade histology but not the extent of resection should be considered when scheduling the long-term follow-up after meningioma surgery. Recurrences ≥ 10 years after surgery are rare, and predictors are lacking.