Growth prediction in asymptomatic meningiomas: the utility of the AIMSS score

Growth dynamics of untreated meningiomas

Background

Knowledge about meningioma growth characteristics is needed for developing biologically rational follow-up routines. In this study of untreated meningiomas followed with repeated magnetic resonance imaging (MRI) scans, we studied growth dynamics and explored potential factors associated with tumor growth.

Methods

In a single-center cohort study, we included 235 adult patients with radiologically suspected intracranial meningioma and at least 3 MRI scans during follow-up. Tumors were segmented using an automatic algorithm from contrast-enhanced T1 series, and, if needed, manually corrected. Potential meningioma growth curves were statistically compared: linear, exponential, linear radial, or Gompertzian. Factors associated with growth were explored.

Results

In 235 patients, 1394 MRI scans were carried out in the median 5-year observational period. Of the models tested, a Gompertzian growth curve best described growth dynamics of meningiomas on group level. 59% of the tumors grew, 27% remained stable, and 14% shrunk. Only 13 patients (5%) underwent surgery during the observational period and were excluded after surgery. Tumor size at the time of diagnosis, multifocality, and length of follow-up were associated with tumor growth, whereas age, sex, presence of peritumoral edema, and hyperintense T2-signal were not significant factors.

Conclusions

Untreated meningiomas follow a Gompertzian growth curve, indicating that increasing and potentially doubling subsequent follow-up intervals between MRIs seems biologically reasonable, instead of fixed time intervals. Tumor size at diagnosis is the strongest predictor of future growth, indicating a potential for longer follow-up intervals for smaller tumors. Although most untreated meningiomas grow, few require surgery.

Incidental Meningiomas: Potential Predictors of Growth and Current State of Management

The rise in availability of neuroimaging has led to an increase in incidentally discovered meningiomas. These tumors are typically asymptomatic and tend to display slow growth. Treatment options include observation with serial monitoring, radiation, and surgery. Although optimal management is unclear, clinicians recommend a conservative approach, which preserves quality of life and limits unnecessary intervention. Several risk factors have been investigated for their potential utility in the development of prognostic models for risk assessment. Herein, the authors review the current literature on incidental meningiomas, focusing their discussion on potential predictive factors for tumor growth and appropriate management practices.

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning

BACKGROUND

Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice.

PURPOSE

To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning.

STUDY TYPE

Retrospective.

POPULATION

A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men.

FIELD STRENGTH/SEQUENCE

The 1.0 T, 1.5 T, and 3.0 T; three-dimensional, T₁ -weighted gradient-echo imaging with contrast enhancement.

ASSESSMENT

The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth.

STATISTICAL TESTS

According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis.

RESULTS

A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1 cm³ were 0.769 and 0.780 with the IVS and EVS, respectively.

DATA CONCLUSION

A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Clinical Presentation and Prognosis

Over the past three decades, the care for patients with meningioma has steadily improved as a result of a better understanding of the natural history, molecular biology, and classification of these tumors. Surgical frameworks for management have been established and validated with more options for adjuvant and salvage treatment available for patients with residual or recurrent disease. Overall these advances have improved clinical outcomes and prognosis.Alongside the improved clinical management has come an increase in biological understanding of these tumors. The number of publications within the field of meningioma research continues to expand and biological studies identifying molecular factors at the cytogenic and genomic level offer exciting potential for more personalized management strategies. As survival and understanding have increased, treatment outcomes are moving from traditional metrics, which describe the morbidity and mortality to more patient-centered measures. The subjective experiences of patients with meningioma are gaining interest among clinical researchers and it is recognized that even supposedly mild symptoms arising from meningioma can have a significant effect on a patient's quality of life.This chapter reviews the varied clinical presentations of meningioma, which in the modern era of widespread brain imaging must include a discussion of incidental meningioma. The second part examines prognosis and the clinical, pathological, and molecular factors that can be used to predict outcomes.

Comparison of Active Surveillance to Stereotactic Radiosurgery for the Management of Patients with an Incidental Frontobasal Meningioma-A Sub-Analysis of the IMPASSE Study

Meningioma is a common incidental finding, and clinical course varies based on anatomical location. The aim of this sub-analysis of the IMPASSE study was to compare the outcomes of patients with an incidental frontobasal meningioma who underwent active surveillance to those who underwent upfront stereotactic radiosurgery (SRS). Data were retrospectively collected from 14 centres. The active surveillance (n = 28) and SRS (n = 84) cohorts were compared unmatched and matched for age, sex, and duration of follow-up (n = 25 each). The study endpoints included tumor progression, new symptom development, and need for further intervention. Tumor progression occurred in 52.0% and 0% of the matched active surveillance and SRS cohorts, respectively (p < 0.001). Five patients (6.0%) treated with SRS developed treatment related symptoms compared to none in the active monitoring cohort (p = 0.329). No patients in the matched cohorts developed symptoms attributable to treatment. Three patients managed with active surveillance (10.7%, unmatched; 12.0%, matched) underwent an intervention for tumor growth with no persistent side effects after treatment. No patients subject to SRS underwent further treatment. Active monitoring and SRS confer a similarly low risk of symptom development. Upfront treatment with SRS improves imaging-defined tumor control. Active surveillance and SRS are acceptable treatment options for incidental frontobasal meningioma.

Validation of a scoring system to evaluate the risk of rapid growth of intracranial meningiomas in neurofibromatosis type 2 patients

OBJECTIVE

Intracranial meningiomas occur in about half of neurofibromatosis type 2 (NF2) patients and are very frequently multiple. Thus, estimating individual meningiomas' growth rates is of great interest to tailor therapeutic interventions. The Asan Intracranial Meningioma Scoring System (AIMSS) has recently been published to estimate the risk of tumor growth in sporadic meningiomas. The current study aimed to determine predictors of rapid meningioma growth in NF2 patients and to evaluate the AIMSS score in a specific NF2 cohort.

METHODS

The authors performed a retrospective analysis of 92 NF2 patients with 358 measured intracranial meningiomas that had been observed prospectively between 2012 and 2018. Tumor volumes were measured at diagnosis and at each follow-up visit. The growth rates were determined and evaluated with respect to the clinicoradiological parameters. Predictors of rapid tumor growth (defined as growth ≥ 2 cm3/yr) were analyzed using univariate followed by multivariate logistic regression to build a dedicated predicting model. Receiver operating characteristic (ROC) curves to predict the risk of rapid tumor growth with the AIMSS versus the authors' multivariate model were compared.

RESULTS

Sixty tumors (16.76%) showed rapid growth. After multivariate analysis, a larger tumor volume at diagnosis (p < 0.0001), presence of peritumoral edema (p = 0.022), absence of calcifications (p < 0.0001), and hyperintense or isointense signal on T2-weighted MRI (p < 0.005) were statistically significantly associated with rapid tumor growth. It is particularly notable that the genetic severity score did not seem to influence the growth rate of NF2 meningiomas. In comparison with the AIMSS, the authors' multivariate model's prediction did not show a statistically significant difference (area under the curve [AUC] 0.82 [95% CI 0.76-0.88] for the AIMSS vs AUC 0.86 [95% CI 0.81-0.91] for the authors' model, p = 0.1).

CONCLUSIONS

The AIMSS score is valid in the authors' cohort of NF2-related meningiomas. It adequately predicted risk of rapid meningioma growth and could aid in decision-making in NF2 patients.

Spatial mechanistic modeling for prediction of the growth of asymptomatic meningiomas

BACKGROUND AND OBJECTIVE

Mathematical modeling of tumor growth draws interest from the medical community as they have the potential to improve patients' care and the use of public health resources. The main objectives of this work are to model the growth of meningiomas - slow-growing benign tumors requiring extended imaging follow-up - and to predict tumor volume and shape at a later desired time using only two times examinations.

METHODS

We develop two variants of a 3D partial differential system of equations (PDE) which yield after a spatial integration systems of ordinary differential equations (ODE) that relate tumor volume with time. Estimation of models parameters is a crucial step to obtain a personalized model for a patient that can be used for descriptive or predictive purposes. As PDE and ODE systems share the same parameters, they are both estimated by fitting the ODE systems to the tumor volumes obtained from MRI examinations acquired at different times. A population approach allows to compensate for sparse sampling times and measurement uncertainties by constraining the variability of the parameters in the population.

RESULTS

Description capabilities of the models are investigated in 39 patients with benign asymptomatic meningiomas who had had at least three surveillance MRI examinations. The two models can fit to the data accurately and more realistically than a naive linear regression. Prediction performances are validated for 33 patients using a population approach. Mean relative errors in volume predictions are less than 10% with ODE systems versus 12.5% with the naive linear model using only two times examinations. Concerning the shape, the mean Sørensen-Dice coefficients are 85% with the PDE systems in a subset of 10 representative patients.

CONCLUSIONS

Our strategy - based on personalization of mathematical model - provides a good insight on meningioma growth and may help decide whether to extend the follow-up or to treat the tumor.

Modern day imaging of meningiomas

Meningiomas are the most common primary tumors of the central nervous system and as such they are often encountered at neuroimaging. Fortunately, meningiomas are readily diagnosed with anatomic computed tomography and magnetic resonance imaging. While conventional imaging is the mainstay for initial diagnosis and delineating tumor for treatment planning and posttreatment follow-up, the last couple of decades have given rise to advanced physiologic and metabolic imaging techniques that serve as powerful tools in the management of meningioma. These modern approaches are allowing imaging to expand its utility to include extraction of biologic and potentially prognostic information that will ultimately improve care for meningioma patients.