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Caroline Apra, Karima Mokhtari, Philippe Cornu, Matthieu Peyre and Michel Kalamarides

OBJECTIVE

Meningeal solitary fibrous tumors/hemangiopericytomas (MSFTs/HPCs) are rare intracranial tumors resembling meningiomas. Their classification was redefined in 2016 by the World Health Organization (WHO) as benign Grade I fibrohyaline type, intermediate Grade II hypercellular type, and malignant highly mitotic Grade III. This grouping is based on common histological features and identification of a common NAB2-STAT6 fusion.

METHODS

The authors retrospectively identified 49 cases of MSFT/HPC. Clinical data were obtained from the medical records, and all cases were analyzed according to this new 2016 WHO grading classification in order to identify malignant transformations.

RESULTS

Recurrent surgery was performed in 18 (37%) of 49 patients. Malignant progression was identified in 5 (28%) of these 18 cases, with 3 Grade I and 2 Grade II tumors progressing to Grade III, 3–13 years after the initial surgery. Of 31 Grade III tumors treated in this case series, 16% (5/31) were proved to be malignant progressions from lower-grade tumors.

CONCLUSIONS

Low-grade MSFTs/HPCs can transform into higher grades as shown in this first report of such progression. This is a decisive argument in favor of a common identity for MSFT and meningeal HPC. High-grade MSFTs/HPCs tend to recur more often and be associated with reduced overall survival. Malignant progression could be one mechanism explaining some recurrences or metastases, and justifying long-term follow-up, even for patients with Grade I tumors.

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Samiya Abi Jaoude, Matthieu Peyre, Vincent Degos, Stéphane Goutagny, Béatrice Parfait and Michel Kalamarides

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.

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Charles A. Valery, Matthieu Faillot, Ioannis Lamproglou, Jean-Louis Golmard, Catherine Jenny, Matthieu Peyre, Karima Mokhtari, Jean-Jacques Mazeron, Philippe Cornu and Michel Kalamarides

OBJECTIVE

Grade II meningiomas, which currently account for 25% of all meningiomas, are subject to multiple recurrences throughout the course of the disease and represent a challenge for the neurosurgeon. Radiosurgery is increasingly performed for the treatment of Grade II meningiomas and is quite efficient in controlling relapses locally at the site of the lesion, but it cannot prevent margin relapses. The aim of this retrospective study was to analyze the technical parameters involved in producing marginal relapses and to optimize loco-marginal control to improve therapeutic strategy.

METHODS

Eighteen patients presenting 58 lesions were treated by Gamma Knife radiosurgery (GKRS) between 2010 and 2015 in Hopital de la Pitié-Salpêtrière. The median patient age was 68 years (25%−75% interval: 61–72 years), and the sex ratio (M/F) was 13:5. The median delay between surgery and first GKRS was 3 years. Patients were classified as having Grade II meningioma using World Health Organization (WHO) 2007 criteria. The tumor growth rate was computed by comparing 2 volumetric measurements before treatment. After GKRS, iterative MRI, performed every 6 months, detected a relapse if tumor volume increased by more than 20%. Patterns of relapse were defined as being local, marginal, or distal. Survival curves were estimated using the Kaplan-Meier method, and the relationship between criterion and potential risk factors was tested by the log-rank test and univariable Cox model.

RESULTS

The median follow-up was 36 months (range 8–57 months). During this period, 3 patients presented with a local relapse, 5 patients with a marginal relapse, and 7 patients with a distal relapse. Crude local control was 84.5%. The local control actuarial rate was 89% at 1 year and 71% at 3 years. The marginal control actuarial rate was 81% at 1 year and 74% at 2 years. The distal control actuarial rate was 100% at 1 year, 81% at 2 years, and 53% at 3 years. Median distal control was 38 months. Progression-free survival (PFS) was 71% at 1 year, 36% at 2 years, and 23% at 3 years. Median PFS was 18 months. Lesions treated with a minimum radiation dose of ≤ 12 Gy had significantly more local relapses than those treated with a dose > 12 Gy (p = 0.04) in univariate analysis.

Marginal control was significantly influenced by tumor growth rate, with a lower growth rate being highly associated with improved marginal control (p = 0.002). There was a trend toward a relationship between dose and marginal control, but it was not significant (p = 0.09). PFS was significantly associated with delay between first surgery and GKRS (p = 0.03). The authors noticed few complications with no sequelae.

CONCLUSIONS

In order to optimize loco-marginal control, radiosurgical treatment should require a minimum dose of > 12 Gy and an extended target volume along the dural insertion. Ideally, these parameters should correspond to the aggressiveness of the lesion, based on genetic features of the tumor.

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Mark W. Youngblood, Daniel Duran, Julio D. Montejo, Chang Li, Sacit Bulent Omay, Koray Özduman, Amar H. Sheth, Amy Y. Zhao, Evgeniya Tyrtova, Danielle F. Miyagishima, Elena I. Fomchenko, Christopher S. Hong, Victoria E. Clark, Maximilien Riche, Matthieu Peyre, Julien Boetto, Sadaf Sohrabi, Sarah Koljaka, Jacob F. Baranoski, James Knight, Hongda Zhu, M. Necmettin Pamir, Timuçin Avşar, Türker Kilic, Johannes Schramm, Marco Timmer, Roland Goldbrunner, Ye Gong, Yaşar Bayri, Nduka Amankulor, Ronald L. Hamilton, Kaya Bilguvar, Irina Tikhonova, Patrick R. Tomak, Anita Huttner, Matthias Simon, Boris Krischek, Michel Kalamarides, E. Zeynep Erson-Omay, Jennifer Moliterno and Murat Günel

OBJECTIVE

Recent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts.

METHODS

Targeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher’s exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features.

RESULTS

Genomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among “mutation unknown” samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor’s underlying driver mutation.

CONCLUSIONS

Using a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.