OBJECTIVE: In meningiomas, the Simpson grading system is applied to estimate the risk of postoperative recurrence, but might suffer from bias and limited overview of the resection cavity. In contrast, the value of the postoperative tumor volume as an objective predictor of recurrence is largely unexplored. The objective of this study was to compare the predictive value of residual tumor volume with the intraoperatively assessed extent of resection (EOR). METHODS: The Simpson grade was determined in 939 patients after surgery for initially diagnosed intracranial meningioma. Tumor volume was measured on initial postoperative MRI within 6 months after surgery. Correlation between both variables and recurrence was compared using a tree-structured Cox regression model. RESULTS: Recurrence correlated with Simpson grading (p = 0.003). In 423 patients (45%) with available imaging, residual tumor volume covered a broad range (0–78.5 cm ³ ). MRI revealed tumor remnants in 8% after gross-total resection (Simpson grade I–III, range 0.12–33.5 cm ³ ) with a Cohen’s kappa coefficient of 0.7153. Postoperative tumor volume was correlated with recurrence in univariate analysis (HR 1.05 per cm ³ , 95% CI 1.02–1.08 per cm ³ , p < 0.001). A tree-structured Cox regression model revealed any postoperative tumor volume > 0 cm ³ as a critical cutoff value for the prediction of relapse. Multivariate analysis confirmed the postoperative tumor volume (HR 1.05, p < 0.001) but not the Simpson grading (p = 0.398) as a predictor for recurrence. CONCLUSIONS: EOR according to Simpson grading was overrated in 8% of tumors compared to postoperative imaging. Because the predictive value of postoperative imaging is superior to the Simpson grade, any residual tumor should be carefully considered during postoperative care of meningioma patients.

Angiogenesis is a key feature during oncogenesis and remains a potential target of antiangiogenic therapy. While commonly described in high-grade lesions, vascularization and its correlation with prognosis in grade I meningiomas is largely unexplored. In the histological classification, not only the number but also the composition of blood vessels seems to be important. Therefore, tumor vessel density and fibrosis were correlated with clinical and imaging variables and prognosis in 295 patients with intracranial grade I meningioma. Expression of pro-angiogenic proteins within the meningiomas was investigated by proteome analyses and further validated by immunohistochemical staining. Fibrotic tumor vessels (FTV) were detected in 48% of all tumors and strongly correlated with vessel density, but not with the histopathological tumor subtype. Occurrence of FTV was correlated with a 2-fold increased risk of recurrence in both univariate and multivariate analyses. Explorative proteome analyses revealed upregulation of VEGF (vascular endothelial growth factor), PlGF (placental growth factor), and IGFBP-3 (insulin-like growth factor-binding protein-3) in tumors displaying FTV. Immunohistochemical analyses confirmed strong correlations between tumor vessel fibrosis and expression of VEGF, PlGF, and IGFBP-3. Presence of FTV was strongly associated with disruption of the arachnoid layer on preoperative MRI in univariate and multivariate analyses. In summary, the occurrence of fibrotic tumor vessels in grade I meningiomas is strongly associated with vessel density, disruption of the arachnoid layer, expression of VEGF, PlGF, IGFBP-3 and tumor recurrence.

Background: The usefulness of 5-ALA-mediated fluorescence-guided resection (FGR) in meningiomas is controversial, and information on the molecular background of fluorescence is sparse. Methods: Specimens obtained during 44 FGRs of intracranial meningiomas were analyzed for the presence of tumor tissue and fluorescence. Protein/mRNA expression of key transmembrane transporters/enzymes involved in PpIX metabolism (ABCB6, ABCG2, FECH, CPOX) were investigated using immunohistochemistry/qPCR. Results: Intraoperative fluorescence was observed in 70 of 111 specimens (63%). No correlation was found between fluorescence and the WHO grade (p = 0.403). FGR enabled the identification of neoplastic tissue (sensitivity 84%, specificity 67%, positive and negative predictive value of 86% and 63%, respectively, AUC: 0.75, p < 0.001), and was improved in subgroup analyses excluding dura specimens (86%, 88%, 96%, 63% and 0.87, respectively; p < 0.001). No correlation was found between cortical fluorescence and tumor invasion (p = 0.351). Protein expression of ABCB6, ABCG2, FECH and CPOX was found in meningioma tissue and was correlated with fluorescence (p < 0.05, each), whereas this was not confirmed for mRNA expression. Aberrant expression was observed in the CNS. Conclusion: FGR enables the intraoperative identification of meningioma tissue with limitations concerning dura invasion and due to ectopic expression in the CNS. ABCB6, ABCG2, FECH and CPOX are expressed in meningioma tissue and are related to fluorescence.

Background and Objectives: Resection of dumbbell tumors can be challenging, and facet joint sparing approaches carry the risk of incomplete resection. In contrast, additional facetectomy may allow better surgical exposure at the cost of spinal stability. The aim of this study is to compare facet-sparing and facetectomy approaches for the treatment of lumbar spine dumbbell tumors. Materials and Methods: In a cohort study setting, we analyzed Eden type 2 and 3 tumors operated in our department. Conventional facet-sparing microsurgical or facetectomy approaches with minimally invasive fusions were performed according to individual surgeons’ preference. Primary outcomes were extent of resection and tumor progression over time. Secondary outcomes were perioperative adverse events. Results: Nineteen patients were included. Nine patients were operated on using a facet-sparing technique. Ten patients underwent facetectomy and fusion. While only one patient (11%) in the facet-sparing group experienced gross total resection (GTR), this was achieved for all patients in the facetectomy group (100%). The relative risk (RR) for incomplete resection in the facet-sparing cohort was 18.7 (95% CI 1.23–284.047; p = 0.035). In addition, time to progression was shorter in the facet-sparing cohort (p = 0.022) and all patients with a residual tumor underwent a second resection after a median follow-up time of 42 months (IQR 25–66). Conclusions: Minimally invasive resection of lumbar Eden type 2 and 3 dumbbell tumors including facetectomy in combination with instrumentation appears to be safe and superior to the facet-sparing approach in terms of local tumor control.

Purpose: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. Methods: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. Results: A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen’s Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen’s Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. Conclusions: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.