• Michael Karsy, Brian Burnett, Antonio Di Ieva, Michael D Cusimano, and Randy L Jensen.
• 1Department of Neurosurgery and.
• J. Neurosurg. 2018 Mar 1; 128 (3): 657-666.

AbstractOBJECTIVE Quantitative assessment of tumor microvascularity has the potential to improve prognostication, advance understanding of tumor biology, and help narrow potential molecular therapies. While the role of tumor microvascularity has been widely studied in meningiomas, this study examines both the role of automated measurements and the impact on surgical outcome. METHODS Two hundred seven patients with Grade I meningiomas underwent surgery between 1996 and 2011. Tissue samples from each patient were retrospectively evaluated for histopathological measures of microvascularity, including staining for von Willebrand factor (vWF), CD31, CD105, hypoxia-inducible factor 1 (HIF-1), vascular endothelial growth factor, glucose transporter 1, and carbonic anhydrase IX. Manual methods of assessing microvascularity were supplemented by a computational analysis of the microvascular patterns by means of fractal analysis. MIB-1 proliferation staining was also performed on the same tumors. These measures were compared with various patient characteristics, tumor volume, estimated blood loss (EBL) during surgery, progression-free survival (PFS), and overall survival (OS). RESULTS The mean patient age was 55.4 ± 14.8 years, and 63 (30.4%) patients were male. Patients harboring tumors ≥ 3 cm were significantly older (56.9 ± 15.2 years vs 53.1 ± 13.6 years; p = 0.07), more frequently male (40.8% vs 14.6%; p = 0.0001), and had greater EBL (446.5 ± 532.2 ml vs 185.4 ± 197.2 ml; p = 0.0001), greater tumor volume (33.9 ± 38.1 ml vs 29.4 ± 23.5 ml; p = 0.0001), higher MIB-1 index values (3.0% ± 5.4% vs 1.7% ± 1.7%; p = 0.03), higher vWF levels (85.6% ± 76.9% vs 54.1% ± 52.4%; p = 0.001), lower HIF-1 expression (1.4 ± 1.3 vs 2.2 ± 1.4; p = 0.004), and worse OS (199.9 ± 7.6 months vs 180.8 ± 8.1 months; p = 0.05) than patients with tumors < 3 cm. In the multivariate logistic regression, MIB-1 (OR 1.14; p = 0.05), vWF (OR 1.01; p = 0.01), and HIF-1 (OR 1.54; p = 0.0001) significantly predicted tumor size. Although multiple factors were predictive of EBL in the univariate linear regression, only vWF remained significant in the multivariate analysis (β = 0.39; p = 0.004). Lastly, MIB-1 was useful via Kaplan-Meier survival analysis for predicting patients with disease progression, whereby an MIB-1 cutoff value of ≥ 3% conferred a 36% sensitivity and 82.5% specificity in predicting disease progression; an MIB-1 value ≥ 3% showed significantly shorter mean PFS (140.1 ± 11.7 months vs 179.5 ± 7.0 months; log-rank test, p = 0.05). The Cox proportional hazards model showed a trend for MIB-1 in predicting disease progression in a hazards model (OR 1.08; 95% CI 0.99-1.19; p = 0.08). CONCLUSIONS These results support the importance of various microvascularity measures in predicting preoperative (e.g., tumor size), intraoperative (e.g., EBL), and postoperative (e.g., PFS and OS) outcomes in patients with Grade I meningiomas. An MIB-1 cutoff value of 3% showed good specificity for predicting tumor progression. The predictive ability of various measures to detect aberrant tumor microvasculature differed, possibly reflecting the heterogeneous underlying biology of meningiomas. It may be necessary to combine assays to understand angiogenesis in meningiomas.

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