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Updated: May 6, 2026

Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations
Published on: October 20, 2017
Vascular Encasement Score as a Prognostic Tool for Outcome in Skull Base Tumor Resection
Iryna Bulakh1, Robert Lucaciu2, Nikola Duerr3
1Department of Neurosurgery, University Hospital Düsseldorf, Heinrich-Heine-University, Düsseldorf, Germany.
Objective:
Involvement of major cerebral arteries in skull-base tumors poses a significant surgical challenge and is associated with increased peri- and postoperative complication rates. The aim of the current study is to introduce a vascular encasement score (VES) to assess intraoperative risk and predict patient outcomes.
Methods:
Patients undergoing surgery for skull base tumors involving at least one major cerebral artery between April 2019 and March 2022 were included. Tumor-vessel contact was assessed on preoperative magnetic resonance imaging, evaluating both the longitudinal and circumferential encasement of arteries. Each parameter was graded from 1 to 5. The VES was calculated by multiplying the summed grades of both dimensions. Neurological outcomes were dichotomized into "good" and "poor" and correlated with VES.
Results:
Forty-eight patients were enrolled, most diagnosed with meningioma (79.17%). The mean resection rate was 77.23%, and the median VES was 32.5. Sixteen patients (33.3%) had poor outcomes. Higher VES scores were significantly associated with poor outcomes (P = 0.019). A receiver operating characteristic-derived threshold of 75 defined high-risk cases. Logistic regression confirmed that high VES (>75) predicted worse outcomes (P = 0.018). All patients with low VES had favorable outcomes and resection rates >90%. In contrast, 62.5% of patients with high VES had poor outcomes, influenced by a lower extent of resection (P < 0.001).
Conclusions:
The magnetic resonance imaging-based VES provides a practical method to quantify vascular involvement in skull base tumors. It may support risk-adapted surgical planning and serve as a foundation for Artificial Intelligence-based tools enabling automated preoperative risk assessment.

