J Neurol Surg B Skull Base 2013; 74 - A215
DOI: 10.1055/s-0033-1336338

Recurrence Rate and Outcome Analysis of Skull Base Meningiomas after Surgical Resection

Ovanes Akobyan 1(presenter), Yury Shulev 1, Vladimir Shamanin 1
  • 1Saint Petersburg, Russia

Objective: Skull base meningiomas are complex. The identification of predictors of recurrence of skull base meningiomas remains an important goal in neurosurgery. The purpose of the research is outcome analysis of surgical treatment and reliable prognosis scale designing.

Methods: A total of 231 patients with skull base meningiomas were studied retrospectively. They were underwent surgery between 1996 and 2010. The mean patient age was 52.2 years. The mean estimated tumor diameter was 3.4 cm. Clinical data, magnetic resonance imaging studies, angiographic data, operative reports, and histopathological findings were examined in patients. Mean follow-up was 74 months (range, 6-146 months). Functional outcomes were determined using the KPS.

Results: Total removal (Simpson I) was obtained in 209 patients (83.6%); Simpson II was achieved in 24 patients (9.6%). In the 231 patients, mean preoperative and follow-up Karnofsky performance scale (KPS) scores were 76.2 ± 11 and 84 ± 8, respectively. The mean MIB-1 index was 1.7% (range, 0-41.6%). Nineteen patients (8.2%) had tumor recurrence. We analyzed standard clinical, neuroradiological, surgical, and histopathological parameters in patients with recurrent skull base meningiomas. As predictors of meningiomas relapse, we define the following: earlier radiation therapy, tumor localization (middle, multiple fossa), tumor size (>3 cm), cranial nerve affection, brain invasion, grade of tumor removal (Simpson I, II, III, IV), histological structure of tumor, and MIB-1 index. As a result, we offer the following numerical recurrence rate scale of meningiomas: low with a score of 0-3, moderate with a score of 4-6, and high with a score greater than 7.

Conclusion: Proper preoperative planning of the extent of tumor resection and prediction of the probability of recurrence allow choosing the correct combination of treatment options for a good functional outcome.