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Clinical and histopathological predictors of outcome in malignant meningioma.

Andrea Daniela MaierJiri BartekFrank ErikssonHeidi UgleholdtMarianne JuhlerHelle BroholmTiit I Mathiesen
Published in: Neurosurgical review (2019)
We investigated possible clinical and histopathological prognostic factors in a malignant meningioma cohort with comprehensive long-term population-based follow-up data. Twenty-four consecutive patients treated surgically for malignant meningioma at the Department of Neurosurgery and the Department of Pathology, Rigshospitalet, Copenhagen, Denmark, from December 2000 to March 2014 were retrospectively evaluated regarding progression-free survival (PFS) and overall survival (OS). Clinical parameters were recorded. All specimens underwent immunohistochemical analysis for Ki-67 and phosphohistone-H3 (PHH3). Prognostication was assessed with Cox proportional hazard regression analysis. The median follow-up was 46.1 months (range 0.7-150.7). The median progression-free survival was 16.5 months (95% CI 11.4-43.0) and the median overall survival was 46.6 months (95% CI 20.4-NA). Six patients were alive at the end of follow-up; two of these had not experienced a recurrence. No clinical parameter showed significant association with PFS or OS. Mitotic index (MI) was significantly associated with PFS and OS, and PHH3 MI with PFS. Immunohistochemical reactivity of Ki-67 > 10% was a negative predictor of PFS (HR 3.92, 95% CI 1.47-10.4, p = 0.0063) and OS (HR 3.35, 95% CI 1.12-10.1, p = 0.0313). The histological subgrouping of grade III meningioma into anaplastic and non-anaplastic revealed increased PFS for the latter (HR 4.57, CI 95% 1.32-15.7, p = 0.0164). We could not verify previous clinical parameters as prognostic factors in malignant meningioma. MI and the PHH3 MI were prognostic within WHO grade III meningiomas for PFS. An overall tumor staining of Ki-67 > 10% correlated with PFS and OS within grade III tumors.
Keyphrases
  • prognostic factors
  • free survival
  • newly diagnosed
  • end stage renal disease
  • optic nerve
  • machine learning
  • lymph node
  • tertiary care
  • patient reported outcomes
  • electronic health record