Abstract
Objectives: This study characterizes primary petroclival meningioma growth rates, before intervention, using volumetric analysis. In addition, predictors of growth are analyzed. Methods: Patients with previously untreated petroclival meningiomas were retrospectively reviewed (1999-2015). Image analysis software was used to perform volumetric analyses of tumor size and growth. Three-dimensional segmentation volumetric analyses were compared with volumes estimated utilizing three orthogonal dimensions. Tumor growth was defined as a 15% increase in volume. Results: Thirty-four patients who underwent at least two magnetic resonance imaging (MRI) studies before intervention were included. The mean age was 55.2 years, and 65.7% were women. The mean tumor volume at presentation was 5.6 cm3 (range, 0.1-25.8 cm3) as determined from segmentation volumetric analysis. At a mean follow-up of 44.5 months (range, 3.7-125.1 mo), 88.2% of tumors grew. The mean annual volumetric growth rate was 2.38 cm3/yr (-0.63 to 25.9 cm3/yr). Tumor volume, T2 hyperintensity within the tumor, peritumoral edema, and ataxia and/or cerebellar symptoms at presentation were all significantly associated with greater rates of tumor growth. Ultimately, 10 (29.4%) patients underwent treatment during the follow-up period. Conclusion: Our experience demonstrates that the vast majority (88%) of untreated petroclival meningiomas grow; the mean volumetric growth rate was noted to be 2.38 cm3/ yr. We found a significant association between increased growth rate and larger tumor size at diagnosis, T2 hyperintensity within the tumor, peritumoral edema, and the presence of ataxia and/or cerebellar symptoms.
Original language | English (US) |
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Pages (from-to) | 123-128 |
Number of pages | 6 |
Journal | Otology and Neurotology |
Volume | 38 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2017 |
Keywords
- Meningioma
- Natural history
- Petroclival meningioma
- Skull base
- Volumetric growth
ASJC Scopus subject areas
- Otorhinolaryngology
- Sensory Systems
- Clinical Neurology