OBJECT: Small vestibular schwannomas (VSs) are often conservatively managed and treated only upon growth. Growth is usually reported in mm/year, but describing the growth of a 3D structure by a single diameter has been questioned. As a result, VS growth dynamics should be further investigated. In addition, baseline clinical parameters that could predict growth would be helpful. In this prospective study the authors aimed to describe growth dynamics in a cohort of conservatively managed VSs. They also compared different growth models and evaluated the ability of baseline parameters to predict future growth. METHODS: Between 2000 and 2006, 178 consecutive patients with unilateral de novo small-sized VSs identified among the Norwegian population of 4.8 million persons were referred to a tertiary care center and were included in a study protocol of conservative management. Tumor size was defined by MR imaging-based volume estimates and was recorded along with clinical data at regular visits. Mixed-effects models were used to analyze the relationships between observations. Three growth models were compared using statistical diagnostic tests: a mm/year-based model, a cm(3)/year-based model, and a volume doubling time (VDT)-based model. A receiver operating characteristic curve analysis was used to determine a cutoff for the VDT-based model for distinguishing growing and nongrowing tumors. RESULTS: A mean growth rate corresponding to a VDT of 4.40 years (95% CI 3.49-5.95) was found. Other growth models in this study revealed mean growth rates of 0.66 mm/year (95% CI 0.47-0.86) and 0.19 cm(3)/year (95% CI 0.12-0.26). Volume doubling time was found to be the most realistic growth model. All baseline variables had p values > 0.09 for predicting growth. CONCLUSIONS: Based on the actual measurements, VDT was the most correct way to describe VS growth. The authors found that a cutoff of 5.22 years provided the best value to distinguish growing from nongrowing tumors. None of the investigated baseline predictors were usable as predictors of growth.
OBJECT: Small vestibular schwannomas (VSs) are often conservatively managed and treated only upon growth. Growth is usually reported in mm/year, but describing the growth of a 3D structure by a single diameter has been questioned. As a result, VS growth dynamics should be further investigated. In addition, baseline clinical parameters that could predict growth would be helpful. In this prospective study the authors aimed to describe growth dynamics in a cohort of conservatively managed VSs. They also compared different growth models and evaluated the ability of baseline parameters to predict future growth. METHODS: Between 2000 and 2006, 178 consecutive patients with unilateral de novo small-sized VSs identified among the Norwegian population of 4.8 million persons were referred to a tertiary care center and were included in a study protocol of conservative management. Tumor size was defined by MR imaging-based volume estimates and was recorded along with clinical data at regular visits. Mixed-effects models were used to analyze the relationships between observations. Three growth models were compared using statistical diagnostic tests: a mm/year-based model, a cm(3)/year-based model, and a volume doubling time (VDT)-based model. A receiver operating characteristic curve analysis was used to determine a cutoff for the VDT-based model for distinguishing growing and nongrowing tumors. RESULTS: A mean growth rate corresponding to a VDT of 4.40 years (95% CI 3.49-5.95) was found. Other growth models in this study revealed mean growth rates of 0.66 mm/year (95% CI 0.47-0.86) and 0.19 cm(3)/year (95% CI 0.12-0.26). Volume doubling time was found to be the most realistic growth model. All baseline variables had p values > 0.09 for predicting growth. CONCLUSIONS: Based on the actual measurements, VDT was the most correct way to describe VS growth. The authors found that a cutoff of 5.22 years provided the best value to distinguish growing from nongrowing tumors. None of the investigated baseline predictors were usable as predictors of growth.
Authors: J Shapey; K Barkas; S Connor; A Hitchings; H Cheetham; S Thomson; J M U-King-Im; R Beaney; D Jiang; S Barazi; R Obholzer; Nwm Thomas Journal: Ann R Coll Surg Engl Date: 2018-03 Impact factor: 1.891
Authors: Kerstin A Kessel; Hanna Fischer; Marco M E Vogel; Markus Oechsner; Henning Bier; Bernhard Meyer; Stephanie E Combs Journal: Strahlenther Onkol Date: 2016-11-01 Impact factor: 3.621
Authors: Jagdeep Singh Virk; Sonal Tripathi; Premjit S Randhawa; Elijah A Kwasa; Nigel D Mendoza; Jonathan Harcourt Journal: Indian J Otolaryngol Head Neck Surg Date: 2014-03-25
Authors: Roland Goldbrunner; Michael Weller; Jean Regis; Morten Lund-Johansen; Pantelis Stavrinou; David Reuss; D Gareth Evans; Florence Lefranc; Kita Sallabanda; Andrea Falini; Patrick Axon; Olivier Sterkers; Laura Fariselli; Wolfgang Wick; Joerg-Christian Tonn Journal: Neuro Oncol Date: 2020-01-11 Impact factor: 12.300