Growth of untreated vestibular schwannoma: a prospective study

2012 ◽  
Vol 116 (4) ◽  
pp. 706-712 ◽  
Author(s):  
Jobin Kotakkathu Varughese ◽  
Cathrine Nansdal Breivik ◽  
Tore Wentzel-Larsen ◽  
Morten Lund-Johansen

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 cm3/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 cm3/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.

Author(s):  
Ashvamedh Singh ◽  
Kulwant Singh ◽  
Anurag Sahu ◽  
R. S. Prasad ◽  
N. Pandey ◽  
...  

Abstract Objective To estimate the level of myelin basic protein (MBP) and look for its validity in outcome prediction among mild-to-moderate head injury patients. Materials and Methods It was a prospective study done at the Department of Neurosurgery, Institute of Medical Sciences, Banaras Hindu University from Jan 2018 to July 2019. All patients who presented to us within 48 hours of injury with mild-to-moderate head injury with apparently normal CT brain were include in the study. The serum sample were collected on the day of admission and 48 hours later, and patients were treated with standard protocols and observed 6 months postdischarge. Results Of the 32 patients enrolled, we observed mean MBP level was higher for severity of brain damage, but not associated with age, mode of injury, and radiological diagnosis. Mean MBP levels were not statistically associated with Glasgow coma scale (GCS) score at admission but was correlated to outcome with p < 0.05, with sensitivity of 50% and specificity 72%, that is, patients with good outcome have lower mean MBP levels. Conclusion MBP as per our analysis can be used as a prognostic marker in patients with head injury. It is not the absolute value rather a trend showing rise in serum MBP levels, which carries a significant value in outcome prediction.


2013 ◽  
Vol 23 (7) ◽  
pp. 1836-1845 ◽  
Author(s):  
Marjolein A. Heuvelmans ◽  
Matthijs Oudkerk ◽  
Geertruida H. de Bock ◽  
Harry J. de Koning ◽  
Xueqian Xie ◽  
...  

2017 ◽  
Vol 5 (9) ◽  
pp. 857-864
Author(s):  
Dr. Arun Divakar ◽  
◽  
Dr. M. Gopala Krishna Pillai ◽  
Dr. Elizabeth Thomas ◽  
◽  
...  

2018 ◽  
Vol 57 (9) ◽  
pp. 1107-1113 ◽  
Author(s):  
Manisha Thapa ◽  
Muthu Sendhil Kumaran ◽  
Tarun Narang ◽  
Uma N. Saikia ◽  
Gitesh U. Sawatkar ◽  
...  

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