J Neurol Surg A Cent Eur Neurosurg 2023; 84(02): 109-115
DOI: 10.1055/s-0041-1739216
Original Article

Growth Analysis of Untreated Meningiomas under Observation

Charles F. Opalak
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Adam P. Sima
2   Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, United States
,
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Andrew Rock
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Aravind Somasundaram
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Kathryn G. Workman
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Alper Dincer
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
,
Vyshak Chandra
3   Department of Neurosurgery, University of Florida, Gainesville, Florida, United States
,
Rafael A. Vega
4   Department of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
,
1   Department of Neurosurgery, Virginia Commonwealth University Health System, Richmond, Virginia, United States
› Author Affiliations
Funding Services for this research project were generated by the VCU Massey Cancer Center Biostatistics Shared Resource, supported, in part, by funding from the NIH-NCI Cancer Center Support Grant P30 CA016059.

Abstract

Background When meningiomas are small or asymptomatic, the decision to observe rather than treat requires balancing the growth potential of the lesion with the outcome and side effects of treatment. The aim of this study is to characterize the growth patterns of untreated meningiomas to better inform the clinical decision-making process.

Methods Patients with meningiomas were identified from 2005 to 2015. Those without treatment who had been followed for 1.5 years, with three magnetic resonance imaging (MRI) scans, were identified. Scans were measured with orthogonal diameters, geometric mean diameters, and volumes using the ABC/2 method. Regression modeling determined what growth pattern these parameters best approximated.

Results Two hundred and fifteen MRI scans for 34 female (82.9%) and 7 male (17%) patients with 43 tumors were evaluated. Initial tumor volumes ranged from 0.13 to 9.98 mL. The mean and median initial volumes were 2.44 and 1.52 mL, respectively. Follow-up times ranged from 21 to 144 months, with a median of 70 months. There were 12 tumors (28%) whose growth rates were significantly greater than zero. For all tumors, use of a linear regression model allowed accurate prediction of the future size using prior data.

Conclusion Three-quarters of presumptive meningiomas managed conservatively do not grow significantly. The remainder have significant growth over time, and the behavior could be approximated with linear regression models.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.


Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.


Author Contributions

Conception and design were done by C.F.O., A.R., R.A.V., and W.C.B. A.R., A.S., K.G.W., M.T.C., A.D., and V.C. were responsible for acquisition of data. Analysis and interpretation of data were done by A.P.S.. C.F.O., A.P.S., A.S., A.R., V.C., M.T.C., and A.D. were involved in drafting the article. Critical revision of manuscript was done by M.T.C., A.R., A.D., C.F.O., A.P.S., A.S., K.G.W., V.C., R.V.A., and W.C.B. All the authors approved the final version of the manuscript.


Previous Presentations

Portions of this work were presented as a poster at the 2016 Annual Meeting of the Society for Neurooncology, Scottsdale, Arizona, United States.




Publication History

Received: 19 April 2021

Accepted: 23 June 2021

Article published online:
12 December 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Chung M, Dahabreh IJ, Hadar N. et al. AHRQ Comparative Effectiveness Technical Briefs. Emerging MRI Technologies for Imaging Musculoskeletal Disorders Under Loading Stress. Rockville, MD: Agency for Healthcare Research and Quality (US); 2011
  • 2 Ostrom QT, Cioffi G, Gittleman H. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro-oncol 2019; 21 (Suppl. 05) v1-v100
  • 3 Surawicz TS, McCarthy BJ, Kupelian V, Jukich PJ, Bruner JM, Davis FG. Descriptive epidemiology of primary brain and CNS tumors: results from the Central Brain Tumor Registry of the United States, 1990-1994. Neuro-oncol 1999; 1 (01) 14-25
  • 4 Katzman GL, Dagher AP, Patronas NJ. Incidental findings on brain magnetic resonance imaging from 1000 asymptomatic volunteers. JAMA 1999; 282 (01) 36-39
  • 5 Yue NC, Longstreth Jr WT, Elster AD, Jungreis CA, O'Leary DH, Poirier VC. Clinically serious abnormalities found incidentally at MR imaging of the brain: data from the Cardiovascular Health Study. Radiology 1997; 202 (01) 41-46
  • 6 Chamoun R, Krisht KM, Couldwell WT. Incidental meningiomas. Neurosurg Focus 2011; 31 (06) E19
  • 7 Vernooij MW, Ikram MA, Tanghe HL. et al. Incidental findings on brain MRI in the general population. N Engl J Med 2007; 357 (18) 1821-1828
  • 8 Chen M-H, Shao Q-M. Monte Carlo estimation of Bayesian credible and HPD intervals. J Comput Graph Stat 1999; 8 (01) 69-92
  • 9 Hashiba T, Hashimoto N, Izumoto S. et al. Serial volumetric assessment of the natural history and growth pattern of incidentally discovered meningiomas. J Neurosurg 2009; 110 (04) 675-684
  • 10 Nakasu S, Fukami T, Nakajima M, Watanabe K, Ichikawa M, Matsuda M. Growth pattern changes of meningiomas: long-term analysis. Neurosurgery 2005; 56 (05) 946-955 , discussion 946–955
  • 11 Nakasu S, Nakasu Y, Fukami T, Jito J, Nozaki K. Growth curve analysis of asymptomatic and symptomatic meningiomas. J Neurooncol 2011; 102 (02) 303-310
  • 12 Behbahani M, Skeie GO, Eide GE, Hausken A, Lund-Johansen M, Skeie BS. A prospective study of the natural history of incidental meningioma-Hold your horses!. Neurooncol Pract 2019; 6 (06) 438-450
  • 13 Nakasu S, Nakasu Y. Natural history of meningiomas: review with meta-analyses. Neurol Med Chir (Tokyo) 2020; 60 (03) 109-120
  • 14 Sreenivasan SA, Madhugiri VS, Sasidharan GM, Kumar RV. Measuring glioma volumes: a comparison of linear measurement based formulae with the manual image segmentation technique. J Cancer Res Ther 2016; 12 (01) 161-168
  • 15 Yu YL, Lee MS, Juan CJ, Hueng DY. Calculating the tumor volume of acoustic neuromas: comparison of ABC/2 formula with planimetry method. Clin Neurol Neurosurg 2013; 115 (08) 1371-1374
  • 16 Zeidman LA, Ankenbrandt WJ, Du H, Paleologos N, Vick NA. Growth rate of non-operated meningiomas. J Neurol 2008; 255 (06) 891-895
  • 17 Opalak CF, Parry M, Rock AK. et al. Comparison of ABC/2 estimation and a volumetric computerized method for measurement of meningiomas using magnetic resonance imaging. J Neurooncol 2019; 144 (02) 275-282
  • 18 Ishi Y, Terasaka S, Yamaguchi S. et al. Reliability of the size evaluation method for meningiomas: maximum diameter, ABC/2 formula, and planimetry method. World Neurosurg 2016; 94: 80-88
  • 19 Jadid KD, Feychting M, Höijer J, Hylin S, Kihlström L, Mathiesen T. Long-term follow-up of incidentally discovered meningiomas. Acta Neurochir (Wien) 2015; 157 (02) 225-230 , discussion 230