Intracranial WHO grade I meningioma: a competing risk analysis of progression and disease-specific survival

  • Charles ChampeauxEmail author
  • Deborah Houston
  • Laurence Dunn
  • Matthieu Resche-Rigon
Original Article - Tumor - Meningioma
Part of the following topical collections:
  1. Tumor – Meningioma



Studies on meningioma are reported with inadequate allowance for competing causes of progression or death. The aim of this study was to describe the outcome of patients with intracranial WHO grade I meningioma and identify factors that may influence disease progression and cause-specific survival.


Pathology reports and clinical data of 505 WHO grade I meningiomas treated between January 2003 and December 2017 were retrospectively reviewed at a single institution. We estimated a cumulative incidence function for progression and cause-specific mortality. A competing risk analysis was conducted on clinical and histological criteria. Median follow-up was 6.2 years.


A total of 530 surgical resections were performed on 505 cases. Forty-one patients received radiotherapy (RT). At data collection, 84 patients had died of their meningioma disease or demonstrated a recurrence eventually treated by redo surgery or RT. The risks of recurrence or meningioma-related death at 5 years were 16.2%, 95%CI[12.5, 20], whereas 5-year overall survival was 86.1%, 95%CI[82.8, 89.6]. In the multivariable Fine-Gray regression for a competing risk model, venous sinus invasion (SHR = 1.8, 95%CI[1.1, 2.9], p0.028), extent of resection (SHR = 0.2, 95%CI[0.1, 0.3], p < 0.001), and progressing meningioma (SHR = 7, 95%CI[3.3, 14.8], p < 0.001) were established as independent prognostic factors of cause-specific death or meningioma progression. In contrast, age at diagnosis < 65 years (HR = 1.1, 95%CI[1, 1.1], p < 0.001) and redo surgery for meningioma recurrence (HR = 2.6, 95%CI[1.4, 5], p = 0.00252) were predictors of the overall survival.


In this large series, WHO grade I meningioma treatment failure correlated with venous sinus invasion, incomplete resection, and progressing tumour; shorter survival correlated with increased age and redo surgery for recurrence. We recommend the cumulative incidence competing risk approach in WHO grade I meningioma studies where unrelated mortality may be substantial, as this approach results in more accurate estimates of disease risk and associated predictors.


WHO grade I meningioma Recurrence Prognostic factors Outcome Competing risks 



confidence interval


cumulative incidence competing risk


cumulative incidence function


competing risk


competing risk regression


gross total resection


high power field


hazard ratio


inter quartile range




overall survival


progression-free survival




subdistribution hazard ratio


sub total resection


total resection


World Health Organization



The authors thank the following person for their assistance: Janice Lafferty, Department of Neurosurgery; Dr. Andres Kulla, Elizabeth Fraser, Jacqueline MacPherson, Department of Neuropathology, Queen Elizabeth University Hospital, Glasgow; Melissa McEwan, Radiotherapy Department, The Beatson West of Scotland Cancer Centre, Glasgow; Thomas Alexander Gerds, Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark

Author contribution

CC: conceived and designed the analysis/collected the data/performed the analysis/wrote the paper. DH: collected the data/revision of the work. LD: collected the data/revision of the work/final approval. MRR: conceived and designed the analysis/revision of the work/final approval.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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 (name of institute/committee) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this retrospective study, formal consent was not required.


  1. 1.
    Abry E, Thomassen IØ, Salvesen ØO, Torp SH (2010) The significance of ki-67/MIB-1 labeling index in human meningiomas: a literature study. Pathol Res Pract 206(12):810–815CrossRefGoogle Scholar
  2. 2.
    Aizer AA, Bi WL, Kandola MS et al (2015) Extent of resection and overall survival for patients with atypical and malignant meningioma. Cancer 121(24):4376–4381CrossRefGoogle Scholar
  3. 3.
    van Alkemade H, de Leau M, Dieleman EMT, Kardaun JWPF, van Os R, Vandertop WP, van Furth WR, Stalpers LJA (2012) Impaired survival and long-term neurological problems in benign meningioma. Neuro-Oncology 14(5):658–666CrossRefGoogle Scholar
  4. 4.
    Austin PC, Lee DS, Fine JP (2016) Introduction to the analysis of survival data in the presence of competing risks. Circulation 133(6):601–609CrossRefGoogle Scholar
  5. 5.
    Champeaux C, Houston D, Dunn L (2017) Atypical meningioma. A study on recurrence and disease-specific survival. Neuro-Chirurgie 63(4):273–281CrossRefGoogle Scholar
  6. 6.
    Champeaux C, Jecko V, Houston D, Thorne L, Dunn L, Fersht N, Khan AA, Resche-Rigon M (2018) Malignant meningioma: an international multicentre retrospective study. Neurosurgery. CrossRefGoogle Scholar
  7. 7.
    Champeaux C, Weller J, Katsahian S (2019) Epidemiology of meningiomas. A nationwide study of surgically treated tumours on French medico-administrative data. Cancer Epidemiol 58:63–70CrossRefGoogle Scholar
  8. 8.
    Champeaux C, Wilson E, Brandner S, Shieff C, Thorne L (2015) World health organization grade III meningiomas.A retrospective study for outcome and prognostic factors assessment. Br J Neurosurg:1–6Google Scholar
  9. 9.
    Champeaux C, Wilson E, Shieff C, Khan AA, Thorne L (2016) WHO grade II meningioma: a retrospective study for outcome and prognostic factor assessment. J Neuro-Oncol 129(2):337–345CrossRefGoogle Scholar
  10. 10.
    von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, Initiative STROBE (2008) The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol 61(4):344–349CrossRefGoogle Scholar
  11. 11.
    Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509CrossRefGoogle Scholar
  12. 12.
    Gennatas ED, Wu A, Braunstein SE et al (2018) Preoperative and postoperative prediction of long-term meningioma outcomes. PLoS One 13(9):e0204161CrossRefGoogle Scholar
  13. 13.
    Hammouche S, Clark S, Wong AHL, Eldridge P, Farah JO (2014) Long-term survival analysis of atypical meningiomas: survival rates, prognostic factors, operative and radiotherapy treatment. Acta Neurochir. CrossRefGoogle Scholar
  14. 14.
    Harrell FE Jr (2015) Regression modeling strategies. Springer-Verlag New York, Inc., Secaucus, NJ, USACrossRefGoogle Scholar
  15. 15.
    Kallio M, Sankila R, Hakulinen T, Jääskeläinen J (1992) Factors affecting operative and excess long-term mortality in 935 patients with intracranial meningioma. Neurosurgery 31(1):2–12PubMedGoogle Scholar
  16. 16.
    Lam Shin Cheung V, Kim A, Sahgal A, Das S (2018) Meningioma recurrence rates following treatment: a systematic analysis. J Neuro-Oncol 136(2):351–361CrossRefGoogle Scholar
  17. 17.
    Lang TA, Altman DG (2015) Basic statistical reporting for articles published in biomedical journals: the “statistical analyses and methods in the published literature” or the SAMPL guidelines. Int J Nurs Stud 52(1):5–9CrossRefGoogle Scholar
  18. 18.
    Lemée J-M, Corniola MV, Da Broi M, Joswig H, Scheie D, Schaller K, Helseth E, Meling TR (2019) Extent of resection in meningioma: predictive factors and clinical implications. Sci Rep 9(1):5944CrossRefGoogle Scholar
  19. 19.
    Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820CrossRefGoogle Scholar
  20. 20.
    Ma X-J, Zhang G-J, Wang W, Li D, Wu Z, Zhang J-T (2019) Proposed treatment for intracranial transitional meningioma: a single-center series of 298 cases. World Neurosurg. CrossRefGoogle Scholar
  21. 21.
    Meling TR, Da Broi M, Scheie D, Helseth E, Smoll NR (2019) Meningioma surgery-are we making progress? World Neurosurg. CrossRefGoogle Scholar
  22. 22.
    Ostrom QT, Gittleman H, Liao P, Vecchione-Koval T, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2017) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014. Neuro-Oncology 19(suppl_5):v1–v88CrossRefGoogle Scholar
  23. 23.
    Pettersson-Segerlind J, Orrego A, Lönn S, Mathiesen T (2011) Long-term 25-year follow-up of surgically treated parasagittal meningiomas. World Neurosurg 76(6):564–571CrossRefGoogle Scholar
  24. 24.
    Core Team R (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  25. 25.
    Rogers CL, Perry A, Pugh S et al (2016) Pathology concordance levels for meningioma classification and grading in NRG Oncology RTOG Trial 0539. Neuro-Oncology 18(4):565–574CrossRefGoogle Scholar
  26. 26.
    RStudio Team (2015) RStudio: integrated development environment for r. RStudio, Inc., Boston, MAGoogle Scholar
  27. 27.
    Sankila R, Kallio M, Jääskeläinen J, Hakulinen T (1992) Long-term survival of 1986 patients with intracranial meningioma diagnosed from 1953 to 1984 in Finland. Comparison of the observed and expected survival rates in a population-based series. Cancer 70(6):1568–1576CrossRefGoogle Scholar
  28. 28.
    Satagopan JM, Ben-Porat L, Berwick M, Robson M, Kutler D, Auerbach AD (2004) A note on competing risks in survival data analysis. Br J Cancer 91(7):1229–1235CrossRefGoogle Scholar
  29. 29.
    Scrucca L, Santucci A, Aversa F (2007) Competing risk analysis using r: an easy guide for clinicians. Bone Marrow Transplant 40(4):381–387CrossRefGoogle Scholar
  30. 30.
    Scrucca L, Santucci A, Aversa F (2010) Regression modeling of competing risk using R: an in depth guide for clinicians. Bone Marrow Transplant 45(9):1388–1395CrossRefGoogle Scholar
  31. 31.
    Simpson D (1957) The recurrence of intracranial meningiomas after surgical treatment. J Neurol Neurosurg Psychiatry 20(1):22–39CrossRefGoogle Scholar
  32. 32.
    Sughrue ME, Sanai N, Shangari G, Parsa AT, Berger MS, McDermott MW (2010) Outcome and survival following primary and repeat surgery for World Health Organization Grade III meningiomas. J Neurosurg 113(2):202–209CrossRefGoogle Scholar
  33. 33.
    Therneau TM, Grambsch PM (2000) Modeling survival data: extending the Cox model. Springer, New YorkCrossRefGoogle Scholar
  34. 34.
    Woehrer A, Hackl M, Waldhör T et al (2014) Relative survival of patients with non-malignant central nervous system tumours: a descriptive study by the Austrian Brain Tumour Registry. Br J Cancer 110(2):286–296CrossRefGoogle Scholar
  35. 35.
    Zaher A, Abdelbari Mattar M, Zayed DH, Ellatif RA, Ashamallah SA (2013) Atypical meningioma: a study of prognostic factors. World Neurosurgery 80(5):549–553CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Neurosurgery, Institute of Neurological SciencesQueen Elizabeth University HospitalGlasgowUK
  2. 2.INSERM U1153, Statistic and Epidemiologic Research Centre Sorbonne Paris Cite (CRESS), ECSTRA teamUniversite Diderot - Paris 7, USPCParisFrance
  3. 3.Department of NeurosurgeryLariboisière HospitalParisFrance

Personalised recommendations