Advertisement

Journal of Neuro-Oncology

, Volume 139, Issue 1, pp 167–175 | Cite as

Tumor growth dynamics in serially-imaged low-grade glioma patients

  • Chloe Gui
  • Suzanne E. Kosteniuk
  • Jonathan C. Lau
  • Joseph F. Megyesi
Clinical Study

Abstract

Background

Diffuse low-grade gliomas (LGGs) are infiltrative, slow-growing primary brain tumors that remain relatively asymptomatic for long periods of time before progressing into aggressive and fatal high-grade gliomas.

Methods

We retrospectively identified LGG patients with numerous (≥ 8) serial magnetic resonance imaging (MRI) studies. Tumor volumes were measured by manual segmentation on serial imaging to study the natural history and growth of the lesion. Patient demographic information, tumor characteristics, and histological data were collected from electronic medical records and paper charts.

Results

Out of 74 LGG patients, 10 patients (13.5%) were identified to meet the study criteria with number of MRIs acquired ranging from 8 to 18 (median, 11.5) over a median of 79.7 months (range 39.8–113.8 months). Tumor diameter increased at a median of 2.17 mm/year in a linear trajectory. Cox regression analysis revealed that initial tumor volume was an independent predictor of time to clinical intervention, and Mann–Whitney U test found that patients younger than 50 years old had significantly slower-growing tumors. Clinical intervention was more likely for tumors above a volume threshold of 73.6 mL.

Conclusion

We retrospectively analyzed the natural history of LGGs of patients managed at a single institution with numerous serial MRI scans. Comparisons of our cohort to the literature suggest that this is a subset of particularly slow-growing and low-risk tumors.

Keywords

Neuro-oncology Low-grade glioma MRI Growth rate Longitudinal 

Notes

Funding

C.G. and S.E.K. received research scholarships from the Summer Research Training Program at the Schulich School of Medicine and Dentistry at Western University and the Mach-Gaensslen Foundation of Canada. J.C.L is funded through the Western University Clinical Investigator Program accredited by the Royal College of Physicians and Surgeons of Canada and a Canadian Institutes of Health Research Frederick Banting and Charles Best Canada Graduate Doctoral Award Scholarship.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interests to declare.

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 work was approved as a retrospective chart review by Western University’s Research Ethics Board.

Supplementary material

11060_2018_2857_MOESM1_ESM.docx (1.7 mb)
Supplementary material 1 (DOCX 1698 KB)
11060_2018_2857_MOESM2_ESM.pdf (70 kb)
Supplementary material 2 (PDF 70 KB)

References

  1. 1.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820.  https://doi.org/10.1007/s00401-016-1545-1 CrossRefPubMedGoogle Scholar
  2. 2.
    Smith JS, Chang EF, Lamborn KR et al (2008) Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J Clin Oncol 26:1338–1345.  https://doi.org/10.1200/JCO.2007.13.9337 CrossRefPubMedGoogle Scholar
  3. 3.
    Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 62:753–766.  https://doi.org/10.1227/01.neu.0000318159.21731.cf CrossRefPubMedGoogle Scholar
  4. 4.
    Keles GE, Lamborn KR, Berger MS (2001) Low-grade hemispheric gliomas in adults: a critical review of extent of resection as a factor influencing outcome. J Neurosurg 95:735–745.  https://doi.org/10.3171/jns.2001.95.5.0735 CrossRefPubMedGoogle Scholar
  5. 5.
    Jakola AS, Myrmel KS, Kloster R et al (2012) Comparison of a strategy favoring early surgical resection vs a strategy favoring watchful waiting in low-grade gliomas. JAMA 308:1881.  https://doi.org/10.1001/jama.2012.12807 CrossRefPubMedGoogle Scholar
  6. 6.
    Duffau H (2016) Long-term outcomes after supratotal resection of diffuse low-grade gliomas: a consecutive series with 11-year follow-up. Acta Neurochir (Wien) 158:51–58.  https://doi.org/10.1007/s00701-015-2621-3 CrossRefGoogle Scholar
  7. 7.
    Reijneveld JC, Sitskoorn MM, Klein M et al (2001) Cognitive status and quality of life in patients with suspected versus proven low-grade gliomas. Neurology 56:618–623.  https://doi.org/10.1212/WNL.56.5.618 CrossRefPubMedGoogle Scholar
  8. 8.
    Peyre M, Cartalat-Carel S, Meyronet D et al (2010) Prolonged response without prolonged chemotherapy: a lesson from PCV chemotherapy in low-grade gliomas. Neuro Oncology 12:1078–1082.  https://doi.org/10.1093/neuonc/noq055 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Pallud J, Blonski M, Mandonnet E et al (2013) Velocity of tumor spontaneous expansion predicts long-term outcomes for diffuse low-grade gliomas. Neuro Oncology 15:595–606.  https://doi.org/10.1093/neuonc/nos331 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Mandonnet E, Delattre JY, Tanguy ML et al (2003) Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann Neurol 53:524–528.  https://doi.org/10.1002/ana.10528 CrossRefPubMedGoogle Scholar
  11. 11.
    Brasil Caseiras G, Ciccarelli O, Altmann DR et al (2009) Low-grade gliomas: six-month tumor growth predicts patient outcome better than admission tumor volume, relative cerebral blood volume, and apparent diffusion coefficient. Radiology 253:505–512.  https://doi.org/10.1148/radiol.2532081623 CrossRefPubMedGoogle Scholar
  12. 12.
    Hlaihel C, Guilloton L, Guyotat J et al (2010) Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol 97:73–80.  https://doi.org/10.1007/s11060-009-9991-4 CrossRefPubMedGoogle Scholar
  13. 13.
    Pallud J, Llitjos J-F, Dhermain F et al (2012) Dynamic imaging response following radiation therapy predicts long-term outcomes for diffuse low-grade gliomas. Neuro Oncology 14:496–505.  https://doi.org/10.1093/neuonc/nos069 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Pallud J, Mandonnet E, Duffau H et al (2006) Prognostic value of initial magnetic resonance imaging growth rates for world health organization grade II gliomas. Ann Neurol 60:380–383.  https://doi.org/10.1002/ana.20946 CrossRefPubMedGoogle Scholar
  15. 15.
    Rees J, Watt H, Jäger HR et al (2009) Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation. Eur J Radiol 72:54–64.  https://doi.org/10.1016/j.ejrad.2008.06.013 CrossRefPubMedGoogle Scholar
  16. 16.
    Epilepsy Implementation Task Force (2015) Provincial guidelines for the management of epilepsy in adults and children. http://epilepsyontario.org/wp-content/uploads/2015/03/Provincial-Guidelines-for-the-Management-of-Epilepsy-in-Adults-and-Children_Janurary-20151.pdf. Accessed 1 Feb 2018
  17. 17.
    Pignatti F, van den Bent M, Curran D et al (2002) Prognostic factors for survival in adult patients with cerebral low-grade glioma. J Clin Oncol 20:2076–2084.  https://doi.org/10.1200/JCO.2002.08.121 CrossRefPubMedGoogle Scholar
  18. 18.
    Chang EF, Clark A, Jensen RL et al (2009) Multiinstitutional validation of the University of California at San Francisco low-grade glioma prognostic scoring system. Clinical article. J Neurosurg 111:203–210.  https://doi.org/10.3171/2009.2.JNS081101 CrossRefPubMedGoogle Scholar
  19. 19.
    Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128.  https://doi.org/10.1016/j.neuroimage.2006.01.015 CrossRefPubMedGoogle Scholar
  20. 20.
    Lau JC, Kosteniuk SE, Bihari F, Megyesi JF (2017) Functional magnetic resonance imaging for preoperative planning in brain tumour surgery. Can J Neurol Sci/J Can des Sci Neurol 44:59–68.  https://doi.org/10.1017/cjn.2016.306 CrossRefGoogle Scholar
  21. 21.
    Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11:178–189.  https://doi.org/10.1016/S1076-6332(03)00671-8 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Pallud J, Varlet P, Devaux B et al (2010) Diffuse low-grade oligodendrogliomas extend beyond MRI-defined abnormalities. Neurology 74:1724–1731.  https://doi.org/10.1212/WNL.0b013e3181e04264 CrossRefPubMedGoogle Scholar
  23. 23.
    Potts MB, Smith JS, Molinaro AM, Berger MS (2012) Natural history and surgical management of incidentally discovered low-grade gliomas. J Neurosurg 116:365–372.  https://doi.org/10.3171/2011.9.JNS111068 CrossRefPubMedGoogle Scholar
  24. 24.
    Zhang ZY, Chan AKY, Ng HK et al (2014) Surgically treated incidentally discovered low-grade gliomas are mostly IDH mutated and 1p19q co-deleted with favorable prognosis. Int J Clin Exp Pathol 7:8627–8636PubMedPubMedCentralGoogle Scholar
  25. 25.
    Opoku-Darko M, Lang ST, Artindale J et al (2017) Surgical management of incidentally discovered diffusely infiltrating low-grade glioma. J Neurosurg.  https://doi.org/10.3171/2017.3.JNS17159 PubMedGoogle Scholar
  26. 26.
    Pallud J, Fontaine D, Duffau H et al (2010) Natural history of incidental world health organization grade II gliomas. Ann Neurol 68:727–733.  https://doi.org/10.1002/ana.22106 CrossRefPubMedGoogle Scholar
  27. 27.
    Claus EB, Black PM (2006) Survival rates and patterns of care for patients diagnosed with supratentorial low-grade gliomas: data from the SEER program, 1973–2001. Cancer 106:1358–1363.  https://doi.org/10.1002/cncr.21733 CrossRefPubMedGoogle Scholar
  28. 28.
    Yan H, Parsons W, Jin G (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773.  https://doi.org/10.1007/s00428-009-0805-z CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Sanson M, Marie Y, Paris S et al (2009) Isocitrate dehydrogenase 1 codon 132 mutation is an important prognostic biomarker in gliomas. J Clin Oncol 27:4150–4154.  https://doi.org/10.1200/JCO.2009.21.9832 CrossRefPubMedGoogle Scholar
  30. 30.
    Houillier C, Wang X, Kaloshi G et al (2010) IDH1 or IDH2 mutations predict longer survival and response to temozolomide in low-grade gliomas. Neurology 75:1560–1566.  https://doi.org/10.1212/WNL.0b013e3181f96282 CrossRefPubMedGoogle Scholar
  31. 31.
    Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508.  https://doi.org/10.1056/NEJMoa1407279 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Gozé C, Blonski M, Le Maistre G et al (2014) Imaging growth and isocitrate dehydrogenase 1 mutation are independent predictors for diffuse low-grade gliomas. Neuro Oncology 16:1100–1109.  https://doi.org/10.1093/neuonc/nou085 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Metellus P, Coulibaly B, Colin C et al (2010) Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 120:719–729.  https://doi.org/10.1007/s00401-010-0777-8 CrossRefPubMedGoogle Scholar
  34. 34.
    Gozé C, Bezzina C, Gozé E et al (2012) 1P19Q loss but not IDH1 mutations influences WHO grade II gliomas spontaneous growth. J Neurooncol 108:69–75.  https://doi.org/10.1007/s11060-012-0831-6 CrossRefPubMedGoogle Scholar
  35. 35.
    Zlatescu MC, TehraniYazdi A, Sasaki H et al (2001) Tumor location and growth pattern correlate with genetic signature in oligodendroglial neoplasms. Cancer Res 61:6713–6715PubMedGoogle Scholar
  36. 36.
    Megyesi JF, Kachur E, Lee DH et al (2004) Imaging correlates of molecular signatures in oligodendrogliomas imaging correlates of molecular signatures in oligodendrogliomas. Clin Cancer Res 10:4303–4306.  https://doi.org/10.1158/1078-0432.CCR-04-0209 CrossRefPubMedGoogle Scholar
  37. 37.
    Jenkins RB, Blair H, Ballman KV et al (2006) A t(1;19)(q10;p10) mediates the combined deletions of 1p and 19q and predicts a better prognosis of patients with oligodendroglioma. Cancer Res 66:9852–9861.  https://doi.org/10.1158/0008-5472.CAN-06-1796 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Clinical Neurological Sciences (Neurosurgery)Western UniversityLondonCanada
  2. 2.Imaging Research LaboratoriesRobarts Research InstituteLondonCanada
  3. 3.Department of Pathology (Neuropathology)Western UniversityLondonCanada
  4. 4.London Health Sciences CentreUniversity HospitalLondonCanada

Personalised recommendations