Skip to main content
SpringerLink
Log in

Expert-validated CSF segmentation of MNI atlas enhances accuracy of virtual glioma growth patterns

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Biomathematical modeling of glioma growth has been developed to optimize treatments delivery and to evaluate their efficacy. Simulations currently make use of anatomical knowledge from standard MRI atlases. For example, cerebrospinal fluid (CSF) spaces are obtained by automatic thresholding of the MNI atlas, leading to an approximate representation of real anatomy. To correct such inaccuracies, an expert-revised CSF segmentation map of the MNI atlas was built. Several virtual glioma growth patterns of different locations were generated, with and without using the expert-revised version of the MNI atlas. The adequacy between virtual and radiologically observed growth patterns was clearly higher when simulations were based on the expert-revised atlas. This work emphasizes the need for close collaboration between clinicians and researchers in the field of brain tumor modeling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Bondiau P-Y, Konukoglu E, Clatz O, Delingette H, Frenay M, Paquis P (2011) Biocomputing: numerical simulation of glioblastoma growth and comparison with conventional irradiation margins. Phys Med 27(2):103–108

    Article  PubMed  Google Scholar 

  2. Corwin D, Holdsworth C, Rockne RC, Trister AD, Mrugala MM, Rockhill JK, Stewart RD, Phillips M, Swanson KR (2013) Toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma. PloS One 8(11):e79115

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Čajavec-Bernard B, Idbaih A, Psimaras D, Dainese Linda et al (2012) A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy. Clin Cancer Res 18(18):5071–5080

    Article  CAS  PubMed  Google Scholar 

  4. Rockne R, Alvord EC Jr, Rockhill JK, Swanson KR (2009) A mathematical model for brain tumor response to radiation therapy. J Math Biol 58(4–5):561–578

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Mandonnet E (2011) Mathematical modeling of glioma on MRI. Rev Neurol 167(10):715–720

    Article  CAS  PubMed  Google Scholar 

  6. Neal ML, Trister AD, Cloke T, Sodt R, Ahn S, Baldock Anne L, Bridge Carly A, Lai Albert, Cloughesy Timothy F, Mrugala Maciej M et al (2013) Discriminating survival outcomes in patients with glioblastoma using a simulation-based, patient-specific response metric. PloS One 8(1):e51951

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Wang CH, Rockhill JK, Mrugala M, Peacock DL, Lai A, Jusenius Katy, Wardlaw Joanna M, Cloughesy T, Spence AM, Rockne R et al (2009) Prognostic significance of growth kinetics in newly diagnosed glioblastomas revealed by combining serial imaging with a novel biomathematical model. Cancer Res 69(23):9133–9140

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Stretton E, Mandonnet E, Geremia E, Menze BH, Delingette H, Ayache N (2012) Predicting the location of glioma recurrence after a resection surgery. Medical Image Computing and Computer-Assisted Intervention, MICCAI, 2012

  9. Fonov VS, Evans AC, McKinstry RC, Almli CR, Collins DL (2009) Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47:S102–S102

    Article  Google Scholar 

  10. Cruywagen G, Woodward D, Tracqui P, Bartoo G, Murray J, Alvord E (1995) The modelling of diffusive tumours. J Biol Syst 3:937–945

    Article  Google Scholar 

  11. Tracqui P, Cruywagen GC, Woodward DE, Bartoo GT, Murray JD, Alvord EC (1995) A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth. Cell Prolif 28:17–31

    Article  CAS  PubMed  Google Scholar 

  12. Woodward DE, Cook J, Tracqui P, Cruywagen GC, Murray JD, Alvord EC (1996) A mathematical model of glioma growth: the effect of extent of surgical resection. Cell Prolif 29(6):269–288

    Article  CAS  PubMed  Google Scholar 

  13. Swanson K, Alvord E, Murray J (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 33(5):317–330

    Article  CAS  PubMed  Google Scholar 

  14. Clatz O, Sermesant M, Bondiau P-Y, Delingette H, Warfield SK, Malandain G, Ayache N (2005) Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans Med Imaging 24:1334–1346

    Article  PubMed Central  PubMed  Google Scholar 

  15. Jbabdi S, Mandonnet E, Duffau H, Capelle L, Swanson K, Pélégrini-Issac M, Guillevin R, Benali H (2005) Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn Resonan Med 54(3):616–624

    Article  Google Scholar 

  16. Stretton E, Geremia E, Menze B, Delingette H, Ayache N (2013) Importance of patient DTI’s to accurately model glioma growth using the reaction diffusion equation. ISBI

  17. Mandonnet E, Capelle L, Duffau H (2006) Extension of paralimbic low grade gliomas: toward an anatomical classification based on white matter invasion patterns. J Neuro Oncol 78(2):179–185

    Article  Google Scholar 

  18. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. doi:10.1016/j.mri.2012.05.001

  19. Clatz O, Sermesant M, Bondiau PY, Delingette H, Warfield SK, Malandain G, Ayache N (2005) Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans Med Imaging 24(10):1334–1346

    Article  PubMed Central  PubMed  Google Scholar 

  20. Gooya A, Biros G, Davatzikos C (2011) Deformable registration of glioma images using em algorithm and diffusion reaction modeling. IEEE Trans Med Imaging 30(2):375–390

    Article  PubMed Central  PubMed  Google Scholar 

  21. Hogea C, Davatzikos C, Biros G (2008) An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects. J Math Biol 56(6):793–825

    Article  PubMed Central  PubMed  Google Scholar 

  22. Konukoglu E, Clatz O, Menze B, Stieltjes B, Weber M, Mandonnet E, Delingette H, Ayache N (2009) Image guided personalization of reaction–diffusion type tumor growth models using modified anisotropic eikonal equations. IEEE Trans Med Imaging 29(1):77–95

    Article  PubMed  Google Scholar 

  23. Menze BH, Stretton E, Konukoglu E, Ayache N (2011) Image-based modeling of tumor growth in patients with glioma. Springer, Heidelberg

    Google Scholar 

  24. Murray JD (2002) Mathematical biology, vol 2. Springer, New York

    Google Scholar 

  25. Swanson KR, Rostomily RC, Alvord EC (2007) A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle. Br J Cancer 98(1):113–119

    Article  PubMed Central  PubMed  Google Scholar 

  26. Tracqui P, Cruywagen G, Woodward D, Bartoo G, Murray J, Alvord EC Jr (1995) A mathematical model of glioma growth: the effect of chemotherapy on spatio-temporal growth. Cell Prolif 28(1):17–31

    Article  CAS  PubMed  Google Scholar 

  27. Konukoglu E (2009) Modeling glioma growth and personalizing growth models in medical images. PhD thesis, University of Nice, Nice

  28. Ebert U, van Saarloos W (2000) Front propagation into unstable states: universal algebraic convergence towards uniformly translating pulled fronts. Phys D 146(1):1–99

    Article  Google Scholar 

  29. Sethian James Albert (1999) Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science, vol 3. Cambridge University Press, Cambridge

    Google Scholar 

  30. Keener J, Sneyd J (1998) Mathematical physiology, interdisciplinary applied mathematics. Springer, New York

    Google Scholar 

  31. Swanson K, Bridge C, Murray J, Alvord E (2003) Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sci 216(1):1–10

    Article  PubMed  Google Scholar 

  32. Swanson KR (1999) Mathematical modeling of the growth and control of tumors. PhD thesis, University of Washington, Seattle

  33. Konukoglu E, Clatz O, Bondiau PY, Delingette H, Ayache N (2010) Extrapolating glioma invasion margin in brain magnetic resonance images: suggesting new irradiation margins. Med Image Anal 14(2):111–125

    Article  PubMed  Google Scholar 

  34. Bohman L-E, Swanson KR, Moore JL, Rockne R, Mandigo C, Hankinson T, Assanah M, Canoll P, Bruce JN (2010) Magnetic resonance imaging characteristics of glioblastoma multiforme: implications for understanding glioma ontogeny. Neurosurgery 67(5):1319

    Article  PubMed Central  PubMed  Google Scholar 

  35. Lim DA, Cha S, Mayo MC, Chen M-H, Keles E, VandenBerg Scott, Berger Mitchel S (2007) Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype. Neuro-oncology 9(4):424–429

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

Erin Stretton’s research work was partially funded by ERC advanced grant MedYMA.

Author information

Authors and Affiliations

  1. Neurosurgery Department, Hôpital Lariboisière, Paris, France

    A. Amelot, S. Froelich & E. Mandonnet

  2. Asclepios Research Project, INRIA, Sophia Antipolis, France

    E. Stretton, H. Delingette & N. Ayache

  3. Université Paris 7, Paris, France

    S. Froelich & E. Mandonnet

  4. IMNC, UMR 8165, Orsay, France

    E. Mandonnet

Authors
  1. A. Amelot
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Stretton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Delingette
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Ayache
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Froelich
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. Mandonnet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Mandonnet.

Additional information

Aymeric Amelot and Erin Stretton are first co-authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amelot, A., Stretton, E., Delingette, H. et al. Expert-validated CSF segmentation of MNI atlas enhances accuracy of virtual glioma growth patterns. J Neurooncol 121, 381–387 (2015). https://doi.org/10.1007/s11060-014-1645-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-014-1645-5

Keywords

Search

Navigation