A Classification-Based Glioma Diffusion Model Using MRI Data

  • Marianne Morris
  • Russell Greiner
  • Jörg Sander
  • Albert Murtha
  • Mark Schmidt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


Gliomas are diffuse, invasive brain tumors. We propose a 3D classification-based diffusion model, CDM, that predicts how a glioma will grow at a voxel-level, on the basis of features specific to the patient, properties of the tumor, and attributes of that voxel. We use Supervised Learning algorithms to learn this general model, by observing the growth patterns of gliomas from other patients. Our empirical results on clinical data demonstrate that our learned CDM model can, in most cases, predict glioma growth more effectively than two standard models: uniform radial growth across all tissue types, and another that assumes faster diffusion in white matter.


White Matter Grey Matter Glioma Cell Uniform Growth Edema Region 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brain Tumor Growth Prediction, http://www.cs.ualberta.ca/~btgp/ai06.html
  2. 2.
    Brown, M., Semelka, R.: MRI Basic Principles and Applications. Wiley, Hoboken (2003)CrossRefGoogle Scholar
  3. 3.
    Clatz, O., Bondiau, P., Delingette, H., et al.: In Silico Tumor Growth: Application to Glioblastomas. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 337–345. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Friston, K., Ashburner, J., Frith, C., Poline, J., Heather, J., Frackowiak, R.: Spatial Registration and Normalization of Images Human Brain Mapping 2, 165–189 (1995)Google Scholar
  5. 5.
    Friston, K., Ashburner, J.: Multimodal Image Coregistration and Partitioning — a Unified Framework. NeuroImage 6, 209–217 (1997)CrossRefGoogle Scholar
  6. 6.
    Halperin, E., Bentel, G., Heinz, E.: Radiation Therapy Treatment Planning in Supratentorial Glioblastoma Multiforme, Int. J. Radiat. Oncol. Biol. Phys. 17, 1347–1350 (1989)CrossRefGoogle Scholar
  7. 7.
    Hochberg, F., Pruitt, A.: Assumptions in the Radiotherapy of Glioblastoma. Neurology 30, 907–911 (1980)Google Scholar
  8. 8.
    Holmes, C., Hoge, R., Collins, L., et al.: Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22(2), 324–333 (1998)CrossRefGoogle Scholar
  9. 9.
    Kansal, A., Torquato, S., Harsh, G., et al.: Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203, 367–382 (2000)CrossRefGoogle Scholar
  10. 10.
    Lee, C.H., Greiner, R., Schmidt, M.: Support Vector Random Fields for Spatial Classification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 121–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    le Cessie, S., van Houwelingen, J.: Ridge Estimators in Logistic Regression. Applied Statistics 41(1), 191–201 (1992)MATHCrossRefGoogle Scholar
  12. 12.
    Morris, M.: Classification-based Glioma Diffusion Modeling. MSc Thesis, University of Alberta (2005)Google Scholar
  13. 13.
    Platt, J.: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods — Support Vector Learning, MIT Press, Cambridge (1998)Google Scholar
  14. 14.
    Price, S., Burnet, N., Donovan, T., et al.: Diffusion tensor imaging of brain tumorss at 3T: a potential tool for assessing white matter tract invasion? Clinical Radiology 58, 455–462 (2003)CrossRefGoogle Scholar
  15. 15.
    Student, The Probable Error of a Mean. Biometrika 6, 1–25 (1908)Google Scholar
  16. 16.
    Swanson, K., Alvord, E., Murray, J.: A Quantitative Model for Differential Motility of Gliomas in grey and White Matter. Cell Prolif. 33, 317–329 (2000)CrossRefGoogle Scholar
  17. 17.
    Swanson, K., Alvord, E., Murray, J.: Quantifying efficacy of chemotherapy of brain tumors with homogeneous and heterogeneous drug delivery. Acta Biotheor 50(4), 223–237 (2002)CrossRefGoogle Scholar
  18. 18.
    Swanson, K., Alvord, E., Murray, J.: Virtual brain tumors (gliomas) enhance the reality of medical imaging and highlight inadequacies of current therapy. British Journal of Cancer 86, 14–18 (2002)CrossRefGoogle Scholar
  19. 19.
    Tabatabai, M., Williams, D., Bursac, Z.: Hyperbolastic growth models: theory and application. Theor. Biol. Med. Model 2(1), 14 (2005)CrossRefGoogle Scholar
  20. 20.
    Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  21. 21.
    Zizzari, A.: Methods on Tumor Recognition and Planning Target Prediction for the Radiotherapy of Cancer, PhD Thesis, University of Magdeburg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marianne Morris
    • 1
  • Russell Greiner
    • 1
  • Jörg Sander
    • 1
  • Albert Murtha
    • 2
  • Mark Schmidt
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Radiation OncologyCross Cancer InstituteEdmontonCanada

Personalised recommendations