¹Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada fmarianne; greiner; joerg; schmidtg@cs.ualberta
²Department of Radiation Oncology, Cross Cancer Institute, 11560 - University Ave, Edmonton, AB, T6G 1Z2, Canada

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Abstract—Gliomas are malignant brain tumors that grow by invading adjacent tissue. We propose and evaluate a 3D classification-based growth 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. We thoroughly study CDM results numerically and analytically in light of the training data we used, and we also discuss the current limitations of the model. We finally conclude the paper with a discussion of promising future research directions.

Index Terms—machine learning, brain tumors, glioma, growth models, prediction

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Cite: Marianne Morris, Russell Greiner, J¨org Sander, Albert Murtha, Mark Schmidt, "Learning a Classication-based Glioma Growth Model Using MRI Data," Journal of Computers vol. 1, no.7, pp. 21-31, 2006.