JOURNAL OF COMPUTERS (JCP)
ISSN : 1796-203X
Volume : 1    Issue : 7    Date : October/November 2006

Learning a Classification-based Glioma Growth Model Using MRI Data
Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, and Mark Schmidt
Page(s): 21-31
Full Text:
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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