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JCP
2006

Learning a Classification-based Glioma Growth Model Using MRI Data

13 years 3 months ago
Learning a Classification-based Glioma Growth Model Using MRI Data
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.
Marianne Morris, Russell Greiner, Jörg Sander
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JCP
Authors Marianne Morris, Russell Greiner, Jörg Sander, Albert Murtha, Mark Schmidt
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