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Learning a Classification-based Glioma Growth Model Using MRI Data

Full Text: jcp01072131.pdf PDF

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. We study CDM numerically and analytically on clinical data.

Citation

M. Morris, R. Greiner, J. Sander, A. Murtha, M. Schmidt. "Learning a Classification-based Glioma Growth Model Using MRI Data". Journal of Computers (JCP), 1(7), pp 21-31, November 2006.

Keywords: machine learning, brain tumor, glioma, growth models, prediction, medical informatics
Category: In Journal
Web Links: BTAP
Related Publication(s): A Classification-based Glioma Diffusion Model Using MRI Data

BibTeX

@article{Morris+al:JCP06,
  author = {Marianne Morris and Russ Greiner and Joerg Sander and Albert Murtha
    and Mark Schmidt},
  title = {Learning a Classification-based Glioma Growth Model Using MRI Data},
  Volume = "1",
  Number = "7",
  Pages = {21-31},
  journal = {Journal of Computers (JCP)},
  year = 2006,
}

Last Updated: January 15, 2017
Submitted by Russ Greiner

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