Modeling Tumor Cellularity in Newly Diagnosed GBMs using MR Imaging and Spectroscopy

Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3213
In this paper, we analyze the relationship between parameters of brain tumors obtained through in vivo magnetic resonance imaging (MRI), in vivo magneticnresonance spectroscopy (MRS), and ex vivo immunohistochemistry (IHC). The goal of our project is to provide a quantitative definition of tumor cellularity based on the in vivo parameters. Biopsy samples obtained from previously untreated patients with a diagnosis of GBM are used to find the link between imaging parameters at the specific biopsy locations and IHC parameters from the corresponding tissue samples. A functional tree (FT) model of tumor cellularity is learned from the in vivo parameters and the remaining histological parameters. The tumor cellularity model is then tested on examples which contain only in vivo parameters, by first estimating the remaining IHC parameters by applying the Expectation Maximization (EM) algorithm, and then using the complete parameter vector for classification.

Reviews
There is no review at this time. Be the first to review this publication!

Quick Comments


Resources
backyellow
Download All
Download Paper , View Paper

Statistics more
backyellow
Global rating: starstarstarstarstar
Review rating: starstarstarstarstar [review]
Paper Quality: plus minus

Information more
backyellow
Categories: Classification, Decision trees and non-metric classification, Missing and Noisy Features
Keywords: tumor cellularity, classification, multivariate model, MRI, MRS, spectroscopy
Export citation:

Share
backyellow
Share

Linked Publications more
backyellow
Diffeomorphic Demons Using ITK's Finite Difference Solver Hierarchy Diffeomorphic Demons Using ITK's Finite Difference Solver Hierarchy
by Vercauteren T., Pennec X., Perchant A., Ayache N.
CTest Integration of Sikuli Automated GUI Testing CTest Integration of Sikuli Automated GUI Testing
by Schwab E., Gelas A., Souhait L., Rannou N., Mosaliganti K., Megason S.

View license
Loading license...

Send a message to the author
main_flat
Powered by Midas