Predictive modeling in glioma grading from MR perfusion images using support vector machines.

@article{Emblem2008PredictiveMI,
  title={Predictive modeling in glioma grading from MR perfusion images using support vector machines.},
  author={Kyrre Eeg Emblem and Frank G. Z{\"o}llner and Bjorn Tennoe and Baard Nedregaard and Terje Nome and Paulina Due-Tonnessen and John Hald and David Scheie and Atle Bjornerud},
  journal={Magnetic resonance in medicine},
  year={2008},
  volume={60 4},
  pages={945-52}
}
The advantages of predictive modeling in glioma grading from MR perfusion images have not yet been explored. The aim of the current study was to implement a predictive model based on support vector machines (SVM) for glioma grading using tumor blood volume histogram signatures derived from MR perfusion images and to assess the diagnostic accuracy of the model and the sensitivity to sample size. A total of 86 patients with histologically-confirmed gliomas were imaged using dynamic susceptibility… CONTINUE READING
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