Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

@article{Parmar2015RadiomicMC,
  title={Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer},
  author={Chintan A. Parmar and Patrick Grossmann and Derek Rietveld and M M Rietbergen and Philippe Lambin and Hugo J. W. L. Aerts},
  journal={Frontiers in oncology},
  year={2015},
  volume={5},
  pages={272}
}
INTRODUCTION "Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were… CONTINUE READING
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