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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Nature Communications · 2014 · Vol. 5(1) · pp. 4006–4006
Hugo J.W.L. AertsEmmanuel Rios VelazquezRalph T. H. LeijenaarChintan ParmarPatrick GroßmannSara CarvalhoJohan BussinkRené MonshouwerBenjamin Haibe‐KainsD. RietveldFrank HoebersMichelle M. RietbergenC. René LeemansAndré DekkerJohn QuackenbushRobert J. GilliesPhilippe Lambin
Radiomics and Machine Learning in Medical ImagingPancreatic and Hepatic Oncology ResearchLung Cancer Diagnosis and TreatmentRadiomicsRadiogenomicsLung cancerMedicineHead and neck cancerPhenotypeHead and neckMedical imagingCancerPathology

MeSH terms

AdenocarcinomaCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellFemaleHead and Neck NeoplasmsHumansLung NeoplasmsMalePhenotypePrognosisTomography, X-Ray ComputedTumor BurdenPositron-Emission TomographyMultimodal Imaging

Funding

  • European Federation of Pharmaceutical Industries and Associations
  • KWF Kankerbestrijding
  • Center for Translational Molecular Medicine
  • Innovative Medicines Initiative
  • National Institutes of Health
  • Interreg
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