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

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 radiomi...

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Detalles Bibliográficos
Autores principales: Parmar, Chintan, Grossmann, Patrick, Rietveld, Derek, Rietbergen, Michelle M., Lambin, Philippe, Aerts, Hugo J. W. L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668290/
https://www.ncbi.nlm.nih.gov/pubmed/26697407
http://dx.doi.org/10.3389/fonc.2015.00272

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4668290/
https://www.ncbi.nlm.nih.gov/pubmed/26697407
http://dx.doi.org/10.3389/fonc.2015.00272

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