A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, w...

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Detalles Bibliográficos
Autores principales: Leger, Stefan, Zwanenburg, Alex, Pilz, Karoline, Lohaus, Fabian, Linge, Annett, Zöphel, Klaus, Kotzerke, Jörg, Schreiber, Andreas, Tinhofer, Inge, Budach, Volker, Sak, Ali, Stuschke, Martin, Balermpas, Panagiotis, Rödel, Claus, Ganswindt, Ute, Belka, Claus, Pigorsch, Steffi, Combs, Stephanie E., Mönnich, David, Zips, Daniel, Krause, Mechthild, Baumann, Michael, Troost, Esther G. C., Löck, Steffen, Richter, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643429/
https://www.ncbi.nlm.nih.gov/pubmed/29038455
http://dx.doi.org/10.1038/s41598-017-13448-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643429/
https://www.ncbi.nlm.nih.gov/pubmed/29038455
http://dx.doi.org/10.1038/s41598-017-13448-3

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