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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Published in:Sci Rep
Main Authors: 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
Format: Artigo
Language:Inglês
Published: Nature Publishing Group UK 2017
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Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC5643429/
https://ncbi.nlm.nih.gov/pubmed/29038455
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-017-13448-3
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