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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Xehetasun bibliografikoak
Argitaratua izan da:Sci Rep
Egile Nagusiak: 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
Formatua: Artigo
Hizkuntza:Inglês
Argitaratua: Nature Publishing Group UK 2017
Gaiak:
Article
Sarrera elektronikoa: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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