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dc.contributor.authorLustberg, Timen_US
dc.contributor.authorBailey, Michaelen_US
dc.contributor.authorThwaites, David I.en_US
dc.contributor.authorMiller, Alexisen_US
dc.contributor.authorCarolan, Martinen_US
dc.contributor.authorHolloway, Loisen_US
dc.contributor.authorVelazquez, Emmanuel Riosen_US
dc.contributor.authorHoebers, Franken_US
dc.contributor.authorDekker, Andreen_US
dc.date.accessioned2016-12-02T15:24:46Z
dc.date.issued2016en_US
dc.identifier.citationLustberg, Tim, Michael Bailey, David I. Thwaites, Alexis Miller, Martin Carolan, Lois Holloway, Emmanuel Rios Velazquez, Frank Hoebers, and Andre Dekker. 2016. “Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting.” Oncotarget 7 (24): 37288-37296. doi:10.18632/oncotarget.8755. http://dx.doi.org/10.18632/oncotarget.8755.en
dc.identifier.issn1949-2553en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:29626080
dc.description.abstractBackground and Purpose To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset. Materials and Methods Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre's (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort). Results: Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so. Conclusions: The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.en
dc.language.isoen_USen
dc.publisherImpact Journals LLCen
dc.relation.isversionofdoi:10.18632/oncotarget.8755en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095076/pdf/en
dash.licenseLAAen_US
dc.subjectlarynxen
dc.subjectsurvival predictionen
dc.subjectrapid learningen
dc.subjectmodel validationen
dc.titleImplementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical settingen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalOncotargeten
dc.date.available2016-12-02T15:24:46Z
dc.identifier.doi10.18632/oncotarget.8755*


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