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dc.contributor.authorDimitriou, Neofytos
dc.contributor.authorArandelovic, Ognjen
dc.contributor.authorHarrison, David James
dc.contributor.authorCaie, Peter David
dc.date.accessioned2018-10-09T11:30:06Z
dc.date.available2018-10-09T11:30:06Z
dc.date.issued2018-10-02
dc.identifier.citationDimitriou , N , Arandelovic , O , Harrison , D J & Caie , P D 2018 , ' A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis ' , npj Digital Medicine , vol. 1 , 52 . https://doi.org/10.1038/s41746-018-0057-xen
dc.identifier.issn2398-6352
dc.identifier.otherPURE: 255731693
dc.identifier.otherPURE UUID: b9222836-116f-459a-86f8-879dba6a752a
dc.identifier.otherORCID: /0000-0002-0031-9850/work/60196547
dc.identifier.otherWOS: 000449683300003
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034186
dc.identifier.urihttp://hdl.handle.net/10023/16174
dc.description.abstractAccurate prognosis is fundamental in planning an appropriate therapy for cancer patients. Consequent to the heterogeneity of the disease, intra- and inter-pathologist variability, and the inherent limitations of current pathological reporting systems, patient outcome varies considerably within similarly staged patient cohorts. This is particularly true when classifying stage IIcolorectal cancer patients using the current TNM guidelines. The aim of the present work is to address this problem through the use of machine learning. In particular, we introduce a novel, data driven framework which makes use of a large number of diverse types of features, readily collected from immunofluorescence imagery. Its outstanding performance in predictingmortality in stage II patients (AUROC= 0:94), exceeds that of current clinical guidelines such as pT stage (AUROC= 0:65), and is demonstrated on a cohort of 173 colorectal cancer patients.
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofnpj Digital Medicineen
dc.rights© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccRC0254en
dc.titleA principled machine learning framework improves accuracy of stage II colorectal cancer prognosisen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1038/s41746-018-0057-x
dc.description.statusPeer revieweden


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