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dc.contributor.authorSirinukunwattana, Korsuk
dc.contributor.authorDomingo, Enric
dc.contributor.authorRichman, Susan
dc.contributor.authorRedmond, Keara L
dc.contributor.authorBlake, Andrew
dc.contributor.authorVerrill, Clare
dc.contributor.authorLeedham, Simon J.
dc.contributor.authorChatzipli, Aikaterini
dc.contributor.authorHardy, Claire
dc.contributor.authorWhalley, Celina
dc.contributor.authorWu, Chieh-Hsi
dc.contributor.authorBeggs, Andrew D.
dc.contributor.authorMcDermott, Ultan
dc.contributor.authorDunne, Philip D.
dc.contributor.authorMeade, Angela A
dc.contributor.authorWalker, Steven M
dc.contributor.authorMurray, Graeme
dc.contributor.authorSamuel, Leslie M.
dc.contributor.authorSeymour, Matthew
dc.contributor.authorTomlinson, Ian
dc.contributor.authorQuirke, Philip
dc.contributor.authorMaughan, Tim
dc.contributor.authorRittscher, Jens
dc.contributor.authorKoelzer, Viktor H.
dc.contributor.authorThe S:CORT Consortium
dc.date.accessioned2021-03-03T22:16:01Z
dc.date.available2021-03-03T22:16:01Z
dc.date.issued2021-03-01
dc.identifier.citationSirinukunwattana , K , Domingo , E , Richman , S , Redmond , K L , Blake , A , Verrill , C , Leedham , S J , Chatzipli , A , Hardy , C , Whalley , C , Wu , C-H , Beggs , A D , McDermott , U , Dunne , P D , Meade , A A , Walker , S M , Murray , G , Samuel , L M , Seymour , M , Tomlinson , I , Quirke , P , Maughan , T , Rittscher , J , Koelzer , V H & The S:CORT Consortium 2021 , ' Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning ' , Gut , vol. 70 , pp. 544-554 . https://doi.org/10.1136/gutjnl-2019-319866en
dc.identifier.issn0017-5749
dc.identifier.otherPURE: 171081585
dc.identifier.otherPURE UUID: 5e92653f-57ed-4a9b-a361-4426e8c9a7ae
dc.identifier.otherPubMed: 32690604
dc.identifier.otherORCID: /0000-0003-3981-2420/work/78069039
dc.identifier.otherScopus: 85088693307
dc.identifier.otherORCID: /0000-0002-8402-8670/work/79064867
dc.identifier.urihttps://hdl.handle.net/2164/15965
dc.descriptionAcknowledgements The authors thank Aurelien de Reynies for advice on CMS calling in FFPE blocks, Claire Butler and Michael Youdell for excellent managing in S:CORT and the MRC Clinical Trials Unit who provided the clinical data from the FOCUS trial with permission from the FOCUS trial steering group. We would further like to thank Indica Labs for providing the HALO software. GRANT SUPPORT The S:CORT consortium is a Medical Research Council stratified medicine consortium jointly funded by the MRC and CRUK. This work was further supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. Jens Rittscher is supported through the EPSRC funded Seebibyte programme (EP/M013774/1). Viktor H Koelzer gratefully acknowledges funding by the Swiss National Science Foundation (P2SKP3_168322/1 and P2SKP3_168322/2), and the Promedica Foundation F-87701-41-01. The authors thank Aurelien de Reynies for advice on CMS calling in FFPE blocks, Claire Butler and Michael Youdell for excellent managing in S:CORT and the MRC Clinical Trials Unit who provided the clinical data from the FOCUS trial with permission from the FOCUS trial steering group. We would further like to thank Indica Labs for providing the HALOTM software. The results published or shown here based in part upon data generated by the TCGA Research Network established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome.nih.gov. We would specifically like to thank all patients who have consented to take part in S:CORT and TCGA. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.en
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofGuten
dc.rightsRe-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made.en
dc.subjectR Medicine (General)en
dc.subjectGastroenterologyen
dc.subjectEngineering and Physical Sciences Research Council (EPSRC)en
dc.subjectEP/M013774/1en
dc.subjectMedical Research Council (MRC)en
dc.subjectCancer Research UKen
dc.subjectNational Institute for Health Research (NIHR)en
dc.subjectSupplementary Dataen
dc.subject.lccR1en
dc.titleImage-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learningen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Applied Medicineen
dc.contributor.institutionUniversity of Aberdeen.Clinical Medicineen
dc.contributor.institutionUniversity of Aberdeen.Institute of Medical Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Health Services Research Uniten
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.1136/gutjnl-2019-319866
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85088693307&partnerID=8YFLogxKen


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