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dc.contributor.authorCaie, Peter David
dc.contributor.authorZhou, Ying
dc.contributor.authorTurnbull, Arran
dc.contributor.authorOniscu, Anca
dc.contributor.authorHarrison, David James
dc.date.accessioned2016-06-15T12:30:03Z
dc.date.available2016-06-15T12:30:03Z
dc.date.issued2016-06-15
dc.identifier.citationCaie , P D , Zhou , Y , Turnbull , A , Oniscu , A & Harrison , D J 2016 , ' Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting ' , Oncotarget , vol. 7 , no. 28 , pp. 44381-44394 . https://doi.org/10.18632/oncotarget.10053en
dc.identifier.issn1949-2553
dc.identifier.otherPURE: 243265325
dc.identifier.otherPURE UUID: 4ac27edd-eb54-401e-a3b8-6450faf0dc9f
dc.identifier.otherScopus: 84978771247
dc.identifier.otherORCID: /0000-0002-0031-9850/work/60196549
dc.identifier.otherWOS: 000385395700114
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034210
dc.identifier.urihttps://hdl.handle.net/10023/8989
dc.descriptionFunding for the study was provided by the NHS Lothian Health Board.en
dc.description.abstractA number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n=50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n=134) (HR = 4; 95% CI, 1.5-11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3-18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7-10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death.
dc.format.extent14
dc.language.isoeng
dc.relation.ispartofOncotargeten
dc.rightsAll site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Licenseen
dc.subjectDigital pathologyen
dc.subjectBig-dataen
dc.subjectTumor budsen
dc.subjectPoorly differentiated clustersen
dc.subjectData miningen
dc.subjectRB Pathologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectNDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccRBen
dc.subject.lccRC0254en
dc.titleNovel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reportingen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.identifier.doihttps://doi.org/10.18632/oncotarget.10053
dc.description.statusPeer revieweden


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