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dc.contributor.authorGavriel, Christos
dc.contributor.authorDimitriou, Neofytos
dc.contributor.authorBrieu, Nicolas
dc.contributor.authorNearchou, Ines P.
dc.contributor.authorArandelovic, Oggie
dc.contributor.authorSchmidt, Günter
dc.contributor.authorHarrison, David J.
dc.contributor.authorCaie, Peter D.
dc.date.accessioned2021-04-01T09:30:10Z
dc.date.available2021-04-01T09:30:10Z
dc.date.issued2021-04-01
dc.identifier.citationGavriel , C , Dimitriou , N , Brieu , N , Nearchou , I P , Arandelovic , O , Schmidt , G , Harrison , D J & Caie , P D 2021 , ' Assessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learning ' , Cancers , vol. 13 , no. 7 , 1624 . https://doi.org/10.3390/cancers13071624en
dc.identifier.issn2072-6694
dc.identifier.otherPURE: 273354070
dc.identifier.otherPURE UUID: 0a61b23d-3c1d-4e23-8c7f-a40885579ea9
dc.identifier.otherORCID: /0000-0001-9041-9988/work/91685661
dc.identifier.otherORCID: /0000-0002-0031-9850/work/91685820
dc.identifier.otherORCID: /0000-0002-1863-5413/work/91685913
dc.identifier.otherScopus: 85103320881
dc.identifier.urihttp://hdl.handle.net/10023/21752
dc.descriptionFunding: This research received financial support from Definiens GmbH and the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].en
dc.description.abstractThe clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10−5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.
dc.language.isoeng
dc.relation.ispartofCancersen
dc.rightsCopyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).en
dc.subjectImmuno-oncologyen
dc.subjectTumour microenvironmenten
dc.subjectTumour buddingen
dc.subjectPD-L1en
dc.subjectMacrophagesen
dc.subjectLymphocytesen
dc.subjectPrognosisen
dc.subjectsurvival analysisen
dc.subjectMachine learningen
dc.subjectDigital pathologyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQR180 Immunologyen
dc.subjectRB Pathologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccQR180en
dc.subject.lccRBen
dc.subject.lccRC0254en
dc.titleAssessment of immunological features in muscle-invasive bladder cancer prognosis using ensemble learningen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Centre for Biophotonicsen
dc.identifier.doihttps://doi.org/10.3390/cancers13071624
dc.description.statusPeer revieweden
dc.identifier.urlhttps://www.mdpi.com/2072-6694/13/7/1624en


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