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dc.contributor.authorBrieu, Nicolas
dc.contributor.authorGavriel, Christos
dc.contributor.authorNearchou, Ines P.
dc.contributor.authorHarrison, David James
dc.contributor.authorSchmidt, Günter
dc.contributor.authorCaie, Peter David
dc.identifier.citationBrieu , N , Gavriel , C , Nearchou , I P , Harrison , D J , Schmidt , G & Caie , P D 2019 , ' Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis ' , Scientific Reports , vol. 9 , 5174 .
dc.identifier.otherORCID: /0000-0002-0031-9850/work/60196551
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034266
dc.identifier.otherORCID: /0000-0002-1863-5413/work/75610530
dc.description.abstractTumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.
dc.relation.ispartofScientific Reportsen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleAutomated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosisen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews. Sir James Mackenzie Institute for Early Diagnosisen
dc.description.statusPeer revieweden

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