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dc.contributor.authorNearchou, Ines P.
dc.contributor.authorLillard, Kate
dc.contributor.authorGavriel, Christos
dc.contributor.authorUeno, Hideki
dc.contributor.authorHarrison, David J.
dc.contributor.authorCaie, Peter D.
dc.identifier.citationNearchou , I P , Lillard , K , Gavriel , C , Ueno , H , Harrison , D J & Caie , P D 2019 , ' Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer ' , Cancer Immunology Research , vol. 7 , no. 4 , pp. 609-620 .
dc.identifier.otherPURE: 257559242
dc.identifier.otherPURE UUID: 01b3d836-e67d-4c0e-bf58-453f7a8b2bce
dc.identifier.otherPubMed: 30846441
dc.identifier.otherScopus: 85064198559
dc.identifier.otherORCID: /0000-0002-0031-9850/work/60196562
dc.identifier.otherWOS: 000462983600009
dc.identifier.otherORCID: /0000-0001-9041-9988/work/64034360
dc.identifier.otherORCID: /0000-0002-1863-5413/work/75610534
dc.descriptionFunding: Medical Research Scotland, and Indica Labs, Inc. provided in-kind resource.en
dc.description.abstractBoth immune profiling and tumor budding significantly correlate with colorectal cancer (CRC) patient outcome, but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II CRC. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3+CD8+ T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: n = 114, validation cohort 1: n = 56, validation cohort 2: n = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs (HR = 5.899, 95% CI, 1.875 - 18.55), low CD3+ 11 T cell density (HR = 9.964, 95% CI 3.156 - 31.46), and low mean number of CD3+CD8+ T cells within 50 μm of TBs (HR = 8.907, 95% CI 2.834 - 28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75, 95% CI 6.46–54.43), than TBs, Immunoscore, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27, 95% CI 3.524–42.73, HR = 15.61, 95% CI 4.692-51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II CRC through an automated image analysis and machine learning workflow.
dc.relation.ispartofCancer Immunology Researchen
dc.rightsCopyright © 2019 American Association for Cancer Research. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at
dc.subjectDigital image analysisen
dc.subjectDigital pathologyen
dc.subjectTumor buddingen
dc.subjectColorectal canceren
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRB Pathologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.titleAutomated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal canceren
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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