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dc.contributor.authorNarayanan, PL
dc.contributor.authorRaza, SEA
dc.contributor.authorHall, AH
dc.contributor.authorMarks, JR
dc.contributor.authorKing, L
dc.contributor.authorWest, RB
dc.contributor.authorHernandez, L
dc.contributor.authorGuppy, N
dc.contributor.authorDowsett, M
dc.contributor.authorGusterson, B
dc.contributor.authorMaley, C
dc.contributor.authorHwang, ES
dc.contributor.authorYuan, Y
dc.date.accessioned2021-04-20T13:37:03Z
dc.date.available2021-04-20T13:37:03Z
dc.date.issued2021-03-01
dc.identifier.citationNPJ breast cancer, 2021, 7 (1), pp. 19 - ?
dc.identifier.issn2374-4677
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4527
dc.identifier.eissn2374-4677
dc.identifier.doi10.1038/s41523-020-00205-5
dc.description.abstractDespite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial variability within ductal carcinoma in situ (DCIS) samples and its association with progression are not well understood. To characterise tissue spatial architecture and the microenvironment of DCIS, we designed and validated a new deep learning pipeline, UNMaSk. Following automated detection of individual DCIS ducts using a new method IM-Net, we applied spatial tessellation to create virtual boundaries for each duct. To study local TIL infiltration for each duct, DRDIN was developed for mapping the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, the colocalisation of TILs with DCIS ducts was significantly lower in pure DCIS compared to adjacent DCIS, which may suggest a more inflamed tissue ecology local to DCIS ducts in adjacent DCIS cases. Our study demonstrates that technological developments in deep convolutional neural networks and digital pathology can enable an automated morphological and microenvironmental analysis of DCIS, providing a new way to study differential immune ecology for individual ducts and identify new markers of progression.
dc.formatElectronic
dc.format.extent19 - ?
dc.languageeng
dc.language.isoeng
dc.publisherNATURE PORTFOLIO
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleUnmasking the immune microecology of ductal carcinoma in situ with deep learning.
dc.typeJournal Article
dcterms.dateAccepted2020-10-21
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1038/s41523-020-00205-5
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfNPJ breast cancer
pubs.issue1
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.publication-statusPublished
pubs.volume7
pubs.embargo.termsNot known
icr.researchteamComputational Pathology & Integrated Genomics
icr.researchteamComputational Pathology & Integrated Genomics
dc.contributor.icrauthorNarayanan, Priya
dc.contributor.icrauthorYuan, Yinyin


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