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dc.contributor.authorSobhani, F
dc.contributor.authorRobinson, R
dc.contributor.authorHamidinekoo, A
dc.contributor.authorRoxanis, I
dc.contributor.authorSomaiah, N
dc.contributor.authorYuan, Y
dc.date.accessioned2021-02-22T09:58:24Z
dc.date.available2021-02-22T09:58:24Z
dc.date.issued2021-02-06
dc.identifier.citationBiochimica et biophysica acta. Reviews on cancer, 2021, pp. 188520 - ?en_US
dc.identifier.issn0304-419X
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4352
dc.identifier.eissn1879-2561en_US
dc.identifier.eissn1879-2561
dc.identifier.doi10.1016/j.bbcan.2021.188520en_US
dc.identifier.doi10.1016/j.bbcan.2021.188520
dc.description.abstractThe field of immuno-oncology has expanded rapidly over the past decade, but key questions remain. How does tumour-immune interaction regulate disease progression? How can we prospectively identify patients who will benefit from immunotherapy? Identifying measurable features of the tumour immune-microenvironment which have prognostic or predictive value will be key to making meaningful gains in these areas. Recent developments in deep learning enable a big-data analysis of pathological samples. Digital approaches allow data to be acquired, integrated and analysed far beyond what is possible with conventional techniques, and to do so efficiently and at scale. This has the potential to reshape what can be achieved in terms of volume, precision and reliability of output, enabling data for large cohorts to be summarised and compared. This review examines applications of Artificial intelligence (AI) to important questions in Immuno-oncology (IO). We discuss general considerations that need to be taken into account before AI can be applied in any clinical setting. We describe AI methods that have been applied to the field of IO to date and present several examples of their use.en_US
dc.formatPrint-Electronicen_US
dc.format.extent188520 - ?en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.titleArtificial intelligence and digital pathology: Opportunities and implications for immuno-oncology.en_US
dc.typeJournal Article
dcterms.dateAccepted2021-01-30
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1016/j.bbcan.2021.188520en_US
rioxxterms.licenseref.startdate2021-02-06
dc.relation.isPartOfBiochimica et biophysica acta. Reviews on canceren_US
pubs.notesNo embargoen_US
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/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Translational Breast Radiobiology
pubs.publication-statusPublisheden_US
pubs.embargo.termsNo embargoen_US
icr.researchteamComputational Pathology & Integrated Genomics
icr.researchteamTranslational Breast Radiobiology
dc.contributor.icrauthorSomaiah, Navitaen_US
dc.contributor.icrauthorRoxanis, Ioannisen_US
dc.contributor.icrauthorSobhani, Faranaken_US
dc.contributor.icrauthorHamidinekoo, Azamen_US


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