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dc.contributor.authorAlataki, A
dc.contributor.authorZabaglo, L
dc.contributor.authorTovey, H
dc.contributor.authorDodson, A
dc.contributor.authorDowsett, M
dc.date.accessioned2021-07-29T09:49:39Z
dc.date.available2021-07-29T09:49:39Z
dc.identifier.citationHistopathology, 2021, 79 (2), pp. 200 - 209
dc.identifier.issn0309-0167
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4707
dc.identifier.eissn1365-2559
dc.identifier.eissn1365-2559
dc.identifier.eissn1365-2559en_US
dc.identifier.doi10.1111/his.14355
dc.identifier.doi10.1111/his.14355
dc.identifier.doi10.1111/his.14355en_US
dc.description.abstractAims Ki67 is a well-established immunohistochemical marker associated with cell proliferation that has prognostic and predictive value in breast cancer. Quantitative evaluation of Ki67 is traditionally performed by assessing stained tissue slides with light microscopy. Automated image analysis systems have become available and, if validated, could provide greater standardisation and improved precision of Ki67 scoring. Here, we aimed to evaluate the use of the Cognition Master Professional Suite (CogM) image analysis software, which is a simple system for scoring Ki67 in primary breast cancer samples.Methods and results Sections from 94 core-cut biopsies, 20 excision specimens and 29 pairs of core-cut biopsies and excision specimens were stained for Ki67 with MIB1 antibody and the Dako EnVision FLEX Detection System. Stained slides were scanned to convert them to digital data. Computer-based Ki67 scoring was performed with CogM. Manual Ki67 scoring assessment was conducted on previously stained sections from the same biopsies with a clinically validated system that had been calibrated against the risk of recurrence. A high correlation between manual and digital scores was observed [r<sub>Cores</sub>  = 0.92, 95% confidence interval (CI) 0.87-0.94, P < 0.0001; r<sub>Excisions</sub>  = 0.95, 95% CI 0.86-0.98, P < 0.0001] and there was no significant bias between them (P = 0.45). There was also a high correlation of Ki67 scores between paired core-cut biopsies and excision specimens when CogM was used (r = 0.78, 95% CI 0.59-0.89, P < 0.0001).Conclusions CogM image analysis allows for standardised automated Ki67 scoring that accurately replicates previously clinically validated and calibrated manual scores.
dc.formatPrint-Electronic
dc.format.extent200 - 209
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA simple digital image analysis system for automated Ki67 assessment in primary breast cancer.
dc.typeJournal Article
dcterms.dateAccepted2021-02-14
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1111/his.14355
dc.relation.isPartOfHistopathology
pubs.issue2
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/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology/Endocrinology (hon.)
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/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology/Endocrinology (hon.)
pubs.publication-statusAccepted
pubs.volume79en_US
pubs.embargo.termsNot known
icr.researchteamEndocrinology
icr.researchteamEndocrinologyen_US
dc.contributor.icrauthorDowsett, Mitchen


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