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dc.contributor.authorAbubakar, M
dc.contributor.authorHowat, WJ
dc.contributor.authorDaley, F
dc.contributor.authorZabaglo, L
dc.contributor.authorMcDuffus, LA
dc.contributor.authorBlows, F
dc.contributor.authorCoulson, P
dc.contributor.authorRaza, Ali H
dc.contributor.authorBenitez, J
dc.contributor.authorMilne, R
dc.contributor.authorBrenner, H
dc.contributor.authorStegmaier, C
dc.contributor.authorMannermaa, A
dc.contributor.authorChang-Claude, J
dc.contributor.authorRudolph, A
dc.contributor.authorSinn, P
dc.contributor.authorCouch, FJ
dc.contributor.authorTollenaar, RA
dc.contributor.authorDevilee, P
dc.contributor.authorFigueroa, J et al
dc.date.accessioned2017-02-16T08:16:15Z
dc.date.available2017-02-16T08:16:15Z
dc.date.issued2016
dc.identifier10.1002/cjp2.42fi_FI
dc.identifier.issn2056-4538
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/288
dc.descriptionArticle
dc.description.abstractAutomated methods are needed to facilitate high-throughput and reproducible scoring of Ki67 and other markers in breast cancer tissue microarrays (TMAs) in large-scale studies. To address this need, we developed an automated protocol for Ki67 scoring and evaluated its performance in studies from the Breast Cancer Association Consortium. We utilized 166 TMAs containing 16,953 tumour cores representing 9,059 breast cancer cases, from 13 studies, with information on other clinical and pathological characteristics. TMAs were stained for Ki67 using standard immunohistochemical procedures, and scanned and digitized using the Ariol system. An automated algorithm was developed for the scoring of Ki67, and scores were compared to computer assisted visual (CAV) scores in a subset of 15 TMAs in a training set. We also assessed the correlation between automated Ki67 scores and other clinical and pathological characteristics. Overall, we observed good discriminatory accuracy (AUC = 85%) and good agreement (kappa = 0.64) between the automated and CAV scoring methods in the training set. The performance of the automated method varied by TMA (kappa range= 0.37–0.87) and study (kappa range = 0.39–0.69). The automated method performed better in satisfactory cores (kappa = 0.68) than suboptimal (kappa = 0.51) cores (p-value for comparison = 0.005); and among cores with higher total nuclei counted by the machine (4,000–4,500 cells: kappa = 0.78) than those with lower counts (50–500 cells: kappa = 0.41; p-value = 0.010). Among the 9,059 cases in this study, the correlations between automated Ki67 and clinical and pathological characteristics were found to be in the expected directions. Our findings indicate that automated scoring of Ki67 can be an efficient method to obtain good quality data across large numbers of TMAs from multicentre studies. However, robust algorithm development and rigorous pre- and post-analytical quality control procedures are necessary in order to ensure satisfactory performance.fi_FI
dc.language.isoENGfi_FI
dc.publisherThe Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltdfi_FI
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/223175
dc.relation.ispartofseriesJournal of Patology: Clinical Research
dc.relation.urihttp://doi.org/10.1002/cjp2.42fi_FI
dc.rightsCC BY http://creativecommons.org/licenses/by/4.0/
dc.subjectbreast cancerfi_FI
dc.subjectautomated algorithmfi_FI
dc.subjecttissue microarraysfi_FI
dc.subjectKi67fi_FI
dc.subjectimmunohistochemistryfi_FI
dc.titleHigh-throughput automated scoring of Ki67 in breast cancer tissue microarrays from the Breast Cancer Association Consortiumfi_FI
dc.typehttp://purl.org/eprint/type/JournalArticle
dc.description.versionPublisher's pdffi_FI
dc.contributor.departmentSchool of Medicine / Clinical Medicine
uef.solecris.id41766125
eprint.statushttp://purl.org/eprint/status/PeerReviewedfi_FI
dc.type.publicationinfo:eu-repo/semantics/article
dc.rights.accessrights© Authors
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/223175
uef.citationinfo.issue3
uef.citationinfo.pages138–153
dc.relation.doihttp://doi.org/10.1002/cjp2.42
dc.description.reviewstatushttp://purl.org/eprint/status/PeerReviewed
dc.format.pagerange138–153
dc.relation.issn2056-4538
dc.relation.issue3
dc.relation.volume2
dc.relation.volume2
dc.rights.accesslevelopenAccess


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