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dc.contributor.authorAli, HR
dc.contributor.authorIrwin, M
dc.contributor.authorMorris, L
dc.contributor.authorDawson, S-J
dc.contributor.authorBlows, FM
dc.contributor.authorProvenzano, E
dc.contributor.authorMahler-Araujo, B
dc.contributor.authorPharoah, PD
dc.contributor.authorWalton, NA
dc.contributor.authorBrenton, JD
dc.contributor.authorCaldas, C
dc.date.accessioned2020-12-22T04:44:23Z
dc.date.available2020-12-22T04:44:23Z
dc.date.issued2013-02-19
dc.identifierpii: bjc2012558
dc.identifier.citationAli, H. R., Irwin, M., Morris, L., Dawson, S. -J., Blows, F. M., Provenzano, E., Mahler-Araujo, B., Pharoah, P. D., Walton, N. A., Brenton, J. D. & Caldas, C. (2013). Astronomical algorithms for automated analysis of tissue protein expression in breast cancer. BRITISH JOURNAL OF CANCER, 108 (3), pp.602-612. https://doi.org/10.1038/bjc.2012.558.
dc.identifier.issn0007-0920
dc.identifier.urihttp://hdl.handle.net/11343/258131
dc.description.abstractBACKGROUND: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress. METHODS: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists. RESULTS: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%. CONCLUSION: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.
dc.languageEnglish
dc.publisherNATURE PUBLISHING GROUP
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0
dc.titleAstronomical algorithms for automated analysis of tissue protein expression in breast cancer
dc.typeJournal Article
dc.identifier.doi10.1038/bjc.2012.558
melbourne.affiliation.departmentSir Peter MacCallum Department of Oncology
melbourne.source.titleBritish Journal of Cancer
melbourne.source.volume108
melbourne.source.issue3
melbourne.source.pages602-612
dc.rights.licenseCC BY-NC-SA
melbourne.elementsid1186371
melbourne.contributor.authorDawson, Sarah-Jane
dc.identifier.eissn1532-1827
melbourne.accessrightsOpen Access


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