Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues

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dc.contributor.authorQaiser T
dc.contributor.authorMukherjee A
dc.contributor.authorPb CR
dc.contributor.authorMunugoti SD
dc.contributor.authorTallam V
dc.contributor.authorPitkäaho T
dc.contributor.authorLehtimäki T
dc.contributor.authorNaughton T
dc.contributor.authorBerseth M
dc.contributor.authorPedraza A
dc.contributor.authorMukundan R
dc.contributor.authorSmith M
dc.contributor.authorBhalerao A
dc.contributor.authorRodner E
dc.contributor.authorSimon M
dc.contributor.authorDenzler J
dc.contributor.authorHuang C-H
dc.contributor.authorBueno G
dc.contributor.authorSnead D
dc.contributor.authorEllis I
dc.contributor.authorIlyas M
dc.contributor.authorRajpoot N
dc.date.accessioned2019-11-01T01:46:25Z
dc.date.available2019-11-01T01:46:25Z
dc.date.issued2018en
dc.date.updated2017-07-03T23:23:46Z
dc.description.abstractEvaluating expression of the Human epidermal growth factor receptor 2 (Her2) by visual examination of immunohistochemistry (IHC) on invasive breast cancer (BCa) is a key part of the diagnostic assessment of BCa due to its recognised importance as a predictive and prognostic marker in clinical practice. However, visual scoring of Her2 is subjective and consequently prone to inter-observer variability. Given the prognostic and therapeutic implications of Her2 scoring, a more objective method is required. In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring. The contest dataset comprised of digitised whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both Haematoxylin & Eosin (H&E) and IHC for Her2. The contesting algorithms automatically predicted scores of the IHC slides for an unseen subset of the dataset and the predicted scores were compared with the 'ground truth' (a consensus score from at least two experts). We also report on a simple Man vs Machine contest for the scoring of Her2 and show that the automated methods could beat the pathology experts on this contest dataset. This paper presents a benchmark for comparing the performance of automated algorithms for scoring of Her2. It also demonstrates the enormous potential of automated algorithms in assisting the pathologist with objective IHC scoring.en
dc.identifier.citationQaiser T, Mukherjee A, Pb CR, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A, Mukundan R, Smith M, Bhalerao A, Rodner E, Simon M, Denzler J, Huang C-H, Bueno G, Snead D, Ellis I, Ilyas M, Rajpoot N Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues. Histopathology. 2018 Jan;72(2):227-238en
dc.identifier.doihttps://doi.org/10.1111/his.13333
dc.identifier.urihttp://hdl.handle.net/10092/17531
dc.language.isoen
dc.subject.anzsrcField of Research::08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processingen
dc.subject.anzsrcField of Research::11 - Medical and Health Sciences::1112 - Oncology and Carcinogenesisen
dc.titleHer2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissuesen
dc.typeJournal Articleen
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