Journal
HISTOPATHOLOGY
Volume 72, Issue 2, Pages 227-238Publisher
WILEY
DOI: 10.1111/his.13333
Keywords
automated HER2 scoring; biomarker quantification; breast cancer; digital pathology; quantitative immunohistochemistry
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Funding
- University Hospital Coventry Warwickshire (UHCW)
- Department of Computer Science at Warwick
- European Project AIDPATH [612471]
- Science Foundation Ireland (SFI) [13/CDA/2224]
- Irish Research Council (IRC) Post Graduate Scholarship
- NIHR
- Pathological Society of Great Britain and Ireland
- MRC [MR/N005953/1] Funding Source: UKRI
- Science Foundation Ireland (SFI) [13/CDA/2224] Funding Source: Science Foundation Ireland (SFI)
- Medical Research Council [MR/N005953/1] Funding Source: researchfish
- National Institute for Health Research [ACF-2009-12-003, CL-2010-12-003] Funding Source: researchfish
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AimsEvaluating 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 recognized importance as a predictive and prognostic marker in clinical practice. However, visual scoring of HER2 is subjective, and consequently prone to interobserver 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. Methods and resultsThe contest data set comprised digitized whole slide images (WSI) of sections from 86 cases of invasive breast carcinoma stained with both haematoxylin and eosin (H&E) and IHC for HER2. The contesting algorithms predicted scores of the IHC slides automatically for an unseen subset of the data set 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 versus Machine' contest for the scoring of HER2 and show that the automated methods could beat the pathology experts on this contest data set. ConclusionsThis 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.
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