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Histopathology. 2018 Jan;72(2):227-238. doi: 10.1111/his.13333. Epub 2017 Oct 27.

HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues.

Author information

1
Department of Computer Science, University of Warwick, Coventry, UK.
2
Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.
3
Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, India.
4
Department of Computer Science, Maynooth University, Maynooth, Ireland.
5
NLP Logix LLC, Jacksonville, FL, USA.
6
VISILAB, E.T.S.I.I, University of Castilla-La Mancha, Ciudad Real, Spain.
7
Department of Computer Science and Software Engineering, University of Canterbury, Canterbury, New Zealand.
8
Department of Statistics, University of Warwick, Coventry, UK.
9
Computer Vision Group, Friedrich Schiller University of Jena, Jena, Germany.
10
MSD International GmbH, Singapore, Singapore.
11
Singapore Agency for Science, Technology and Research, Singapore, Singapore.
12
Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
13
Nottingham Molecular Pathology Node, University of Nottingham, Nottingham, UK.

Abstract

AIMS:

Evaluating 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 RESULTS:

The 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.

CONCLUSIONS:

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.

KEYWORDS:

automated HER2 scoring; biomarker quantification; breast cancer; digital pathology; quantitative immunohistochemistry

PMID:
28771788
DOI:
10.1111/his.13333
[Indexed for MEDLINE]

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