(2018) Her2 Challenge Contest : a detailed assessment of automated Her2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology, 72 (2). pp. 227-238. doi:10.1111/his.13333 (In Press)

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Abstract

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 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.

Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues. Available from: https://www.researchgate.net/publication/317087832_Her2_Challenge_Contest_A_Detailed_Assessment_of_Automated_Her2_Scoring_Algorithms_in_Whole_Slide_Images_of_Breast_Cancer_Tissues [accessed Jul 31, 2017].

Item Type:	Journal Article
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Breast -- Cancer -- Histopathology
Journal or Publication Title:	Histopathology
Publisher:	Wiley-Blackwell Publishing Ltd.
ISSN:	0309-0167
Official Date:	January 2018
Dates:	Date Event January 2018 Published 3 August 2017 Available 29 July 2017 Accepted
Date of first compliant deposit:	31 July 2017
Volume:	72
Number:	2
Page Range:	pp. 227-238
DOI:	10.1111/his.13333
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Restricted or Subscription Access
Funder:	University Hospitals Coventry and Warwickshire NHS Trust, University of Warwick. Department of Computer Science, Academia and Industry Collaboration for Digital Pathology (AIDPATH), Science Foundation Ireland (SFI), Irish Research Council (IRC), National Institute for Health Research (Great Britain) (NIHR), Pathological society of Great Britain and Ireland
Grant number:	612471 (AIDPATH), 13/CDA/2224 (SFI)
Related URLs:	Other
Open Access Version:	ArXiv

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