Qaiser, Talha and Mukherjee, Abhik and Reddy P.B., Chaitanya and Munugoti, Sai D. and Tallam, Vamsi and Pitkäaho, Tomi and Lehtimäki, Taina M. and Naughton, Thomas J. and Berseth, Matt and Pedraza, Aníbal and Mukundan, Ramakrishnan and Smith, Matthew and Bhalerao, Abhir and Rodner, Erik and Simon, Marcel and Denzler, Joachim and Huang, Chao-Hui and Bueno, Gloria and Snead, David and Ellis, Ian O. and Ilyas, Mohammad and Rajpoot, Nasir (2017) 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. ISSN 1365-2559

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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.

Item Type:	Article
Keywords:	automated HER2 scoring; biomarker quantification; breast cancer; digital pathology; quantitative immunohistochemistry;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	12378
Identification Number:	https://doi.org/10.1111/his.13333
Depositing User:	Thomas Naughton
Date Deposited:	07 Feb 2020 16:16
Journal or Publication Title:	Histopathology
Publisher:	Wiley
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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