Her2 challenge contest: a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues

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

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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.

Methods and Results

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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/963507
Additional Information: This is the peer reviewed version of the following article: Qaiser, T., Mukherjee, A., Reddy Pb, C., Munugoti, S. D., 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. O., Ilyas, M. and Rajpoot, N. (), Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues. Histopathology. Accepted Author Manuscript, which has been published in final form at http://dx.doi.org/10.1111/his.13333. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Keywords: Digital Pathology; Automated Her2 Scoring; Biomarker Quantification; Quantitative Immunohistochemistry; Breast Cancer
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Division of Cancer and Stem Cells
Identification Number: https://doi.org/10.1111/his.13333
Depositing User: Eprints, Support
Date Deposited: 15 Sep 2017 11:46
Last Modified: 04 May 2020 19:53
URI: https://eprints.nottingham.ac.uk/id/eprint/46432

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