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; Pitk�aho, Tomi; Lehtim�ki, Taina; Naughton, Thomas; Berseth, Matt; Pedraza, An�bal; Mukundan, Ramakrishnan; Smith, Matthew; Bhalerao, Abhir; Rodner, Erik; Simon, Marcel; Denzler, Joachim; Huang, Chao-Hui; Bueno, Gloria; Snead, David; Ellis, Ian O; Ilyas, Mohammad; Rajpoot, Nasir

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 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.
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.

Citation

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., …Rajpoot, N. (2018). HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology, 72(2), 227-238. https://doi.org/10.1111/his.13333

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2017
• Online Publication Date: Aug 3, 2017
• Publication Date: Jan 1, 2018
• Deposit Date: Sep 15, 2017
• Publicly Available Date: Sep 15, 2017
• Journal: Histopathology
• Print ISSN: 0309-0167
• Electronic ISSN: 1365-2559
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 72
• Issue: 2
• Pages: 227-238
• DOI: https://doi.org/10.1111/his.13333
• Keywords: Digital Pathology; Automated Her2 Scoring; Biomarker Quantification; Quantitative Immunohistochemistry; Breast Cancer
• Public URL: https://nottingham-repository.worktribe.com/output/963507
• Publisher URL: http://onlinelibrary.wiley.com/doi/10.1111/his.13333/abstract
• 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.
• Contract Date: Sep 15, 2017