Talha Qaiser1, Abhik Mukherjee2, Chaitanya Reddy Pb3, Sai D Munugoti3, Vamsi Tallam3, Tomi Pitkäaho4, Taina Lehtimäki4, Thomas Naughton4, Matt Berseth5, Aníbal Pedraza6, Ramakrishnan Mukundan7, Matthew Smith8, Abhir Bhalerao1, Erik Rodner9, Marcel Simon9, Joachim Denzler9, Chao-Hui Huang10,11, Gloria Bueno6, David Snead12, Ian O Ellis2, Mohammad Ilyas2,13, Nasir Rajpoot1,12. 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.
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.
Authors: Robert Pell; Karin Oien; Max Robinson; Helen Pitman; Nasir Rajpoot; Jens Rittscher; David Snead; Clare Verrill Journal: J Pathol Clin Res Date: 2019-03-25
Authors: Ahmed Serag; Adrian Ion-Margineanu; Hammad Qureshi; Ryan McMillan; Marie-Judith Saint Martin; Jim Diamond; Paul O'Reilly; Peter Hamilton Journal: Front Med (Lausanne) Date: 2019-10-01
Authors: Esther Abels; Liron Pantanowitz; Famke Aeffner; Mark D Zarella; Jeroen van der Laak; Marilyn M Bui; Venkata Np Vemuri; Anil V Parwani; Jeff Gibbs; Emmanuel Agosto-Arroyo; Andrew H Beck; Cleopatra Kozlowski Journal: J Pathol Date: 2019-09-03 Impact factor: 7.996