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International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis

International Workshop on Computing and Visualization for Intravascular Imaging

International Workshop on Computer Assisted Stenting

LABELS 2018, CVII 2018, STENT 2018: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis pp 45–54Cite as

Generation of a HER2 Breast Cancer Gold-Standard Using Supervised Learning from Multiple Experts

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11043))

Abstract

Breast cancer is one of the most common cancer in women around the world. For diagnosis, pathologists evaluate the expression of biomarkers such as HER2 protein using immunohistochemistry over tissue extracted by a biopsy. This assessment is performed through microscopic inspection, estimating intensity and integrity of the membrane cells’s staining and scoring the sample as 0 (negative), 1+, 2+, or 3+ (positive): a subjective decision that depends on the interpretation of the pahologist.

This work is aimed to achieve consensus among opinions of pathologists in cases of HER2 breast cancer biopsies, using supervised learning methods based on multiple experts. The main goal is to generate a reliable public breast cancer gold-standard, to be used as training/testing dataset in future developments of machine learning methods for automatic HER2 overexpression assessment.

There were collected 30 breast cancer biopsies, with positive and negative diagnosis, where tumor regions were marked as regions-of-interest (ROIs). Magnification of \(20\times \) was used to crop non-overlapping rectangular sections according to a grid over the ROIs, leading a dataset with 1.250 images.

In order to collect the pathologists’ opinions, an Android application was developed. The biopsy sections are presented in a random way, and for each image, the expert must assign a score (0, 1+, 2+, 3+). Currently, six referent Chilean breast cancer pathologists are working on the same set of samples.

Getting the pathologists’ acceptance was a hard and time consuming task. Even more, obtaining the scoring of pathologists is a task that requires subtlety communication and time to manage their progress in the use of the application.

Supported by FONDECYT 3160559.

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Acknowledgements

Violeta Chang thanks pathologists M.D. Fernando Gabler, M.D. Valeria Cornejo, M.D. Leonor Moyano, M.D. Ivan Gallegos, M.D. Gonzalo De Toro and M.D. Claudia Ramis for their willing collaboration in the manual scoring of breast cancer biopsy sections. The author thanks Jimena Lopez for support with cancer tissue digitalization and the Biobank of Tissues and Fluids of the University of Chile for support with the collection of cancer biopsies. This research is funded by FONDECYT 3160559.

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Authors and Affiliations

  1. Laboratory for Scientific Image Analysis SCIANLab, Anatomy and Developmental Biology Department, Faculty of Medicine, University of Chile, Av Independencia 1027, Block A, 2nd Floor, Independencia, Santiago, Chile

    Violeta Chang

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  1. Violeta Chang
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Correspondence to Violeta Chang .

Editor information

Editors and Affiliations

  1. University College London, London, UK

    Danail Stoyanov

  2. University of Leeds, Leeds, UK

    Zeike Taylor

  3. University of Barcelona, Barcelona, Spain

    Simone Balocco

  4. University of Bern, Bern, Switzerland

    Raphael Sznitman

  5. Sunnybrook Health Science Centre, Toronto, ON, Canada

    Anne Martel

  6. German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany

    Lena Maier-Hein

  7. Ecole de Technologie Superieure, Montreal, QC, Canada

    Luc Duong

  8. Technical University of München, Garching, Bayern, Germany

    Guillaume Zahnd

  9. Technical University Munich, Munich, Germany

    Stefanie Demirci

  10. Technical University of München, Garching, Bayern, Germany

    Shadi Albarqouni

  11. Imperial College London, London, UK

    Su-Lin Lee

  12. University College London, London, UK

    Stefano Moriconi

  13. Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina

  14. Technical University of Munich, Garching, Germany

    Diana Mateus

  15. University of Dundee, Dundee, UK

    Emanuele Trucco

  16. École de Technologie Supérieure, Montréal, QC, Canada

    Eric Granger

  17. Universite de Rennes 1, Rennes, France

    Pierre Jannin

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Chang, V. (2018). Generation of a HER2 Breast Cancer Gold-Standard Using Supervised Learning from Multiple Experts. In: Stoyanov, D., et al. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS CVII STENT 2018 2018 2018. Lecture Notes in Computer Science(), vol 11043. Springer, Cham. https://doi.org/10.1007/978-3-030-01364-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-01364-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01363-9

  • Online ISBN: 978-3-030-01364-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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