Artificial intelligence in digital breast pathology: Techniques and applications.

Journal: Breast (Edinburgh, Scotland)
Published Date:

Abstract

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field.

Authors

  • Asmaa Ibrahim
    Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine, The University of Nottingham and Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK.
  • Paul Gamble
    Google Health, Google, Palo Alto, CA, USA.
  • Ronnachai Jaroensri
    Google Health, Google, Palo Alto, CA, USA.
  • Mohammed M Abdelsamea
    Department of Mathematics, Faculty of Science, University of Assiut, Assiut 71516, Egypt ; IMT Institute for Advanced Studies, Piazza S. Francesco 19, 55100 Lucca, Italy.
  • Craig H Mermel
    Google Health, Palo Alto, CA USA.
  • Po-Hsuan Cameron Chen
    Google Health, Palo Alto, CA USA.
  • Emad A Rakha
    Department of Cellular Pathology, University of Nottingham, Nottingham, UK. Emad.Rakha@Nottingham.Ac.Uk.