Improved breast cancer histological grading using deep learning.

Journal: Annals of oncology : official journal of the European Society for Medical Oncology
Published Date: Sep 29, 2021

Abstract

BACKGROUND: The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. However, ∼50% of patients are classified as grade 2, an intermediate risk group with low clinical value. To improve risk stratification of NHG 2 breast cancer patients, we developed and validated a novel histological grade model (DeepGrade) based on digital whole-slide histopathology images (WSIs) and deep learning.

Authors

  • Y Wang
    1 School of Public Health, Capital Medical University, Beijing, China.
  • B Acs
    From the, Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
  • S Robertson
    Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden.
  • B Liu
    Department of Cardiology, Manchester University NHS Foundation Trust, Manchester, United Kingdom.
  • L Solorzano
    Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
  • C Wählby
    Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
  • J Hartman
    From the, Department of Oncology and Pathology, Karolinska Institutet, Stockholm, Sweden.
  • M Rantalainen
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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