Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

Journal: Breast cancer research : BCR
Published Date: Jan 29, 2024

Abstract

BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance.

Authors

  • Abhinav Sharma
    Department of Biological Sciences and Bioengineering (BSBE), IIT, Kanpur, India.
  • Philippe Weitz
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Yinxi Wang
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Bojing Liu
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  • Johan Vallon-Christersson
    Division of Oncology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
  • Johan Hartman
    Department of Oncology/Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
  • Mattias Rantalainen
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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