Prediction of gene expression-based breast cancer proliferation scores from histopathology whole slide images using deep learning.

Journal: BMC cancer
PMID: 39663527

Abstract

BACKGROUND: In breast cancer, several gene expression assays have been developed to provide a more personalised treatment. This study focuses on the prediction of two molecular proliferation signatures: an 11-gene proliferation score and the MKI67 proliferation marker gene. The aim was to assess whether these could be predicted from digital whole slide images (WSIs) using deep learning models.

Authors

  • Andreas Ekholm
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
  • Yinxi Wang
    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.
  • Constance Boissin
    Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden. constance.boissin@ki.se.
  • Mattias Rantalainen
    Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

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