Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.

Journal: Journal of pathology informatics
Published Date: Aug 1, 2025

Abstract

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after treatment. In a subset of cases systemic chemotherapy is offered before surgery, so called neoadjuvant chemotherapy (NAC), to downstage the disease resulting in 40-50% of cases to a pathological complete response. Meanwhile, patients receiving NAC suffer from toxic side effects and in a proportion of patients a significant amount of residual tumor remains. This study aims to predict the outcome of NAC with deep learning technology based on the microscopic morphological characteristics in whole slide images of hematoxylin and eosin (H&E) slides from the pre-operative tumor biopsy before chemotherapy.

Authors

  • B Sturm
    Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • P Lock
    Kentalis, Goes, the Netherlands.
  • D Kumar
    Indian Council of Agricultural Research-Indian Agricultural Statistics Research Institute, New Delhi, India.
  • W A M Blokx
    Department of Pathology, Division of Laboratories, Pharmacy and Biomedical Genetics, University Medical Center Utrecht, Utrecht, the Netherlands.
  • J A W M van der Laak
    Radboud University Medical Center, Nijmegen, Netherlands.

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