A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

Journal: Medical physics
Published Date: Jul 5, 2023

Abstract

BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival.

Authors

  • Khadijeh Saednia
    Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Canada.
  • William T Tran
    Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Faculty of Medicine, Department Radiation Oncology, University of Toronto, Toronto, Canada; Faculty of Health and Wellbeing, Sheffield Hallam University, Sheffield, United Kingdom; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada. Electronic address: william.tran@sunnybrook.ca.
  • Ali Sadeghi-Naini
    Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

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