Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

Journal: JCO clinical cancer informatics
Published Date: Jan 1, 2021

Abstract

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological complete response (pCR) has prognostic value depending on BC subtype. Rates of pCR, however, can be variable. Predictive modeling is desirable to help identify patients early who may have suboptimal NAC response. Here, we test and compare the predictive performances of machine learning (ML) prediction models to a standard statistical model, using clinical and pathological data.

Authors

  • Nicholas Meti
    Division of Medical Oncology, 71545Sunnybrook Health Sciences Centre, Toronto, Canada.
  • 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.
  • Andrew Lagree
    Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Sami Tabbarah
    Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Majid Mohebpour
    Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.
  • Alex Kiss
    Institute of Clinical Evaluative Sciences, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
  • Fang-I Lu
    Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Anatomic Pathology, Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Elzbieta Slodkowska
    Department of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
  • Sonal Gandhi
    Division of Medical Oncology, 71545Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Katarzyna Joanna Jerzak
    Division of Medical Oncology, Department of Medicine, University of Toronto, ON, Canada.
  • Lauren Fleshner
    Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, ON, Canada.
  • Ethan Law
    Odette Cancer Program, Sunnybrook Health Sciences Centre, Toronto, Canada; Radiogenomics Laboratory, Sunnybrook Health Sciences Centre, Toronto, Canada.
  • Ali Sadeghi-Naini
    Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, 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.

Keywords

External Resources

Popular Topics
Recent Journals