Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

Abstract

Authors

• Kailyn Stenhouse
  Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada. Electronic address: kjstenho@ucalgary.ca.
• Michael Roumeliotis
  Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD. Electronic address: mroumel1@jhu.edu.
• Philip Ciunkiewicz
  Department of Biomedical Engineering, University of Calgary, Calgary, Alberta, Canada.
• Kevin Martell
  Department of Oncology, University of Calgary, Calgary, Alberta, Canada.
• Sarah Quirk
  Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
• Robyn Banerjee
  Department of Oncology, University of Calgary, Calgary, Alberta, Canada.
• Corinne Doll
  Department of Oncology, University of Calgary, Calgary, Alberta, Canada.
• Tien Phan
  Department of Oncology, University of Calgary, Calgary, Alberta, Canada.
• Svetlana Yanushkevich
  Biometric Technologies Laboratory, Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
• Philip McGeachy
  Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada; Department of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada; Department of Oncology, University of Calgary, Calgary, Alberta, Canada.

Keywords

External Resources