Conventional vs machine learning-based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial.

Journal: Brachytherapy
PMID: 32317241

Abstract

PURPOSE: The purpose of this study was to evaluate the noninferiority of Day 30 dosimetry between a machine learning-based treatment planning system for prostate low-dose-rate (LDR) brachytherapy and the conventional, manual planning technique. As a secondary objective, the impact of planning technique on clinical workflow efficiency was also evaluated.

Authors

  • Alexandru Nicolae
    Department of Physics, Ryerson University, Toronto, Ontario, Canada; Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
  • Mark Semple
    Department of Medical Physics, Sunnybrook Odette Cancer Centre, Toronto, ON, Canada.
  • Lin Lu
    School of Economics and Management, Guangxi Normal University, Guilin, China.
  • Mackenzie Smith
    Department of Radiation Therapy, Sunnybrook Odette Cancer Centre, Toronto, ON, Canada.
  • Hans Chung
    Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
  • Andrew Loblaw
    Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
  • Gerard Morton
    Department of Radiation Oncology, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
  • Lucas Castro Mendez
    Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, ON, Canada.
  • Chia-Lin Tseng
    Department of Radiation Oncology, Sunnybrook Odette Cancer Centre, Toronto, ON, Canada.
  • Melanie Davidson
    Department of Medical Physics, Sunnybrook Odette Cancer Centre, Toronto, ON, Canada.
  • Ananth Ravi
    Department of Medical Physics, Odette Cancer Center, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. Electronic address: ananth.ravi@sunnybrook.ca.

Keywords

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