Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

Journal: Technical innovations & patient support in radiation oncology
Published Date: Feb 26, 2025

Abstract

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML) models developed with planning CT (pCT) anatomy for dose and rectal toxicity prediction. The SML models aim to support HS insertion decision-making prior to RT planning procedures.

Authors

  • Madison Bush
    Queensland University of Technology, Faculty of Health, School of Clinical Sciences, Brisbane, Queensland, Australia.
  • Scott Jones
    Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Qld, 4101, Australia.
  • Catriona Hargrave
    Radiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Qld, 4101, Australia.

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