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Automatic plan selection using deep network-A prostate study.

Philippe Y Chatigny1,2, Cédric Bélanger1,2, Éric Poulin2

  • 1Département de physique, de génie physique et d'optique, et Centre de recherche sur le cancer, Université Laval, Québec, Quebec, Canada.

Medical Physics
|December 10, 2024
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Summary
This summary is machine-generated.

Multicriteria optimization (MCO) in high-dose-rate (HDR) brachytherapy now uses deep learning (DL) for automatic plan selection. This AI approach rapidly ranks thousands of plans, matching expert choices and improving clinical efficiency.

Keywords:
HDR prostate brachytherapyMCOdeep learning

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence in Healthcare

Background:

  • High-dose-rate (HDR) brachytherapy planning benefits from multicriteria optimization (MCO) algorithms.
  • MCO generates numerous Pareto optimal plans rapidly, shifting focus to selecting the best plan from thousands.

Purpose of the Study:

  • Introduce novel, visual-like criteria beyond traditional dose-volume histogram (DVH) metrics for plan evaluation.
  • Develop and implement a deep learning (DL) framework for automatic selection of optimal HDR brachytherapy plans.

Main Methods:

  • Train a DL algorithm using new visual-like criteria (bladder, rectum, urethra, prostate cold spot) and standard DVH metrics.
  • Input for the DL model includes 3D dose and anatomical mask images for plan ranking and selection.
  • Algorithm trained on 835 prostate cancer patients and validated on 20 patients previously assessed by clinical medical physicists.

Main Results:

  • The DL network ranks 2000 plans in 10 seconds, significantly faster than expert manual selection.
  • Four DL networks were trained, offering trade-offs between target coverage and organs at risk (OAR) sparing.
  • The best DL network's plan selection showed no statistical difference compared to expert choices for multiple criteria.

Conclusions:

  • The DL-based approach is flexible, allowing custom criteria and trade-offs for plan quality.
  • This fast and robust method adds minimal time to MCO planning, showing strong potential for clinical adoption.