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Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination.

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This summary is machine-generated.

Our automated radiotherapy planning system won first place in the ESTRO 2023 challenge. The system adapts to different guidelines, showing promise for fully automated treatment planning.

Keywords:
Automated planningDeep learningKnowledge-based planningMulticriteria optimizationPlan scorecards

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Area of Science:

  • Medical Physics
  • Radiotherapy Technology

Background:

  • The ESTRO 2023 Physics Workshop featured a challenge on Fully-Automated Radiotherapy Treatment Planning (Auto-RTP).
  • Participants aimed to automatically generate treatment plans with minimal user intervention for prostate cancer patients.

Purpose of the Study:

  • To present a winning approach for automated radiotherapy planning.
  • To demonstrate adaptability to varying contouring guidelines and treatment prescriptions.

Main Methods:

  • Developed an integrated auto-contouring and auto-planning pipeline using 3D U-Net models and the Eclipse Scripting Application Programming Interface.
  • Employed RapidPlan models with multicriteria optimization and scorecards tailored to challenge objectives.
  • Utilized DICOM operations for engine activation and automated target volume definition, field geometry, and optimization.

Main Results:

  • Secured 1st place across all three challenge phases with scores of 79.9, 77.3, and 78.5.
  • Achieved the highest plan scores for prostate-only cases, averaging over 90.
  • Currently lead the "Plan Only" leaderboard with a score of 90.0.

Conclusions:

  • The automated pipeline demonstrates adaptability, advancing fully automated radiotherapy planning.
  • Further research is required to evaluate the clinical integration and acceptability of these automated plans.