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Automatically configuring the reference point method for automated multi-objective treatment planning.

Rens van Haveren1, Ben J M Heijmen1, Sebastiaan Breedveld1

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Automated treatment planning is improved by a new procedure that configures the reference point method (RPM) automatically. This reduces manual effort and enhances efficiency for generating high-quality radiation therapy plans.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Automated treatment planning algorithms produce consistent, high-quality plans.
  • Configuring these algorithms is a manual, time-consuming trial-and-error process.
  • The reference point method (RPM) was previously introduced for fast, automated multi-objective treatment planning.

Purpose of the Study:

  • To propose and evaluate a new procedure for automatically generating a single configuration of the RPM for each tumor site.
  • To reduce the manual workload associated with configuring automated treatment planning algorithms.
  • To improve the efficiency and effectiveness of automated clinical treatment planning workflows.

Main Methods:

  • A procedure was developed to automatically configure the RPM based on plan characteristics from a training set of prostate cancer patient data (287 patients).
  • The procedure acquires plan characteristics from existing Pareto optimal plans generated by a two-phase [Formula: see text]-constraint method.
  • RPM configurations were automatically generated based on user-defined preferences for plan objective trade-offs and evaluated on a separate test set.

Main Results:

  • The automated procedure successfully generated an RPM configuration for each training set.
  • RPM-generated plans using the automated configurations showed similar or slightly better quality compared to the test set plans.
  • The proposed method significantly reduced the manual configuration workload for automated treatment planning.

Conclusions:

  • The developed procedure effectively automates the configuration of the RPM for multi-objective treatment planning.
  • This automation leads to significant improvements in the efficiency and effectiveness of clinical workflows.
  • The method holds promise for optimizing radiation therapy planning across different tumor sites.