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Automated evaluation for rapid implementation of knowledge-based radiotherapy planning models.

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Summary

Knowledge-based planning (KBP) models were evaluated using the automated RapidCompare tool. Line optimization objectives in KBP produced the highest quality treatment plans without manual intervention.

Keywords:
VMATautomationautoplanningclinical scriptingknowledge-based planning

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Knowledge-based planning (KBP) enhances treatment plan quality and reduces variability by leveraging data from prior plans.
  • Increasing clinical adoption of KBP necessitates robust quantitative methods for evaluating its performance.
  • Varian RapidPlan models are widely used, requiring effective tools for their analysis.

Purpose of the Study:

  • To introduce RapidCompare, a .NET application for automated creation and analysis of Varian RapidPlan models.
  • To quantitatively evaluate the performance of different KBP optimization strategies.
  • To demonstrate the utility of RapidCompare in assessing KBP model efficacy.

Main Methods:

  • RapidCompare was developed to process calculation parameters and reference plans, automating plan generation and dose-volume metric evaluation.
  • A cohort of 85 patients (50 training, 10 testing, 25 validation) was utilized to demonstrate RapidCompare's functionality.
  • Three optimization templates (DVH, fixed-dose/volume, gEUD) were compared in the validation cohort using consistent planning target volume constraints.

Main Results:

  • RapidCompare automated the generation of 75 treatment plans with minimal manual input.
  • The "Lines" optimization template showed a slight advantage over "Dose/Volume" in improving DVH metrics, notably reducing heart V30Gy and spinal cord max dose.
  • The generalized equivalent uniform dose (gEUD) optimization model resulted in significant target volume heterogeneity.

Conclusions:

  • Automated evaluation with RapidCompare enabled the assessment of multiple optimization templates in a larger cohort than manual methods would permit.
  • KBP models employing line optimization objectives yielded superior plan quality without manual adjustments.
  • The study highlights the potential of automated tools for rigorous KBP model validation and optimization.