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A Rapid Method for Modeling a Variable Cycle Engine
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A phantom geometry approach to develop a RapidPlan knowledge-based planning model.

Himank Kalra1, Raj Pal Singh1, Anuj Kumar2

  • 1Department of Physics, GLA University, Mathura, 281406, India.

Medical Dosimetry : Official Journal of the American Association of Medical Dosimetrists
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a RapidPlan (RP) model using phantom data for prostate cancer radiotherapy, achieving excellent target coverage and significant organ-at-risk sparing. The model provides accurate dosimetry predictions, enhancing treatment planning consistency and quality.

Keywords:
Knowledge based planningModel TrainingPhantomRadiotherapy Treatment PlanningRapidPlan Model

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Radiotherapy Planning

Background:

  • Knowledge-based radiation therapy (KBRT) models, such as RapidPlan (RP), enhance treatment planning consistency and quality.
  • Developing accurate RP models requires robust datasets for training and validation.

Purpose of the Study:

  • To develop and validate a RapidPlan (RP) model using phantom geometries simulating prostate cancer patient anatomy for optimized dosimetry.
  • To evaluate the model's effectiveness in target coverage and organ-at-risk (OAR) sparing across different dose regimens.

Main Methods:

  • A library of 240 volumetric modulated arc therapy (VMAT) plans was created using customized phantom geometries representing diverse prostate anatomy.
  • The RP model was trained on these phantom plans and validated using data from 30 prostate cancer patients across three dose regimens.
  • Plans were optimized adhering to clinical constraints, ensuring PTV coverage and ALARA principles for OAR protection.

Main Results:

  • The RP model demonstrated strong target coverage, with 95% of the PTV receiving at least 95% of the prescribed dose.
  • Significant rectal sparing was observed for the 78 Gy regimen (Rectum V65Gy decreased from 15.4% to 10.1%, p=0.001).
  • Notable bladder sparing was achieved for the 60 Gy regimen (Bladder V48Gy decreased from 16.8% to 9.7%, p=0.005).

Conclusions:

  • The phantom-based RP model provides accurate dosimetry predictions and achieves clinically relevant OAR sparing for prostate cancer radiotherapy.
  • This approach enables scalable training of knowledge-based planning (KBP) models, reducing inter-planner variability and maintaining plan quality.
  • The developed RP model shows significant potential for improving prostate cancer treatment planning efficiency and outcomes.