An Integrated Framework Based on Full Monte Carlo Simulations for Double-Scattering Proton Therapy.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Proton therapy offers precise dose delivery, but accurate treatment planning and dose verification are crucial.
• Full Monte Carlo (MC) simulations provide a high level of accuracy for dose calculations in proton therapy.
• Previous work validated MC models for proton dose calculations, necessitating integration for clinical application.

Purpose of the Study:

• To develop an integrated framework for full Monte Carlo simulations in passive double-scattering proton therapy.
• To facilitate the clinical use of MC simulations by incorporating patient-specific data and a user-friendly interface.
• To enable accurate dose verification and evaluation of proton therapy treatment plans.

Main Methods:

• Developed an integrated framework utilizing a full Monte Carlo model for treatment plan simulations.
• Incorporated patient-specific applicators and compensators, and patient anatomy from CT images.
• Created a graphical user interface for streamlined workflow from DICOM import to MC parallelization.
• Performed end-to-end tests and validated the framework using three clinical cases.

Main Results:

• The integrated framework demonstrated correct functionality across all tested features.
• Comparisons between treatment planning system (TPS) calculations and MC results revealed significant dose differences (up to 17%) in specific regions like beam penumbra and distal fall-off.
• Discrepancies were attributed to variations in dose algorithms, beamline modeling, and dose metrics, while other regions showed acceptable agreement.

Conclusions:

• An integrated framework for full Monte Carlo simulations in double-scattering proton therapy has been successfully developed.
• The framework serves as a valuable tool for dose verification and comprehensive evaluation of proton therapy treatment plans.
• The study highlights the importance of accurate modeling and potential discrepancies between TPS and MC calculations in clinical scenarios.