A Monte Carlo-based treatment planning tool for proton therapy

Area of Science:

• Medical Physics
• Radiotherapy
• Computational Biology

Background:

• Monte Carlo (MC) simulations offer superior accuracy for dose and fluence distribution in radiotherapy compared to analytical algorithms.
• Analytical algorithms are widely used in treatment planning due to faster computation times, but may sacrifice precision.

Purpose of the Study:

• To introduce a novel MC-based treatment planning (MCTP) tool for proton therapy.
• To support advanced treatment planning studies and research applications in proton therapy.

Main Methods:

• The MCTP tool is based on the FLUKA MC code, enabling single and multiple-field optimization.
• It optimizes Relative Biological Effectiveness (RBE)-weighted dose using constant (1.1) or variable RBE, incorporating a validated local effect model for radiobiological input.
• Treatment plans were developed for water phantoms and patient-CT geometries.

Main Results:

• The developed MCTP tool was validated against experimental dosimetric measurements and commercial analytical treatment planning calculations.
• The tool demonstrated accurate dose and fluence distribution predictions in realistic patient treatment scenarios.
• Treatment plans were generated for clinical parameters used at the Italian National Center for Oncological Hadron Therapy.

Conclusions:

• A versatile and validated MCTP tool for proton therapy has been successfully developed.
• The tool is suitable for research and supporting treatment planning at advanced ion beam therapy facilities.
• This advancement offers improved accuracy and flexibility in proton therapy planning.