Introducing an innovative IMPT algorithm toward clinic implementation of the MCF MKM RBE model for carbon ion therapy

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Intensity Modulated Particle Therapy (IMPT) planning for carbon ion therapy requires accurate relative biological effectiveness (RBE) modeling.
• Conventional RBE models like MKM or LEM lack microdosimetric detail, limiting predictive accuracy.
• The novel MCF MKM RBE model offers improved accuracy but presents computational challenges due to its complexity and lack of gradient information.

Purpose of the Study:

• To develop and evaluate a novel optimization framework for IMPT using the MCF MKM RBE model.
• To address the computational challenges posed by the MCF MKM model, particularly the inaccessibility of gradient information.
• To achieve improved RBE dose distributions in carbon ion therapy planning.

Main Methods:

• Formulated an RBE dose optimization problem using the discretized MCF MKM, termed the Abridged Microdosimetry Distribution Model (AMDM).
• Developed a randomized gradient-free optimization method (RGFM) to optimize monitor unit settings without explicit gradient calculations.
• Validated the RGFM on clinical prostate and brain cancer scenarios, comparing results with physical dose optimization benchmarks.

Main Results:

• The RGFM successfully optimized MCF MKM-based IMPT plans, demonstrating improved target coverage and reduced dose hot spots compared to benchmarks.
• While the MCF MKM increased computational time, the RGFM achieved high-quality solutions within clinically acceptable timeframes.
• Multi-field optimization (MFO) further enhanced plan quality, improving target coverage and critical organ sparing over single-field optimization (SFO).

Conclusions:

• The study confirms the feasibility of using RGFM for MCF MKM-based RBE dose optimization in carbon ion therapy IMPT.
• This approach facilitates the clinical integration of advanced RBE models, paving the way for more biologically optimized treatments.
• Future research will focus on algorithm refinement, parallel computing, and expanded case studies to enhance efficiency and generalizability.