Malignant induction probability maps for radiotherapy using X-ray and proton beams.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Radiotherapy planning requires precise dose distribution visualization.
• Assessing the risk of secondary cancer induction is crucial for long-term patient outcomes.
• Current methods often lack integrated visualization of dose and cancer risk.

Purpose of the Study:

• To develop and display Malignant Induction Probability (MIP) maps alongside dose distributions for X-ray and proton therapy.
• To model cancer induction risk based on dose, fractionation, and relative biological effectiveness.
• To explore the influence of treatment geometry on MIP.

Main Methods:

• Utilized a linear quadratic model to calculate MIP, incorporating dose, fractionation, cell killing, and cancer induction.
• Developed an interactive MATLAB program for visualizing dose distributions and MIP maps.
• Modeled two scenarios: central tumor and superficial tumor, with varying treatment field options.

Main Results:

• Proton therapy's MIP increases with the number of treatment fields, potentially exceeding X-ray MIP in some configurations.
• Protons demonstrate lower MIPs for superficial targets due to the absence of exit dose.
• A 'dose bath' significantly increases MIP (up to tenfold), highlighting the impact of scattered radiation.
• MIP is influenced by treatment geometry, including beam path length and irradiated tissue volume.

Conclusions:

• Three-dimensional visualization of carcinogenesis risk is achievable.
• Reducing the volume of irradiated normal tissue is critical for minimizing total MIP.
• Findings support the use of advanced treatment gantries and simplified field arrangements in particle therapy, respecting normal tissue constraints.