Semi-automated radiotherapy treatment planning with a mathematical model to satisfy treatment goals

Area of Science:

• Medical Physics
• Computational Biology
• Radiotherapy

Background:

• Iterative algorithms are crucial for optimizing radiation therapy treatment planning (RTTP).
• Accurate dose distribution is essential for effective cancer treatment and minimizing side effects.
• Existing methods may face challenges in complex clinical scenarios.

Purpose of the Study:

• To present and contrast clinical results from two distinct iterative algorithms for RTTP.
• To demonstrate the application of a novel model for calculating radiation intensity distributions.
• To highlight the adaptability of the method for both 2-D and 3-D RTTP.

Main Methods:

• Subdividing patient cross-sections into grids and radiation beams into pencil rays.
• Formulating a system of linear inequalities based on anatomical data and treatment parameters.
• Utilizing iterative algorithms, including the Cimmino algorithm with weighted treatment goals.

Main Results:

• Computerized algorithms provide solutions within minutes after parameter input.
• The Cimmino algorithm allows for prioritized treatment goal fulfillment through weighting.
• The model successfully calculates radiation intensity distributions for prescribed dose distributions.

Conclusions:

• The presented iterative algorithms offer a feasible approach to RTTP.
• The method is applicable to both 2-D and 3-D radiation therapy treatment planning.
• Weighted goal assignment enhances the algorithm's flexibility in complex cases.