A method to dynamically balance intensity modulated radiotherapy dose between organs-at-risk.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Intensity-Modulated Radiation Therapy (IMRT) planning is interactive and time-consuming.
• Current methods limit understanding of dose trade-offs between structures.
• Existing Pareto optimization is computationally intensive and non-intuitive.

Purpose of the Study:

• To develop an intuitive and fast non-Pareto approach for IMRT treatment planning.
• To enable rapid exploration of dose trade-offs for optimized plans.
• To reduce the time required for achieving satisfactory treatment plans.

Main Methods:

• A novel non-Pareto approach generates pre-computed optimization sequences.
• Sequences incrementally reduce dose to organs at risk (OARs) while maintaining target coverage.
• Planners combine sequences in real-time to visualize and refine plans.

Main Results:

• The method generates computationally efficient optimization sequences.
• Real-time combination of sequences allows rapid plan visualization and understanding of dose trade-offs.
• Demonstrated effectiveness on prostate cancer and olfactory neuroblastoma cases.

Conclusions:

• The non-Pareto approach significantly accelerates IMRT plan optimization.
• It enhances the planner's ability to understand and manage dose trade-offs.
• This method offers a faster pathway to tailored and effective radiation therapy plans.