Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic...

Area of Science:

• Radiation Oncology
• Medical Physics
• Computational Biology

Background:

• Radiotherapy treatment planning requires precise optimization of parameters to meet clinical objectives.
• Volumetric Modulated Arc Therapy (VMAT) for prostate cancer necessitates robust optimization strategies to balance target coverage and organ-at-risk sparing.
• Current manual optimization processes can be time-consuming and may not consistently achieve optimal plan quality.

Purpose of the Study:

• To introduce and evaluate an automated approach for fine-tuning optimization parameters in VMAT prostate planning.
• To enhance the consistency and quality of radiotherapy treatment plans by ensuring all planning constraints are met.
• To improve the efficiency of the treatment planning process.

Main Methods:

• An in-house developed script was utilized to automatically fine-tune optimization parameters.
• A knowledge-based planning model was integrated into the automated fine-tuning workflow.
• The method was applied to 25 VMAT prostate treatment plans.

Main Results:

• The automated fine-tuning successfully increased the number of plans meeting all constraints from 10/25 to 22/25.
• A reduction in mean monitor units per gray was observed without an increase in plan complexity.
• The automation demonstrated significant time and resource savings in treatment planning.

Conclusions:

• Automated parameter fine-tuning is an effective strategy for improving VMAT prostate plan quality and consistency.
• This approach enhances efficiency in radiotherapy treatment planning, leading to faster plan delivery.
• The developed method offers a valuable tool for optimizing radiotherapy workflows.