Improving normal tissue sparing using scripting in endometrial cancer radiation therapy planning

Area of Science:

• Radiation Oncology
• Medical Physics
• Oncology

Background:

• External beam radiation therapy (EBRT) for endometrial cancer requires precise planning to maximize target dose while minimizing organ at risk (OAR) exposure.
• Manual planning is time-consuming and can be subject to inter-observer variability.
• Scripting offers a potential solution for automating and optimizing radiation therapy planning.

Purpose of the Study:

• To evaluate the efficacy of Python scripting for automating endometrial cancer EBRT planning.
• To compare scripted planning with manual planning regarding planning time, OAR protection, and dosimetric parameters.
• To assess the feasibility of improving OAR sparing and reducing planning time through automated contouring and planning.

Main Methods:

• A Python-based scripting approach was developed within the RayStation™ planning system.
• Seven OARs were automatically contoured using scripting to enhance dose reduction.
• CT data from 14 endometrial cancer patients were used for both manual and scripted planning comparisons.

Main Results:

• Scripted planning reduced mean total planning time from 368 ± 8 s to 55 ± 2 s (p < 0.001).
• Automatic planning significantly decreased mean OAR doses (p < 0.001), including maximum doses to bilateral femoral heads and rectum.
• Total monitor unit (MU) values increased slightly in scripted plans (1369 ± 95) compared to manual plans (1146 ± 126).

Conclusions:

• Scripted planning provides significant time savings and improved dosimetric outcomes for endometrial cancer EBRT.
• Automated contouring and planning using scripting enhance OAR protection and treatment efficiency.
• This technique represents a valuable advancement for optimizing radiation therapy for endometrial cancer patients.