Room: Exhibit Hall | Forum 5
Purpose: Radiotherapy replans are generally created without directly considering the existing plan, requiring an iterative process of manual dose summation and re-optimization. The Treatment Adaptation module in RayStation (v6.1) is fundamentally different in that it incorporates preexisting dose directly into the optimization process. We investigate best-planning strategies, highlight potential workflow and dosimetric advantages and challenges, and report on our initial experience using this module.
Methods: Three clinical replan cases (shoulder, head and neck, and lung cases originally planned in Pinnacle) were imported into RayStation and replanned using the Treatment Adaptation module. The initial planning CT was deformably registered to the replanning CT using ANACONDA hybrid registration. RayStation allows mixed objective functions (a combination of replan-only and initial-plus-replan composite objectives). Different combinations of objectives were evaluated for creating adaptive replans. The RayStation- and Pinnacle-generated replans and composites were compared using target and OAR dose-volume data.
Results: The RayStation Treatment Adaptation module simplifies the replanning workflow while maintaining plan quality. Targets optimized with replan-only objectives performed best, with comparable target coverage to the Pinnacle replans (D95 within 60 cGy between the two plans) and improved homogeneity. OARs optimized with composite objectives produced improved OAR sparing. Serial organs also used replan-only objectives to ensure acceptable fractional dose. Replan characteristics were sensitive to the quality of the image registration, indicating that extra care is necessary with registration.
Conclusion: Incorporating preexisting dose into the replan optimization process provides valuable information that allows for more informed replan evaluation, reduces iterative optimization and manual dose accumulation, and saves time. Our initial experience shows that further work is necessary to characterize the impact of deformable image registration on replan optimization using RayStationâ€™s Treatment Adaptation module.