(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: To determine the relative dosimetric impact of delineation variability (DV) when inter-observer and inter-technique planning variability (PV), and setup variability (SV) with are considered.
Methods: 409 plans for a single head-and-neck patient from the 2017 Radiation Knowledge plan competition were used. Plans were created with Eclipse (N=227), Pinnacle (N=49), RayStation (N=25), Monaco (N=75), and TomoTherapy (N=33) with delivery techniques conventional linac IMRT (N=142), volumetric modulated arc therapy (VMAT, N=234), and helical TomoTherapy (N=33). All plans were optimized using a consistent set of target volumes and a single OAR structure set. Four additional OAR structure sets were contoured by radiation oncologists (N=2) and medical physics residents (N=2) who had completed head-and-neck contouring training. Probabilistic DVHs, dose-volume coverage maps (DVCM), which shows the probability of achieving a dose metric, were computed for each OAR on the following scenarios: SV alone (N=1000), SV+PV (N=1000*409), SV+DV (N=1000*5), SV+PV+DV (total variability [TV], N=1000*409*5). Analysis focused on the probability of exceeding the maximum dose constraint exceeded 5% for each OAR.
Results: The primary source of variability was PV, which was expected due to inter-observer planning abilities and preferences during the optimization planning process, even when all participants utilized the same constraints. The parotid had the most significant interquartile range (IQR) on the PV scenario. Conversely, adding SV, DV, and TV each reduced the IQR, showing a washing out effect on the DVCM.
Conclusion: Assessment of OAR sensitivity to DV will be highly sensitive to the specific planning technique and planner, likely requiring plan-specific assessment of in-tolerance delineation variations. Incorporation SV and DV variabilities in plan assessments washes out their relative impacts on maximum dose.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by NIH R01CA222216.
Keywords
Image-guided Therapy, Dose, Image Analysis
Taxonomy
TH- Dataset Analysis/Biomathematics: Informatics
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