SU-F-BRD-07: Empirical Derivation of Site-Specific Margin Formulas




To empirically derive margin formulas from existing clinical radiotherapy plans accounting for respiratory motion and setup uncertainties.


We simulated realistic treatment scenarios, including respiratory motion and setup errors. Individual probability density functions (PDF) from respiratory data were used to simulate respiratory motion. Random (σ) and systematic (Σ) setup errors were modeled as Gaussian distributions. One-dimensional dose profiles were extracted from existing radiotherapy plans and convolved with respiratory PDFs and random error distributions to produce blurred dose profiles. Each blurred dose profile was then shifted 1000 times by randomly sampling the simulated systematic error distribution. Margins were determined from the distance between the simulated treatment and the original 95% isodose level. An equation was fit for each (σ, Σ) combination to derive margin formulas for 90% of the population receiving 95% dose. This methodology can be applied to different tumor sites. Here, dose profiles were extracted from partial breast 3DCRT plans in the anterior-posterior (AP) and superior-inferior (SI) directions. Respiratory motion data was from healthy volunteers, and a clinically relevant range of random and systematic setup errors (standard deviations 1 – 4 mm) was determined from the literature.


The PBI margin formulas in the AP and SI directions for 95% dose coverage for 90% of the population were very similar: M= 0.68σ + 1.54Σ and M= 0.72σ + 1.50Σ, respectively. Systematic setup errors had the largest influence on required margin size, whereas realistic respiratory amplitude had minimal impact. The derived formulas resulted in a smaller systematic component than commonly-used theoretical margin recipes.


We have demonstrated a method to derive empirical margin formulas from existing patient radiotherapy plans, incorporating realistic respiratory motion and appropriate ranges of random and systematic error. Site-specific margin formulas may be better suited to patient populations than theoretically derived, ideal geometry general margin formulas.