Effects of varying statistical uncertainty using a Monte Carlo based treatment planning system for VMAT
Jacob Rembish1, Pamela Myers, Daniel Saenz
1Department of Radiation Oncology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
Purpose:
To determine the severity of the effects on VMAT dose calculations caused by varying statistical uncertainties (SU) per control point in a Monte Carlo based treatment planning system (TPS) and to assess the impact of the uncertainty during dose volume histogram (DVH) evaluation.
Methods:
For this study, 13 archived patient plans were selected for recalculation. Treatment sites included prostate, lung, and head and neck. These plans were each recalculated five times with varying uncertainty levels using Elekta's Monaco Version 5.11.00 Monte Carlo Gold Standard XVMC dose calculation algorithm. The statistical uncertainty per control point ranged from 2 to 10% at intervals of 2%, while the grid spacing was set at 3 mm for all calculations. Indices defined by the RTOG describing conformity, coverage, and homogeneity were recorded for each recalculation.
Results:
For all indices tested, one-way ANOVA tests failed to reject the null hypothesis that there is no significant difference between SU levels (p>0.05). Using the Bland-Altman analysis method, it was determined that we can expect the indices (with the exception of CIRTOG) to be within 1% of the lowest uncertainty calculation when calculating at 4% SU per control point. Beyond that, we can expect the indices to be within 3% of the lowest uncertainty calculation.
Conclusion:
Increasing the SU per control point exponentially decreased the amount of time required for dose calculations, while creating minimal observable differences in DVHs and isodose lines.
