Generation of prescriptions robust against geometric uncertainties in dose painting by numbers
DOI:
https://doi.org/10.3109/0284186X.2014.930171Abstract
Background. In the context of dose painting by numbers delivered with intensity-modulated radiotherapy, the robustness of dose distributions against geometric uncertainties can be ensured by robust optimization. As robust optimization is seldom available in treatment planning systems (TPS), we propose an alternative method that reaches the same goal by modifying the heterogeneous dose prescription (based on 18FDG-PET) and guarantees coverage in spite of systematic and random errors with known standard deviations Σ and σ, respectively.
Material and methods. The objective was that 95% of all voxels in the GTVPET received at least 95% of the prescribed dose despite geometric errors. The prescription was modified by a geometric dilation of αΣ for systematic errors and a deconvolution by a Gaussian function of width σ for random errors. For a 90% confidence interval, α = 2.5. Planning was performed on a TomoTherapy system, such that 95% of the voxels received at least 95% of the modified prescription and less than 5% of the voxels received more than 105% of the modified prescription. The applicability of the method was illustrated for two head-and-neck tumors.
Results. Systematic and random displacements larger than αΣ and σ degraded coverage. Down to 62.8% of the points received at least 95% of prescribed dose for the largest considered displacements (5 mm systematic translation and 3 mm standard deviation for random errors). When systematic and random displacements were smaller than αΣ and σ, no degradation of target coverage was observed.
Conclusions. The method led to treatment plans with target coverage robust against geometric uncertainties without the need to incorporate these in the optimizer of the TPS. The methodology was illustrated for head-and-neck cancer but can be potentially extended to all treatment sites.
