Generation of prescriptions robust against geometric uncertainties in dose painting by numbers

Abstract

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 GTV_PET 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.

Acknowledgements

The medical and physics teams of the radiotherapy department of Cliniques Universitaires Saint-Luc are acknowledged for their precious help. Sarah Differding is a PhD student supported by a grant T l vie from the Belgian “Fonds National pour la Recherche Scientifique’ (Grant T l vie-FC 92318-n°7460113F)”. J. A. Lee is a Research Associate of the Belgian ‘Fonds National pour la Recherche Scientifique’ (Research Associate-FC 63880)”.

Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Supplementary material available online

Supplementary Appendix 1–2 and Figures 1 and 2.