Dose painting by numbers: a universal method to ensure the robustness of heterogeneous prescriptions against geometric uncertainties
(2013) 2013 Symposium of Belgian Hospital Physicist Association — Location: Mechelen, Belgium (1.February.2013)
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- Introduction Dose painting by numbers (DBPN) is gaining interest in the radiotherapy community because of the potential of improved local tumor control with minimal increase of side effects. In DPBN, a non-uniform dose-distribution is prescribed to a tumor volume. In current research, the prescription is often derived from PET images acquired with FDG as a tracer of cell metabolism. There are many sources of uncertainties questioning the relevance of this technique, both on biological and physical points of view. We focus here on a specific physical uncertainty, that is, geometric errors in patient positioning, their effect on delivered dose distributions, and how those can be managed practically. The voxel-by-voxel non-uniform dose prescription in DPBN makes usual margin recipes (van Herk et al) inadequate to account for geometric and random geometric uncertainties. Robust treatment plans for DPBN can be achieved by incorporating geometric quncertainties in the plan optimization process (Witte et al). Although powerful, this method is not available in most commercial treatment planning systems (TPS) and may be time-consuming. Our approach aims at providing a universal solution (i.e. TPS independent), by including systematic and random geometric uncertainties implicitly in the prescription for DPBN. Material and methods The proposed method modifies the heterogeneous dose prescription D_P to ensure robustness of planned dose D_Planned against standard deviations of systematic errors Σ and random errors σ. D_P was based in this study on a FDG-PET image with an escalation from 70 to 86 Gy within GTVPET delineated automatically from PET images (Geets et al). The workflow is illustrated in figure 1. The objective was that 95% of all voxels in the GTVPET received at least 95% of their respective prescribed dose even in the presence of geometric errors (Q0.95>95%). The prescription D_P was modified by a morphological dilation of αΣ and a deconvolution by σ (assuming gaussian distribution). The GTVPET was also extended by αΣ, to generate a PTVPET volume. For a 90% confidence interval, α=2.5. The planning process was performed on a TomoTherapy system such that 95% of the points within PTVPET received at least 95% of the modified prescription (Q0.95>95%) and less than 5% of the points received more than 105% of the modified prescription. Q0.95 and Q1.05 are derived from the cumulative quality volume histograms, the quality factor Q being the ratio between the planned dose and the modified prescribed dose. Robustness was evaluated by translating and blurring D_Planned and by comparing the resulting dose with the unmodified dose prescription within GTVPET. The methodology was illustrated for two head-and-neck tumors treated by helical TomoTherapy. To obtain non-uniform dose distributions matching the non-uniform prescription, the guidelines published by Deveau et al were followed. Results For both patients, the TomoTherapy system was capable to reproduce the (modified) non-uniform prescription with Q0.95>95% and Q1.05<5%. Systematic and random displacements larger than αΣ and σ lead to a degradation of coverage of GTVPET. When systematic and random displacements were smaller than αΣ and σ, no degradation of target coverage was observed. Figure 2 illustrates two examples for one patient. In figure 2 (a), no correction of the prescription was performed, leading to significant underdosage when geometric errors were simulated (down to 62.8% for Q0.95). In figure 2 (b), target coverage was preserved even in the presence of geometric errors. By construction, the method leads to excess dosage of the GTVPET volume as shown in figure 2 (b). However, the methodology limits implicitly the excess dose. Whatever the values αΣ and σ used to determine the corrected prescription, displacements larger than αΣ and σ lead to coverage degradation. This observation ensures that there is no dose delivered more than necessary to cover the tumor even in the presence of geometric errors of maximum αΣ and σ. Conclusions The methodology presented here allows for planned dose distributions in the context of DPBN robust against geometric errors without robust optimization of the dose. Instead, the prescription is modified, using relevant values of systematic and random uncertainties. The methodology can be implemented in any radiotherapy facility treating patients by DPBN. Random deviations of about 1 mm showed no significant impact on the considered cases. In fact, systematic errors have much more impact and can be easily taken into account without robust optimization. Although the method was illustrated for head-and-neck tumors, it is potentially valid for any site.
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Sterpin, E., Differding, S., Janssens, G., Grégoire, V., & Lee, J. (2013). Dose painting by numbers: a universal method to ensure the robustness of heterogeneous prescriptions against geometric uncertainties. 2013 Symposium of Belgian Hospital Physicist Association, Mechelen, Belgium. https://hdl.handle.net/2078.5/205634