Optimal beam margins in linac-based VMAT stereotactic ablative body radiotherapy: a Pareto front analysis for liver metastases

Abstract

We explored the Pareto fronts mathematical strategy to determine the optimal block margin and prescription isodose for stereotactic body radiotherapy (SBRT) treatments of liver metastases using the volumetric-modulated arc therapy (VMAT) technique. Three targets (planning target volumes [PTVs] = 20, 55, and 101 cc) were selected. A single fraction dose of 26 Gy was prescribed (prescription dose [PD]). VMAT plans were generated for 3 different beam energies. Pareto fronts based on (1) different multileaf collimator (MLC) block margin around PTV and (2) different prescription isodose lines (IDL) were produced. For each block margin, the greatest IDL fulfilling the criteria (95% of PTV reached 100%) was considered as providing the optimal clinical plan for PTV coverage. Liver D_mean, V7Gy, and V12Gy were used against the PTV coverage to generate the fronts. Gradient indexes (GI and mGI), homogeneity index (HI), and healthy liver irradiation in terms of D_mean, V7Gy, and V12Gy were calculated to compare different plans. In addition, each target was also optimized with a full-inverse planning engine to obtain a direct comparison with anatomy-based treatment planning system (TPS) results. About 900 plans were calculated to generate the fronts. GI and mGI show a U-shaped behavior as a function of beam margin with minimal values obtained with a +1 mm MLC margin. For these plans, the IDL ranges from 74% to 86%. GI and mGI show also a V-shaped behavior with respect to HI index, with minimum values at 1 mm for all metrics, independent of tumor dimensions and beam energy. Full-inversed optimized plans reported worse results with respect to Pareto plans. In conclusion, Pareto fronts provide a rigorous strategy to choose clinical optimal plans in SBRT treatments. We show that a 1-mm MLC block margin provides the best results with regard to healthy liver tissue irradiation and steepness of dose fallout.

Keywords: Pareto; Planning optimization; SBRT; VMAT.