Robustness Comparison of the Virtual Bolus Method and Robust Optimization in Postmastectomy Radiation Therapy Using...

Background In postmastectomy radiation therapy (PMRT), respiratory motion compromises dose delivery. Quantitative comparisons between virtual bolus (VB) and robust optimization (RO) remained limited, and there is no definitive consensus regarding optimal skin-flash strategies. This study evaluated the relative motion robustness of VB and RO in PMRT and explored the influence of VB thickness and density on dosimetric robustness. Methodology This study included 20 patients with left-sided PMRT planned in RayStation using standard optimization (SO), VB (27 combinations of three planning target volume (PTV) margins, three VB thicknesses, and three densities), and RO (three uncertainty settings). Respiratory motion was simulated by shifting the isocenter by 3-15 mm. Robustness was quantified using changes in chest wall D_98% and D_2% relative to the nominal plan. Results VB configurations showed substantial variability. The most robust plans consistently involved a VB thickness equal to the PTV margin plus 8 mm at 0.4 g/cm³. Under a 5-mm shift, median D_2% changes for SO, VB, and RO were 2.6%, -0.8%, and 0.7%, while D_98% changes were -8.7%, -0.3%, and -2.6%, respectively. Top-ranked VB settings maintained dose stability even when motion exceeded the applied PTV margin. Conversely, thin, low-density VB configurations caused marked D_2% escalation (up to 3%) and were considered suboptimal. For representative configurations, changes in heart D_mean and lung V_20Gy under isocenter shifts were small and comparable among SO, VB, and RO. Conclusions Under the tested conditions, appropriately configured VB demonstrated greater dosimetric robustness of target coverage than RO and SO. Robustness relied on the combined effects of PTV margin, VB thickness, and density. A configuration with VB thickness equal to the PTV margin plus 8 mm at 0.4 g/cm³ achieved the most stable performance, providing practical dosimetric insight for robustness-oriented PMRT planning.