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

Area of Science:

• Radiation Oncology
• Medical Physics
• Radiotherapy Planning

Background:

• Respiratory motion during postmastectomy radiation therapy (PMRT) significantly compromises radiation dose delivery.
• Limited quantitative data exists comparing virtual bolus (VB) and robust optimization (RO) for motion management in PMRT.
• Optimal skin-flash strategies for addressing respiratory motion in PMRT remain undefined.

Purpose of the Study:

• To evaluate the relative motion robustness of VB and RO in PMRT.
• To investigate the impact of VB thickness and density on dosimetric robustness.
• To compare VB and RO against standard optimization (SO) for motion adaptation.

Main Methods:

• Twenty patients undergoing left-sided PMRT were retrospectively analyzed.
• Treatment plans were generated using standard optimization (SO), virtual bolus (VB) with 27 parameter combinations, and robust optimization (RO) with three uncertainty settings.
• Respiratory motion was simulated by isocenter shifts (3-15 mm), and dosimetric robustness was assessed by changes in chest wall D98% and D2% relative to the nominal plan.

Main Results:

• The most robust VB configurations utilized a thickness equal to the planning target volume (PTV) margin plus 8 mm at 0.4 g/cm³.
• Under a 5-mm shift, VB demonstrated superior dosimetric stability for D98% (-0.3%) and D2% (-0.8%) compared to SO (-8.7%, 2.6%) and RO (-2.6%, 0.7%).
• Optimized VB plans maintained target coverage even with motion exceeding the PTV margin, unlike suboptimal thin/low-density configurations.

Conclusions:

• Appropriately configured VB provides superior dosimetric robustness for target coverage in PMRT compared to RO and SO.
• The effectiveness of VB is dependent on the synergistic interplay of PTV margin, VB thickness, and density.
• A VB configuration of PTV margin + 8 mm thickness and 0.4 g/cm³ density offers practical dosimetric benefits for robustness-oriented PMRT planning.