Optimizing lung SBRT delivery: A hybrid approach combining dynamic conformal arc (DCA) and volumetric modulated arc...

This study introduces and evaluates a hybrid dynamic conformal arc-volumetric modulated arc therapy (hDCA-VMAT) technique for lung stereotactic body radiotherapy (SBRT). The goal is to combine the planning efficiency of VMAT with the delivery robustness of dynamic conformal arc (DCA) techniques, particularly for low-density lung targets where motion and dose calculation uncertainties pose challenges.

Twenty-four previously treated lung SBRT cases were retrospectively replanned using hDCA-VMAT, conventional VMAT, and aperture controlled VMAT (VMAT_AC). hDCA-VMAT plans were initiated with a manually created DCA plan, followed by limited inverse optimization with constrained aperture modulation. Plans were created in Eclipse v16.1 and calculated using the AcurosXB algorithm. Dosimetric plan quality, beam complexity, and delivery efficiency were assessed. Complexity was quantified using aperture-based metrics (e.g., average leaf pair opening, beam area, modulation index). Pretreatment delivery accuracy was evaluated via EPID-based gamma analysis at 3%/1, 2%/1, and 1%/1 mm Gamma criteria.

All techniques produced clinically acceptable plans. Target coverage and conformity indices were comparable, but hDCA-VMAT plans demonstrated reduced mid-dose spread and significantly lower modulation. hDCA-VMAT achieved the lowest modulation factor (2.1 ± 0.52) and shortest beam on time (1.74 ± 0.46 min), a 27%-30% reduction compared to VMAT and VMAT_AC. Beam complexity metrics confirmed larger, more circular apertures for hDCA-VMAT. Gamma pass rates were significantly higher for hDCA-VMAT across all criteria, particularly under stringent 1%/1 mm criteria.

The hDCA-VMAT technique offers a practical, streamlined approach for lung SBRT planning that reduces modulation while maintaining high plan quality. By initiating with a DCA plan and applying limited optimization only when necessary, hDCA-VMAT minimizes planning complexity and improves delivery efficiency. These benefits are especially relevant for treating low-density lung tumors, where robustness to motion and delivery accuracy are critical.