Automatic feathering algorithm for VMAT craniospinal irradiation: A comprehensive comparison with other VMAT planning strategies

In craniospinal irradiation, field matching is very sensitive to intrafraction positional uncertainties in cranio-caudal direction, which could lead to severe overdoses/underdoses inside the planning target volume. During the last decade, significant efforts were made to develop volumetric-modulated arc therapy strategies, which were less sensitive to setup uncertainties. In this study, a treatment planning system-integrated method, named automatic feathering (AF) algorithm, was compared against other volumetric-modulated arc therapy strategies. Three patients were retrospectively included. Five different planning techniques were compared, including overlap (O), staggered overlap (SO), gradient optimization (GO), overlap with AF algorithm turned on (O-AF), and staggered overlap with AF algorithm turned on (SO-AF). Three overlapping lengths were considered (5 cm, 7.5 cm, and 10 cm). The middle isocenter was shifted of ±1 mm, ±3 mm, and ±5 mm to simulate setup uncertainties. Plan robustness against simulated uncertainties was evaluated by calculating near maximum and near minimum dose differences between shifted and nonshifted plans (ΔD2%, ΔD98%). Dose differences among combinations of techniques and junction lengths were tested using Wilcoxon signed-rank test. Higher ΔD2% and ΔD98% were obtained using the overlap technique (ΔD2% = 15.4%, ΔD98% = 15.0%). O-AF and SO-AF provided comparable plan robustness to GO technique. Their performance improved significantly for grater overlapping length. For 10-cm overlap and 5-mm shift, GO, O-AF, and SO-AF yielded to the better plan robustness (5.7% < ΔD2% < 6.0%, 6.1% < ΔD98% < 7.6%). SO provided an intermediate plan robustness (9.8% < ΔD2% < 10.8%, 8.9% < ΔD98% < 10.3%). The addition of AF to the overlap technique significantly improves plan robustness especially if larger overlapping lengths are used. Using the AF algorithm, plans become as robust as plans optimized with more sophisticated and time-consuming approaches (like GO).