Evaluation of optimization workflow using design of experiment (DoE) for various field configurations in volumetric-modulated arc therapy

•To determine the field configurations, DoE was employed in workflow of VMAT planning.•Efficient dose calculator was developed for DoE determination.•Planning with equivalent quality to experts’ created were provided by DoE process.•This process is feasible for clinical practice and will improve treatment quality.•It will provide treatment planning, removing the influence of individual experience. In volumetric-modulated arc therapy (VMAT), field configurations such as couch or arc angles are defined manually or using a template. A field configuration is reselected through trial-and-error in the case of undesirable resultant planning. To efficiently plan for desirable quality, configurations should be assessed before dose calculation. Design of experiments (DoE) is an optimization technique that efficiently reveals the influence of inputs on outputs. We developed an original tool using DoE to determine the field configuration selection and evaluated the efficacy of this workflow for clinical practice. Computed-tomography scans of 17 patients and target structures were acquired retrospectively from a brain tumor treated using a dual-arc VMAT plan. The configurations of the couch, arc, collimator angles, field sizes, and beam energy were determined using DoE. The resultant dose distributions obtained using the DoE-selected configuration were compared with the clinical plan. The averaged differences between the DoE and clinical plan for 17 patients of doses to 50% of the planning target volume (PTV-D50%), Brain-D60%, Brain-D30%, Brain stem-D1%, Left eye-D1%, Right eye-D1%, Optic nerve-D1%, and Chiasm-D1% were 0.2 ± 0.5%, −1.0 ± 4.6%, 1.7 ± 3.5%, −2.5 ± 6.7%, −0.2 ± 4.9%, −1.2 ± 3.6%, −2.8 ± 7.3%, and −2.1 ± 5.7%, respectively. Our optimization workflow obtained using DoE for various field configurations provided the same or slightly superior plan quality compared with that created by experts. This process is feasible for clinical practice and will efficiently improve treatment quality while removing the influence of the planner’s experience.