Validation of automated complex head and neck treatment planning with pencil beam scanning proton therapy. Issue 2 (22nd December 2021) - Hedrick, Samantha Grace; Petro, Scott; Ward, Alex; Morris, Bart
Abstract: Background: Pencil beam scanning (PBS) proton therapy offers dosimetric advantages for several treatment sites, including head and neck (H&N). However, to achieve the optimal target coverage and robustness, these plans can be complex and time consuming to develop and optimize. Automating the treatment planning process can ensure a high‐quality and standardized plan, reduce burden to the planner, and decrease time‐to‐treatment. We utilized in‐house scripting to automate a four‐field multi‐field optimization (MFO) H&N planning technique. Methods and materials: Ten bilateral H&N patients were planned in RayStation v6 with a four‐field modified‐X beam configuration using MFO planning. Automation included creation of avoidance structures to control spot placement and development of standardized beams, PBS spot settings, robust optimization objectives, and patient‐specific predicted planning constraints. Each patient was planned both with and without automation to evaluate differences in planning time, perceived effort and plan quality, plan robustness, and OAR sparing. Results: On average, scripted plans required 3.2 h, compared to 4.3 h without the script. There was no difference in target coverage or plan robustness with or without automation. Automation significantly reduced mean dose to the oral cavity, parotids, esophagus, trachea, and larynx. Perceived effort was scaled from 1 (minimum effort) to 100 (maximum effort), and automation reduced perceived effort by 42% ( p  < 0.05). Two non‐scripted plans required re‐planning due to errors. Conclusions: Automation of this multi‐beam, the MFO proton planning process reduced planning time and improved OAR sparing compared to the same planning process without scripting. Scripting generation of complex structures and planning objectives reduced burden on the planner. With most current treatment planning software, this automation is simple to implement and can standardize quality of care across all treatment planners.