Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning.Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans.ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D_2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D_98% and D_2%, rectum V_40Gy and V_60Gy, bladder D_2% and V_60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with ?0.82 (?16.43–1.08) vs. 0.91 (?5.98–6.25).ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.