Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study. (January 2020)

• Record Type: Journal Article
• Authors: Künzel, Luise A.; Leibfarth, Sara; Dohm, Oliver S.; Müller, Arndt-Christian; Zips, Daniel; Thorwarth, Daniela
• Is Part Of: Physica medica. Volume 69(2020)
• Page Start: 101
• Page End: 109
• Publication Date: 2020-01
• Subjects: Radiotherapy -- Automatic planning -- Particle swarm optimization -- VMAT; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2019.12.007
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Particle Swarm Optimization (PSO) is a statistical optimization suitable for automated planning. Competing plans are evaluated by introducing a plan quality score based on Dose-volume-histogram parameters. PSO was successfully implemented and tested for N = 10 post-operative prostate cancer cases. PSO plans achieved comparable target dose to manual plans, but improved organ at risk dose sparing. Abstract: Objective: To investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning. Material and Methods: In 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. Results: PSO 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 D2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D98% and D2%, rectum V40Gy and V60Gy, bladder D2% and V60Gy ) 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). Conclusion: PSO 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.