Luise A Künzel1, Sara Leibfarth2, Oliver S Dohm3, Arndt-Christian Müller3, Daniel Zips4, Daniela Thorwarth5. 1. Section for Biomedical Physic, Department for Radiation Oncology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany. Electronic address: luise.kuenzel@med.uni-tuebingen.de. 2. Section for Biomedical Physic, Department for Radiation Oncology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany. 3. Department for Radiation Oncology, University Hospital Tübingen, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany. 4. Department for Radiation Oncology, University Hospital Tübingen, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany; German Cancer Consortium (DKTK), Partner Site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany. 5. Section for Biomedical Physic, Department for Radiation Oncology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany; German Cancer Consortium (DKTK), Partner Site Tübingen; and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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.
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.
Authors: Jose R Teruel; Martha Malin; Elisa K Liu; Allison McCarthy; Kenneth Hu; Bejamin T Cooper; Erik P Sulman; Joshua S Silverman; David Barbee Journal: J Appl Clin Med Phys Date: 2020-09-23 Impact factor: 2.102