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

Autor: Künzel LA; 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., Leibfarth S; Section for Biomedical Physic, Department for Radiation Oncology, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany., Dohm OS; Department for Radiation Oncology, University Hospital Tübingen, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany., Müller AC; Department for Radiation Oncology, University Hospital Tübingen, University Hospital Tübingen, Hoppe-Seyler-Str. 3, 72076 Tübingen, Germany., Zips D; 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., Thorwarth D; 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.
Jazyk: angličtina
Zdroj: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2020 Jan; Vol. 69, pp. 101-109. Date of Electronic Publication: 2019 Dec 17.
DOI: 10.1016/j.ejmp.2019.12.007
Abstrakt: 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 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).
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.
(Copyright © 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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