Literature DB >> 29079336

Photon optimizer (PO) vs progressive resolution optimizer (PRO): a conformality- and complexity-based comparison for intensity-modulated arc therapy plans.

Diana Binny1, Tanya Kairn2, Craig M Lancaster3, Jamie V Trapp4, Scott B Crowe5.   

Abstract

This study aimed to provide guidance on the advantages and limitations of a new optimizer, "photon optimizer" (PO), when compared with its predecessor, "progressive resolution optimizer" (PRO), for intensity-modulated arc therapy (IMAT) plans. Eleven study plans that included a cohort of prostate, head and neck, and brain treatment sites were optimized using both PRO and PO algorithms. A plan template using the same objectives for the same number of iterations was used for each optimized plan to obtain hypothetical treatment plans that would be comparable with a clinical plan. Analysis was performed using plan conformity-based parameters such as target volume coverage factor, conformation number and homogeneity indices, and plan complexity assessment parameters such as small aperture score, modulation indices, and monitor unit variation with arc angle for prostate, brain and head, and neck IMAT treatment plans. Plan conformality analysis demonstrated that conformation numbers, target volume coverage factors, and homogeneity indices produced by the 2 optimizers were comparable for most anatomic sites. IMAT treatment plans produced using the PRO optimizer were found to be less complex than plans produced using the PO optimizer, in terms of multileaf collimator (MLC) leaf position variability and modulation complexity scores. Similarly, the PRO optimizer was shown to produce treatment plans that used fewer monitor units (and generally fewer monitor unit per degree of arc rotation) than PO optimizer. This study demonstrated that the PO optimizer can produce IMAT treatment plans with a similar degree of dose conformity to the target volume and generally improved organ at risk sparing, compared with the PRO optimizer. Better coverage to organs at risk produced by plans optimized using PO was observed to have higher MLC variability and monitor units. Therefore, careful evaluation of treatment plan conformity and complexity before assessing its deliverability is recommended when implementing the routine use of PO optimizer. Crown
Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Monitor unit indices; Optimization; Photon optimizer; Progressive resolution optimizer

Mesh:

Year:  2017        PMID: 29079336     DOI: 10.1016/j.meddos.2017.10.003

Source DB:  PubMed          Journal:  Med Dosim        ISSN: 1873-4022            Impact factor:   1.482


  8 in total

Review 1.  Complexity metrics for IMRT and VMAT plans: a review of current literature and applications.

Authors:  Sophie Chiavassa; Igor Bessieres; Magali Edouard; Michel Mathot; Alexandra Moignier
Journal:  Br J Radiol       Date:  2019-07-24       Impact factor: 3.039

2.  Comparison of global and local gamma evaluation results using isodose levels.

Authors:  Liting Yu; Tanya Kairn; Jamie V Trapp; Scott B Crowe
Journal:  Phys Eng Sci Med       Date:  2021-02-08

3.  Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning.

Authors:  Hanlin Wang; Ruoxi Wang; Jiacheng Liu; Jian Zhang; Kaining Yao; Haizhen Yue; Yibao Zhang; Jing You; Hao Wu
Journal:  Br J Radiol       Date:  2021-06-16       Impact factor: 3.629

4.  Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator.

Authors:  Paulo Quintero; Yongqiang Cheng; David Benoit; Craig Moore; Andrew Beavis
Journal:  Br J Radiol       Date:  2021-04-29       Impact factor: 3.629

5.  Improving treatment efficiency via photon optimizer (PO) MLC algorithm for synchronous single-isocenter/multiple-lesions VMAT lung SBRT.

Authors:  Lana Sanford; Damodar Pokhrel
Journal:  J Appl Clin Med Phys       Date:  2019-09-20       Impact factor: 2.102

6.  Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions.

Authors:  Maria Rago; Lorenzo Placidi; Mattia Polsoni; Giulia Rambaldi; Davide Cusumano; Francesca Greco; Luca Indovina; Sebastiano Menna; Elisa Placidi; Gerardina Stimato; Stefania Teodoli; Gian Carlo Mattiucci; Silvia Chiesa; Fabio Marazzi; Valeria Masiello; Vincenzo Valentini; Marco De Spirito; Luigi Azario
Journal:  PLoS One       Date:  2021-01-15       Impact factor: 3.240

7.  Analysis of prostate intensity- and volumetric-modulated arc radiation therapy planning quality with PlanIQTM.

Authors:  Motoharu Sasaki; Yuji Nakaguuchi; Takeshi Kamomae; Akira Tsuzuki; Satoshi Kobuchi; Kenmei Kuwahara; Shoji Ueda; Yuto Endo; Hitoshi Ikushima
Journal:  J Appl Clin Med Phys       Date:  2021-03-25       Impact factor: 2.102

8.  RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies.

Authors:  A Fogliata; L Cozzi; G Reggiori; A Stravato; F Lobefalo; C Franzese; D Franceschini; S Tomatis; M Scorsetti
Journal:  Radiat Oncol       Date:  2019-10-30       Impact factor: 3.481

  8 in total

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