Literature DB >> 18752085

The feasibility of using Pareto fronts for comparison of treatment planning systems and delivery techniques.

Rickard O Ottosson1, Per E Engstrom, David Sjöström, Claus F Behrens, Anna Karlsson, Tommy Knöös, Crister Ceberg.   

Abstract

Pareto optimality is a concept that formalises the trade-off between a given set of mutually contradicting objectives. A solution is said to be Pareto optimal when it is not possible to improve one objective without deteriorating at least one of the other. A set of Pareto optimal solutions constitute the Pareto front. The Pareto concept applies well to the inverse planning process, which involves inherently contradictory objectives, high and uniform target dose on one hand, and sparing of surrounding tissue and nearby organs at risk (OAR) on the other. Due to the specific characteristics of a treatment planning system (TPS), treatment strategy or delivery technique, Pareto fronts for a given case are likely to differ. The aim of this study was to investigate the feasibility of using Pareto fronts as a comparative tool for TPSs, treatment strategies and delivery techniques. In order to sample Pareto fronts, multiple treatment plans with varying target conformity and dose sparing of OAR were created for a number of prostate and head & neck IMRT cases. The DVHs of each plan were evaluated with respect to target coverage and dose to relevant OAR. Pareto fronts were successfully created for all studied cases. The results did indeed follow the definition of the Pareto concept, i.e. dose sparing of the OAR could not be improved without target coverage being impaired or vice versa. Furthermore, various treatment techniques resulted in distinguished and well separated Pareto fronts. Pareto fronts may be used to evaluate a number of parameters within radiotherapy. Examples are TPS optimization algorithms, the variation between accelerators or delivery techniques and the degradation of a plan during the treatment planning process. The issue of designing a model for unbiased comparison of parameters with such large inherent discrepancies, e.g. different TPSs, is problematic and should be carefully considered.

Mesh:

Year:  2009        PMID: 18752085     DOI: 10.1080/02841860802251559

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  15 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Automatic replanning of VMAT plans for different treatment machines: A template-based approach using constrained optimization.

Authors:  Luise A Künzel; Oliver S Dohm; Markus Alber; Daniel Zips; Daniela Thorwarth
Journal:  Strahlenther Onkol       Date:  2018-05-30       Impact factor: 3.621

3.  Comparative analysis of Pareto surfaces in multi-criteria IMRT planning.

Authors:  K Teichert; P Süss; J I Serna; M Monz; K H Küfer; C Thieke
Journal:  Phys Med Biol       Date:  2011-05-25       Impact factor: 3.609

4.  Evaluation of the optimal combinations of modulation factor and pitch for Helical TomoTherapy plans made with TomoEdge using Pareto optimal fronts.

Authors:  Geert De Kerf; Dirk Van Gestel; Lobke Mommaerts; Danielle Van den Weyngaert; Dirk Verellen
Journal:  Radiat Oncol       Date:  2015-09-17       Impact factor: 3.481

5.  Feasibility of identification of gamma knife planning strategies by identification of pareto optimal gamma knife plans.

Authors:  C A Giller
Journal:  Technol Cancer Res Treat       Date:  2011-12

6.  Feasibility of constant dose rate VMAT in the treatment of nasopharyngeal cancer patients.

Authors:  Wenliang Yu; Haijiao Shang; Congying Xie; Ce Han; Jinling Yi; Yongqiang Zhou; Xiance Jin
Journal:  Radiat Oncol       Date:  2014-11-04       Impact factor: 3.481

7.  Initial evaluation of automated treatment planning software.

Authors:  Dawn Gintz; Kujtim Latifi; Jimmy Caudell; Benjamin Nelms; Geoffrey Zhang; Eduardo Moros; Vladimir Feygelman
Journal:  J Appl Clin Med Phys       Date:  2016-05-08       Impact factor: 2.102

8.  Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.

Authors:  Florian Stieler; Hui Yan; Frank Lohr; Frederik Wenz; Fang-Fang Yin
Journal:  Radiat Oncol       Date:  2009-09-25       Impact factor: 3.481

9.  Employing the therapeutic operating characteristic (TOC) graph for individualised dose prescription.

Authors:  Aswin L Hoffmann; Henk Huizenga; Johannes H A M Kaanders
Journal:  Radiat Oncol       Date:  2013-03-07       Impact factor: 3.481

10.  Efficiency of biological versus physical optimization for single-arc VMAT for prostate and head and neck cases.

Authors:  Vadzim Pyshniak; Irina Fotina; Alena Zverava; Stanislau Siamkouski; Elena Zayats; Georgy Kopanitsa; Dzmitry Okuntsau
Journal:  J Appl Clin Med Phys       Date:  2014-07-08       Impact factor: 2.102

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.