Literature DB >> 18451463

How many plans are needed in an IMRT multi-objective plan database?

David Craft1, Thomas Bortfeld.   

Abstract

In multi-objective radiotherapy planning, we are interested in Pareto surfaces of dimensions 2 up to about 10 (for head and neck cases, the number of structures to trade off can be this large). A key question that has not been answered yet is: how many plans does it take to sufficiently represent a high-dimensional Pareto surface? In this paper, we present a method to answer this question, and we show that the number of points needed is modest: 75 plans always controlled the error to within 5%, and in all cases but one, N + 1 plans, where N is the number of objectives, was enough for <15% error. We introduce objective correlation matrices and principal component analysis (PCA) of the beamlet solutions as two methods to understand this. PCA reveals that the feasible beamlet solutions of a Pareto database lie in a narrow, small dimensional subregion of the full beamlet space, which helps explain why the number of plans needed to characterize the database is small.

Mesh:

Year:  2008        PMID: 18451463     DOI: 10.1088/0031-9155/53/11/002

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  21 in total

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2.  Simultaneous navigation of multiple Pareto surfaces, with an application to multicriteria IMRT planning with multiple beam angle configurations.

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Journal:  Med Phys       Date:  2010-02       Impact factor: 4.071

3.  Calculating and controlling the error of discrete representations of Pareto surfaces in convex multi-criteria optimization.

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Journal:  Phys Med       Date:  2009-12-21       Impact factor: 2.685

Review 4.  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

5.  Reduced-order constrained optimization (ROCO): clinical application to head-and-neck IMRT.

Authors:  Linda Rivera; Ellen Yorke; Alex Kowalski; Jie Yang; Richard J Radke; Andrew Jackson
Journal:  Med Phys       Date:  2013-02       Impact factor: 4.071

6.  Automated intensity modulated treatment planning: The expedited constrained hierarchical optimization (ECHO) system.

Authors:  Masoud Zarepisheh; Linda Hong; Ying Zhou; Jung Hun Oh; James G Mechalakos; Margie A Hunt; Gig S Mageras; Joseph O Deasy
Journal:  Med Phys       Date:  2019-05-29       Impact factor: 4.071

7.  Reduced-order constrained optimization in IMRT planning.

Authors:  Renzhi Lu; Richard J Radke; Jie Yang; Laura Happersett; Ellen Yorke; Andrew Jackson
Journal:  Phys Med Biol       Date:  2008-11-07       Impact factor: 3.609

8.  Multicriteria optimization informed VMAT planning.

Authors:  Huixiao Chen; David L Craft; David P Gierga
Journal:  Med Dosim       Date:  2013-12-19       Impact factor: 1.482

9.  Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework.

Authors:  Hao H Zhang; Warren D D'Souza; Leyuan Shi; Robert R Meyer
Journal:  Int J Radiat Oncol Biol Phys       Date:  2009-08-01       Impact factor: 7.038

10.  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

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