Literature DB >> 11324953

An optimization method for importance factors and beam weights based on genetic algorithms for radiotherapy treatment planning.

X Wu1, Y Zhu.   

Abstract

We propose a new method for selecting importance factors (for regions of interest like organs at risk) used to plan conformal radiotherapy. Importance factors, also known as weighting factors or penalty factors, are essential in determining the relative importance of multiple objectives or the penalty ratios of constraints incorporated into cost functions, especially in dealing with dose optimization in radiotherapy treatment planning. Researchers usually choose importance factors on the basis of a trial-and-error process to reach a balance between all the objectives. In this study, we used a genetic algorithm and adopted a real-number encoding method to represent both beam weights and importance factors in each chromosome. The algorithm starts by optimizing the beam weights for a fixed number of iterations then modifying the importance factors for another fixed number of iterations. During the first phase, the genetic operators, such as crossover and mutation, are carried out only on beam weights, and importance factors for each chromosome are not changed or 'frozen'. In the second phase, the situation is reversed: the beam weights are 'frozen' and the importance factors are changed after crossover and mutation. Through alternation of these two phases, both beam weights and importance factors are adjusted according to a fitness function that describes the conformity of dose distribution in planning target volume and dose-tolerance constraints in organs at risk. Those chromosomes with better fitness are passed into the next generation, showing that they have a better combination of beam weights and importance factors. Although the ranges of the importance factors should be set in advance by using this algorithm, it is much more convenient than selecting specific numbers for importance factors. Three clinical examples are presented and compared with manual plans to verify this method. Three-dimensional standard displays and dose-volume histograms are shown to demonstrate that this method is feasible, automatic and convenient.

Mesh:

Year:  2001        PMID: 11324953     DOI: 10.1088/0031-9155/46/4/313

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


  11 in total

1.  A neural network approach to treatment optimization.

Authors:  Paul Munro; Siripun Sanguansintukual
Journal:  Proc AMIA Symp       Date:  2002

2.  Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.

Authors:  Chenyang Shen; Yesenia Gonzalez; Peter Klages; Nan Qin; Hyunuk Jung; Liyuan Chen; Dan Nguyen; Steve B Jiang; Xun Jia
Journal:  Phys Med Biol       Date:  2019-05-29       Impact factor: 3.609

3.  A hierarchical evolutionary algorithm for multiobjective optimization in IMRT.

Authors:  Clay Holdsworth; Minsun Kim; Jay Liao; Mark H Phillips
Journal:  Med Phys       Date:  2010-09       Impact factor: 4.071

4.  A two-stage sequential linear programming approach to IMRT dose optimization.

Authors:  Hao H Zhang; Robert R Meyer; Jianzhou Wu; Shahid A Naqvi; Leyuan Shi; Warren D D'Souza
Journal:  Phys Med Biol       Date:  2010-01-14       Impact factor: 3.609

5.  Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.

Authors:  Chenyang Shen; Dan Nguyen; Liyuan Chen; Yesenia Gonzalez; Rafe McBeth; Nan Qin; Steve B Jiang; Xun Jia
Journal:  Med Phys       Date:  2020-03-28       Impact factor: 4.071

6.  Evaluation of plan quality improvements in PlanIQ-guided Autoplanning.

Authors:  Bojarajan Perumal; Harikrishna Etti Sundaresan; Vaitheeswaran Ranganathan; Natarajan Ramar; Gipson Joe Anto; Samir Ranjan Meher
Journal:  Rep Pract Oncol Radiother       Date:  2019-09-20

7.  Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy.

Authors:  Chenyang Shen; Liyuan Chen; Yesenia Gonzalez; Xun Jia
Journal:  Med Phys       Date:  2021-02-16       Impact factor: 4.071

8.  A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy.

Authors:  Chenyang Shen; Liyuan Chen; Xun Jia
Journal:  Phys Med Biol       Date:  2021-06-23       Impact factor: 3.609

Review 9.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24

10.  The application of distance transformation on parameter optimization of inverse planning in intensity-modulated radiation therapy.

Authors:  Hui Yan; Fang-Fang Yin
Journal:  J Appl Clin Med Phys       Date:  2008-04-16       Impact factor: 2.102

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