Literature DB >> 29846178

Lung IMRT planning with automatic determination of beam angle configurations.

Lulin Yuan1, Wei Zhu, Yaorong Ge, Yuliang Jiang, Yang Sheng, Fang-Fang Yin, Q Jackie Wu.   

Abstract

Beam angle configuration is a major planning decision in intensity modulated radiation treatment (IMRT) that has a significant impact on dose distributions and thus quality of treatment, especially in complex planning cases such as those for lung cancer treatment. We propose a novel method to automatically determine beam configurations that incorporates noncoplanar beams. We then present a completely automated IMRT planning algorithm that combines the proposed method with a previously reported OAR DVH prediction model. Finally, we validate this completely automatic planning algorithm using a set of challenging lung IMRT cases. A beam efficiency index map is constructed to guide the selection of beam angles. This index takes into account both the dose contributions from individual beams and the combined effect of multiple beams by introducing a beam-spread term. The effect of the beam-spread term on plan quality was studied systematically and the weight of the term to balance PTV dose conformity against OAR avoidance was determined. For validation, complex lung cases with clinical IMRT plans that required the use of one or more noncoplanar beams were re-planned with the proposed automatic planning algorithm. Important dose metrics for the PTV and OARs in the automatic plans were compared with those of the clinical plans. The results are very encouraging. The PTV dose conformity and homogeneity in the automatic plans improved significantly. And all the dose metrics of the automatic plans, except the lung V5 Gy, were statistically better than or comparable with those of the clinical plans. In conclusion, the automatic planning algorithm can incorporate non-coplanar beam configurations in challenging lung cases and can generate plans efficiently with quality closely approximating that of clinical plans.

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Mesh:

Year:  2018        PMID: 29846178      PMCID: PMC6691500          DOI: 10.1088/1361-6560/aac8b4

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


  4 in total

1.  A reinforcement learning application of a guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy.

Authors:  Azar Sadeghnejad-Barkousaraie; Gyanendra Bohara; Steve Jiang; Dan Nguyen
Journal:  Mach Learn Sci Technol       Date:  2021-05-13

2.  Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

Authors:  Yang Sheng; Jiahan Zhang; Chunhao Wang; Fang-Fang Yin; Q Jackie Wu; Yaorong Ge
Journal:  Technol Cancer Res Treat       Date:  2019-01-01

3.  Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Authors:  Matt Mistro; Yang Sheng; Yaorong Ge; Chris R Kelsey; Jatinder R Palta; Jing Cai; Qiuwen Wu; Fang-Fang Yin; Q Jackie Wu
Journal:  Front Artif Intell       Date:  2020-08-28

4.  On the Importance of Individualized, Non-Coplanar Beam Configurations in Mediastinal Lymphoma Radiotherapy, Optimized With Automated Planning.

Authors:  Linda Rossi; Patricia Cambraia Lopes; Joana Marques Leitão; Cecile Janus; Marjan van de Pol; Sebastiaan Breedveld; Joan Penninkhof; Ben J M Heijmen
Journal:  Front Oncol       Date:  2021-04-15       Impact factor: 6.244

  4 in total

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