Literature DB >> 25658486

Standardized beam bouquets for lung IMRT planning.

Lulin Yuan1, Q Jackie Wu, Fangfang Yin, Ying Li, Yang Sheng, Christopher R Kelsey, Yaorong Ge.   

Abstract

The selection of the incident angles of the treatment beams is a critical component of intensity modulated radiation therapy (IMRT) planning for lung cancer due to significant variations in tumor location, tumor size and patient anatomy. We investigate the feasibility of establishing a small set of standardized beam bouquets for planning. The set of beam bouquets were determined by learning the beam configuration features from 60 clinical lung IMRT plans designed by experienced planners. A k-medoids cluster analysis method was used to classify the beam configurations in the dataset. The appropriate number of clusters was determined by maximizing the value of average silhouette width of the classification. Once the number of clusters had been determined, the beam arrangements in each medoid of the clusters were designated as the standardized beam bouquet for the cluster. This standardized bouquet set was used to re-plan 20 cases randomly selected from the clinical database. The dosimetric quality of the plans using the beam bouquets was evaluated against the corresponding clinical plans by a paired t-test. The classification with six clusters has the largest average silhouette width value and hence would best represent the beam bouquet patterns in the dataset. The results shows that plans generated with a small number of standardized bouquets (e.g. 6) have comparable quality to that of clinical plans. These standardized beam configuration bouquets will potentially help improve plan efficiency and facilitate automated planning.

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Year:  2015        PMID: 25658486      PMCID: PMC4384508          DOI: 10.1088/0031-9155/60/5/1831

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


  18 in total

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2.  Predicting dose-volume histograms for organs-at-risk in IMRT planning.

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Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

3.  Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.

Authors:  Lulin Yuan; Yaorong Ge; W Robert Lee; Fang Fang Yin; John P Kirkpatrick; Q Jackie Wu
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4.  4π noncoplanar stereotactic body radiation therapy for centrally located or larger lung tumors.

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6.  Unsupervised clustering of multivariate circular data.

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7.  Role of beam orientation optimization in intensity-modulated radiation therapy.

Authors:  A Pugachev; J G Li; A L Boyer; S L Hancock; Q T Le; S S Donaldson; L Xing
Journal:  Int J Radiat Oncol Biol Phys       Date:  2001-06-01       Impact factor: 7.038

8.  A surrogate-based metaheuristic global search method for beam angle selection in radiation treatment planning.

Authors:  H H Zhang; S Gao; W Chen; L Shi; W D D'Souza; R R Meyer
Journal:  Phys Med Biol       Date:  2013-03-21       Impact factor: 3.609

9.  A population-based comparative effectiveness study of radiation therapy techniques in stage III non-small cell lung cancer.

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10.  Comparison of 2 common radiation therapy techniques for definitive treatment of small cell lung cancer.

Authors:  Shervin M Shirvani; Aditya Juloori; Pamela K Allen; Ritsuko Komaki; Zhongxing Liao; Daniel Gomez; Michael O'Reilly; James Welsh; Vassiliki Papadimitrakopoulou; James D Cox; Joe Y Chang
Journal:  Int J Radiat Oncol Biol Phys       Date:  2013-09-01       Impact factor: 7.038

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  12 in total

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

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2.  Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.

Authors:  Yang Sheng; Yaorong Ge; Lulin Yuan; Taoran Li; Fang-Fang Yin; Qingrong Jackie Wu
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3.  Automated inverse optimization facilitates lower doses to normal tissue in pancreatic stereotactic body radiotherapy.

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4.  Beam selection for stereotactic ablative radiotherapy using Cyberknife with multileaf collimation.

Authors:  James L Bedford; Peter Ziegenhein; Simeon Nill; Uwe Oelfke
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5.  Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

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Journal:  Technol Cancer Res Treat       Date:  2019-01-01

6.  Artificial intelligence will reduce the need for clinical medical physicists.

Authors:  Xiaoli Tang; Brian Wang; Yi Rong
Journal:  J Appl Clin Med Phys       Date:  2018-01       Impact factor: 2.102

7.  Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.

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Journal:  J Appl Clin Med Phys       Date:  2018-04-06       Impact factor: 2.102

Review 8.  Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Authors:  Chunhao Wang; Xiaofeng Zhu; Julian C Hong; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019-01-01

9.  Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation.

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Journal:  Med Phys       Date:  2019-10-22       Impact factor: 4.071

10.  Automated Intensity Modulated Radiation Therapy Treatment Planning for Cervical Cancer Based on Convolution Neural Network.

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Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec
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