Literature DB >> 26348663

Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning.

Yang Sheng1, Taoran Li, You Zhang, W Robert Lee, Fang-Fang Yin, Yaorong Ge, Q Jackie Wu.   

Abstract

An atlas-based IMRT planning technique for prostate cancer was developed and evaluated. A multi-dose atlas was built based on the anatomy patterns of the patients, more specifically, the percent distance to the prostate and the concaveness angle formed by the seminal vesicles relative to the anterior-posterior axis. A 70-case dataset was classified using a k-medoids clustering analysis to recognize anatomy pattern variations in the dataset. The best classification, defined by the number of classes or medoids, was determined by the largest value of the average silhouette width. Reference plans from each class formed a multi-dose atlas. The atlas-guided planning (AGP) technique started with matching the new case anatomy pattern to one of the reference cases in the atlas; then a deformable registration between the atlas and new case anatomies transferred the dose from the atlas to the new case to guide inverse planning with full automation. 20 additional clinical cases were re-planned to evaluate the AGP technique. Dosimetric properties between AGP and clinical plans were evaluated. The classification analysis determined that the 5-case atlas would best represent anatomy patterns for the patient cohort. AGP took approximately 1 min on average (corresponding to 70 iterations of optimization) for all cases. When dosimetric parameters were compared, the differences between AGP and clinical plans were less than 3.5%, albeit some statistical significances observed: homogeneity index (p  >  0.05), conformity index (p  <  0.01), bladder gEUD (p  <  0.01), and rectum gEUD (p  =  0.02). Atlas-guided treatment planning is feasible and efficient. Atlas predicted dose can effectively guide the optimizer to achieve plan quality comparable to that of clinical plans.

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Mesh:

Year:  2015        PMID: 26348663      PMCID: PMC4605424          DOI: 10.1088/0031-9155/60/18/7277

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


  24 in total

1.  On-line re-optimization of prostate IMRT plans for adaptive radiation therapy.

Authors:  Q Jackie Wu; Danthai Thongphiew; Zhiheng Wang; Boonyanit Mathayomchan; Vira Chankong; Sua Yoo; W Robert Lee; Fang-Fang Yin
Journal:  Phys Med Biol       Date:  2008-01-10       Impact factor: 3.609

2.  Atlas-based auto-segmentation of head and neck CT images.

Authors:  Xiao Han; Mischa S Hoogeman; Peter C Levendag; Lyndon S Hibbard; David N Teguh; Peter Voet; Andrew C Cowen; Theresa K Wolf
Journal:  Med Image Comput Comput Assist Interv       Date:  2008

3.  Multiatlas-based segmentation with preregistration atlas selection.

Authors:  Thomas R Langerak; Floris F Berendsen; Uulke A Van der Heide; Alexis N T J Kotte; Josien P W Pluim
Journal:  Med Phys       Date:  2013-09       Impact factor: 4.071

4.  An on-line replanning method for head and neck adaptive radiotherapy.

Authors:  Ergun E Ahunbay; Cheng Peng; Andrew Godley; Christopher Schultz; X Allen Li
Journal:  Med Phys       Date:  2009-10       Impact factor: 4.071

5.  Knowledge-based IMRT treatment planning for prostate cancer.

Authors:  Vorakarn Chanyavanich; Shiva K Das; William R Lee; Joseph Y Lo
Journal:  Med Phys       Date:  2011-05       Impact factor: 4.071

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

Authors:  Lindsey M Appenzoller; Jeff M Michalski; Wade L Thorstad; Sasa Mutic; Kevin L Moore
Journal:  Med Phys       Date:  2012-12       Impact factor: 4.071

7.  Online adaptive replanning method for prostate radiotherapy.

Authors:  Ergun E Ahunbay; Cheng Peng; Shannon Holmes; Andrew Godley; Colleen Lawton; X Allen Li
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-04-01       Impact factor: 7.038

8.  Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer.

Authors:  Liza J Stapleford; Joshua D Lawson; Charles Perkins; Scott Edelman; Lawrence Davis; Mark W McDonald; Anthony Waller; Eduard Schreibmann; Tim Fox
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-16       Impact factor: 7.038

9.  Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

Authors:  Xiaofeng Yang; Ning Wu; Guanghui Cheng; Zhengyang Zhou; David S Yu; Jonathan J Beitler; Walter J Curran; Tian Liu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-10-13       Impact factor: 7.038

10.  Comparison of online IGRT techniques for prostate IMRT treatment: adaptive vs repositioning correction.

Authors:  Danthai Thongphiew; Q Jackie Wu; W Robert Lee; Vira Chankong; Sua Yoo; Ryan McMahon; Fang-Fang Yin
Journal:  Med Phys       Date:  2009-05       Impact factor: 4.071

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  12 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.  Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings-a phantom simulation approach.

Authors:  Martin Hito; Wentao Wang; Hunter Stephens; Yibo Xie; Ruilin Li; Fang-Fang Yin; Yaorong Ge; Q Jackie Wu; Qiuwen Wu; Yang Sheng
Journal:  Quant Imaging Med Surg       Date:  2021-12

3.  Artificial Intelligence in Radiation Therapy.

Authors:  Yabo Fu; Hao Zhang; Eric D Morris; Carri K Glide-Hurst; Suraj Pai; Alberto Traverso; Leonard Wee; Ibrahim Hadzic; Per-Ivar Lønne; Chenyang Shen; Tian Liu; Xiaofeng Yang
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2021-08-24

4.  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
Journal:  Med Phys       Date:  2017-09-30       Impact factor: 4.071

5.  Knowledge mapping visualization analysis of the military health and medicine papers published in the web of science over the past 10 years.

Authors:  Xuan-Ming Zhang; Xuan Zhang; Xu Luo; Hai-Tao Guo; Li-Qun Zhang; Ji-Wei Guo
Journal:  Mil Med Res       Date:  2017-07-12

6.  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

7.  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

8.  Fluence Map Prediction Using Deep Learning Models - Direct Plan Generation for Pancreas Stereotactic Body Radiation Therapy.

Authors:  Wentao Wang; Yang Sheng; Chunhao Wang; Jiahan Zhang; Xinyi Li; Manisha Palta; Brian Czito; Christopher G Willett; Qiuwen Wu; Yaorong Ge; Fang-Fang Yin; Q Jackie Wu
Journal:  Front Artif Intell       Date:  2020-09-08

9.  A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.

Authors:  Jieping Zhou; Zhao Peng; Yuchen Song; Yankui Chang; Xi Pei; Liusi Sheng; X George Xu
Journal:  J Appl Clin Med Phys       Date:  2020-04-13       Impact factor: 2.102

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

Authors:  Chen Jihong; Bai Penggang; Zhang Xiuchun; Chen Kaiqiang; Chen Wenjuan; Dai Yitao; Qian Jiewei; Quan Kerun; Zhong Jing; Wu Tianming
Journal:  Technol Cancer Res Treat       Date:  2020 Jan-Dec
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