Literature DB >> 19560069

Analysis of treatment planning time among systems and planners for intensity-modulated radiation therapy.

Indra J Das1, Vadim Moskvin, Peter A Johnstone.   

Abstract

Radiation oncology is a technologically advanced health care specialty in which numerous innovations, such as intensity-modulated radiation therapy (IMRT), require significant manpower and resources. For 3 main disease sites (prostate, head and neck, and lung), the authors investigated IMRT planning time across the United States among commonly used treatment planning systems (TPS). Treatment planning time was investigated in different components of IMRT: data transfer, contouring, beam arrangements, optimization, dose calculation, and phantom plans. The results showed significant variability among the TPS depending on the functionality and efficiency of the TPS algorithm. This study provides a road map to quantify the manpower needed and the selection of proper tools for IMRT planning and could be a model for any health care task.

Mesh:

Year:  2009        PMID: 19560069     DOI: 10.1016/j.jacr.2008.12.013

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  15 in total

1.  Human-computer interaction in radiotherapy target volume delineation: a prospective, multi-institutional comparison of user input devices.

Authors: 
Journal:  J Digit Imaging       Date:  2011-10       Impact factor: 4.056

2.  Registration by interactive inverse simulation: application for adaptive radiotherapy.

Authors:  Eulalie Coevoet; Nick Reynaert; Eric Lartigau; Luis Schiappacasse; Jérémie Dequidt; Christian Duriez
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-04-07       Impact factor: 2.924

3.  Domain knowledge driven 3D dose prediction using moment-based loss function.

Authors:  Gourav Jhanwar; Navdeep Dahiya; Parmida Ghahremani; Masoud Zarepisheh; Saad Nadeem
Journal:  Phys Med Biol       Date:  2022-09-14       Impact factor: 4.174

4.  Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions.

Authors:  M A Deeley; A Chen; R D Datteri; J Noble; A Cmelak; E Donnelly; A Malcolm; L Moretti; J Jaboin; K Niermann; Eddy S Yang; David S Yu; B M Dawant
Journal:  Phys Med Biol       Date:  2013-05-17       Impact factor: 3.609

5.  A national survey of the availability of intensity-modulated radiation therapy and stereotactic radiosurgery in Canada.

Authors:  Eman Z AlDuhaiby; Stephen Breen; Jean-Pierre Bissonnette; Michael Sharpe; Linda Mayhew; Scott Tyldesley; Derek R Wilke; David C Hodgson
Journal:  Radiat Oncol       Date:  2012-02-07       Impact factor: 3.481

6.  On correlations in IMRT planning aims.

Authors:  Arkajyoti Roy; Indra J Das; Omid Nohadani
Journal:  J Appl Clin Med Phys       Date:  2016-11-08       Impact factor: 2.102

7.  Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction.

Authors:  Yun Yang; Taoran Li; Lunlin Yuan; Yaorong Ge; Fang-Fang Yin; W Robert Lee; Q Jackie Wu
Journal:  J Appl Clin Med Phys       Date:  2015-03-08       Impact factor: 2.102

8.  The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

Authors:  Shihao Li; Jianghong Xiao; Ling He; Xingchen Peng; Xuedong Yuan
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec

9.  Automated IMRT planning with regional optimization using planning scripts.

Authors:  Ilma Xhaferllari; Eugene Wong; Karl Bzdusek; Michael Lock; Jeff Chen
Journal:  J Appl Clin Med Phys       Date:  2013-01-07       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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