Literature DB >> 28840767

Automatic treatment planning facilitates fast generation of high-quality treatment plans for esophageal cancer.

Christian Rønn Hansen1,2, Morten Nielsen1, Anders Smedegaard Bertelsen1, Irene Hazell1, Eva Holtved3, Ruta Zukauskaite2,3, Jon Kroll Bjerregaard2,3, Carsten Brink1,2, Uffe Bernchou1,2.   

Abstract

BACKGROUND: The quality of radiotherapy planning has improved substantially in the last decade with the introduction of intensity modulated radiotherapy. The purpose of this study was to analyze the plan quality and efficacy of automatically (AU) generated VMAT plans for inoperable esophageal cancer patients.
MATERIAL AND METHODS: Thirty-two consecutive inoperable patients with esophageal cancer originally treated with manually (MA) generated volumetric modulated arc therapy (VMAT) plans were retrospectively replanned using an auto-planning engine. All plans were optimized with one full 6MV VMAT arc giving 60 Gy to the primary target and 50 Gy to the elective target. The planning techniques were blinded before clinical evaluation by three specialized oncologists. To supplement the clinical evaluation, the optimization time for the AU plan was recorded along with DVH parameters for all plans.
RESULTS: Upon clinical evaluation, the AU plan was preferred for 31/32 patients, and for one patient, there was no difference in the plans. In terms of DVH parameters, similar target coverage was obtained between the two planning methods. The mean dose for the spinal cord increased by 1.8 Gy using AU (p = .002), whereas the mean lung dose decreased by 1.9 Gy (p < .001). The AU plans were more modulated as seen by the increase of 12% in mean MUs (p = .001). The median optimization time for AU plans was 117 min.
CONCLUSIONS: The AU plans were in general preferred and showed a lower mean dose to the lungs. The automation of the planning process generated esophageal cancer treatment plans quickly and with high quality.

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Year:  2017        PMID: 28840767     DOI: 10.1080/0284186X.2017.1349928

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  15 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.  Impact of dosimetric differences between CT and MRI derived target volumes for external beam cervical cancer radiotherapy.

Authors:  Vikneswary Batumalai; Siobhan Burke; Dale Roach; Karen Lim; Glen Dinsdale; Michael Jameson; Cesar Ochoa; Jacqueline Veera; Lois Holloway; Shalini Vinod
Journal:  Br J Radiol       Date:  2020-06-18       Impact factor: 3.039

3.  Automatic Segmentation of Clinical Target Volumes for Post-Modified Radical Mastectomy Radiotherapy Using Convolutional Neural Networks.

Authors:  Zhikai Liu; Fangjie Liu; Wanqi Chen; Xia Liu; Xiaorong Hou; Jing Shen; Hui Guan; Hongnan Zhen; Shaobin Wang; Qi Chen; Yu Chen; Fuquan Zhang
Journal:  Front Oncol       Date:  2021-02-16       Impact factor: 6.244

4.  Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface.

Authors:  Charles Huang; Yong Yang; Neil Panjwani; Stephen Boyd; Lei Xing
Journal:  IEEE Trans Biomed Eng       Date:  2021-09-20       Impact factor: 4.756

5.  A knowledge-based approach to automated planning for hepatocellular carcinoma.

Authors:  Yujie Zhang; Tingting Li; Han Xiao; Weixing Ji; Ming Guo; Zhaochong Zeng; Jianying Zhang
Journal:  J Appl Clin Med Phys       Date:  2017-11-15       Impact factor: 2.102

6.  Evaluation of auto-planning in IMRT and VMAT for head and neck cancer.

Authors:  Zi Ouyang; Zhilei Liu Shen; Eric Murray; Matt Kolar; Danielle LaHurd; Naichang Yu; Nikhil Joshi; Shlomo Koyfman; Karl Bzdusek; Ping Xia
Journal:  J Appl Clin Med Phys       Date:  2019-07-04       Impact factor: 2.102

7.  Individualized automated planning for dose bath reduction in robotic radiosurgery for benign tumors.

Authors:  Linda Rossi; Alejandra Méndez Romero; Maaike Milder; Erik de Klerck; Sebastiaan Breedveld; Ben Heijmen
Journal:  PLoS One       Date:  2019-02-06       Impact factor: 3.240

8.  Comparison of dose metrics between automated and manual radiotherapy planning for advanced stage non-small cell lung cancer with volumetric modulated arc therapy.

Authors:  Iris H P Creemers; Johannes M A M Kusters; Peter G M van Kollenburg; Liza C W Bouwmans; Dominic A X Schinagl; Johan Bussink
Journal:  Phys Imaging Radiat Oncol       Date:  2019-03-18

9.  Plan quality for high-risk prostate cancer treated with high field magnetic resonance imaging guided radiotherapy.

Authors:  Rasmus Lübeck Christiansen; Christian Rønn Hansen; Rikke Hedegaard Dahlrot; Anders Smedegaard Bertelsen; Olfred Hansen; Carsten Brink; Uffe Bernchou
Journal:  Phys Imaging Radiat Oncol       Date:  2018-07-21

10.  Adapting automated treatment planning configurations across international centres for prostate radiotherapy.

Authors:  Dale Roach; Geert Wortel; Cesar Ochoa; Henrik R Jensen; Eugene Damen; Philip Vial; Tomas Janssen; Christian Rønn Hansen
Journal:  Phys Imaging Radiat Oncol       Date:  2019-04-24
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