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Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model

BACKGROUND: The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model...

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Autores principales: Yu, Gang, Li, Yang, Feng, Ziwei, Tao, Cheng, Yu, Zuyi, Li, Baosheng, Li, Dengwang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870074/
https://www.ncbi.nlm.nih.gov/pubmed/29587782
http://dx.doi.org/10.1186/s13014-018-0996-z
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author Yu, Gang
Li, Yang
Feng, Ziwei
Tao, Cheng
Yu, Zuyi
Li, Baosheng
Li, Dengwang
author_facet Yu, Gang
Li, Yang
Feng, Ziwei
Tao, Cheng
Yu, Zuyi
Li, Baosheng
Li, Dengwang
author_sort Yu, Gang
collection PubMed
description BACKGROUND: The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model (Model-S). METHODS: A library consisting of 60 liver cancer patients with IMRT planning was used to set up two models (Model-S, Model-G), using the RapidPlan knowledge-based planning system. Model-S consisted of 30 patients with special anatomical characteristics where the distance from planning target volume (PTV) to the right kidney was less than three centimeters and Model-G was configurated using all 60 patients in this library. Knowledge-based IMRT plans were created for the evaluation group formed of 13 patients similar to those included in Model-S by Model-G, Model-S and manually (M), named RPG-plans, RPS-plans and M-plans, respectively. The differences in the dose-volume histograms (DVHs) were compared, not only between RP-plans and their respective M-plans, but also between RPG-plans and RPS-plans. RESULTS: For all 13 patients, RapidPlan could automatically produce clinically acceptable plans. Comparing RP-plans to M-plans, RP-plans improved V(95%) of PTV and had greater dose sparing in the right kidney. For the normal liver, RPG-plans delivered similar doses, while RPS-plans delivered a higher dose than M-plans. With respect to RapidPlan models, RPS-plans had better conformity index (CI) values and delivered lower doses to the right kidney V(20Gy) and maximizing point doses to spinal cord, while delivering higher doses to the normal liver. CONCLUSION: The study shows that RapidPlan can create high-quality plans, and our specific model can improve the CI of PTV, resulting in more sparing of OAR in IMRT for individual liver cancer patients.
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spelling pubmed-58700742018-03-29 Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model Yu, Gang Li, Yang Feng, Ziwei Tao, Cheng Yu, Zuyi Li, Baosheng Li, Dengwang Radiat Oncol Research BACKGROUND: The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model (Model-S). METHODS: A library consisting of 60 liver cancer patients with IMRT planning was used to set up two models (Model-S, Model-G), using the RapidPlan knowledge-based planning system. Model-S consisted of 30 patients with special anatomical characteristics where the distance from planning target volume (PTV) to the right kidney was less than three centimeters and Model-G was configurated using all 60 patients in this library. Knowledge-based IMRT plans were created for the evaluation group formed of 13 patients similar to those included in Model-S by Model-G, Model-S and manually (M), named RPG-plans, RPS-plans and M-plans, respectively. The differences in the dose-volume histograms (DVHs) were compared, not only between RP-plans and their respective M-plans, but also between RPG-plans and RPS-plans. RESULTS: For all 13 patients, RapidPlan could automatically produce clinically acceptable plans. Comparing RP-plans to M-plans, RP-plans improved V(95%) of PTV and had greater dose sparing in the right kidney. For the normal liver, RPG-plans delivered similar doses, while RPS-plans delivered a higher dose than M-plans. With respect to RapidPlan models, RPS-plans had better conformity index (CI) values and delivered lower doses to the right kidney V(20Gy) and maximizing point doses to spinal cord, while delivering higher doses to the normal liver. CONCLUSION: The study shows that RapidPlan can create high-quality plans, and our specific model can improve the CI of PTV, resulting in more sparing of OAR in IMRT for individual liver cancer patients. BioMed Central 2018-03-27 /pmc/articles/PMC5870074/ /pubmed/29587782 http://dx.doi.org/10.1186/s13014-018-0996-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Yu, Gang
Li, Yang
Feng, Ziwei
Tao, Cheng
Yu, Zuyi
Li, Baosheng
Li, Dengwang
Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title_full Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title_fullStr Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title_full_unstemmed Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title_short Knowledge-based IMRT planning for individual liver cancer patients using a novel specific model
title_sort knowledge-based imrt planning for individual liver cancer patients using a novel specific model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870074/
https://www.ncbi.nlm.nih.gov/pubmed/29587782
http://dx.doi.org/10.1186/s13014-018-0996-z
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