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Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT
Current commercially available planning systems with Monte Carlo (MC)‐based final dose calculation in IMRT planning employ pencil‐beam (PB) algorithms in the optimization process. Consequently, dose coverage for SBRT lung plans can feature cold‐spots at the interface between lung and tumor tissue. F...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718552/ https://www.ncbi.nlm.nih.gov/pubmed/23149794 http://dx.doi.org/10.1120/jacmp.v13i6.4007 |
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author | Altman, Michael B. Jin, Jian‐Yue Kim, Sangroh Wen, Ning Liu, Dezhi Siddiqui, M. Salim Ajlouni, Munther I. Movsas, Benjamin Chetty, Indrin J. |
author_facet | Altman, Michael B. Jin, Jian‐Yue Kim, Sangroh Wen, Ning Liu, Dezhi Siddiqui, M. Salim Ajlouni, Munther I. Movsas, Benjamin Chetty, Indrin J. |
author_sort | Altman, Michael B. |
collection | PubMed |
description | Current commercially available planning systems with Monte Carlo (MC)‐based final dose calculation in IMRT planning employ pencil‐beam (PB) algorithms in the optimization process. Consequently, dose coverage for SBRT lung plans can feature cold‐spots at the interface between lung and tumor tissue. For lung wall (LW)‐seated tumors, there can also be hot spots within nearby normal organs (example: ribs). This study evaluated two different practical approaches to limiting cold spots within the target and reducing high doses to surrounding normal organs in MC‐based IMRT planning of LW‐seated tumors. First, “iterative reoptimization”, where the MC calculation (with PB‐based optimization) is initially performed. The resultant cold spot is then contoured and used as a simultaneous boost volume. The MC‐based dose is then recomputed. The second technique uses noncoplanar beam angles with limited path through lung tissue. Both techniques were evaluated against a conventional coplanar beam approach with a single MC calculation. In all techniques the prescription dose was normalized to cover 95% of the PTV. Fifteen SBRT lung cases with LW‐seated tumors were planned. The results from iterative reoptimization showed that conformity index (CI) and/or PTV dose uniformity ([Formula: see text]) improved in 12/15 plans. Average improvement was 13%, and 24%, respectively. Nonimproved plans had PTVs near the skin, trachea, and/or very small lung involvement. The maximum dose to 1cc volume (D1cc) of surrounding OARs decreased in 14/15 plans (average 10%). Using noncoplanar beams showed an average improvement of 7% in 10/15 cases and 11% in 5/15 cases for CI and [Formula: see text] , respectively. The D1cc was reduced by an average of 6% in 10/15 cases to surrounding OARs. Choice of treatment planning technique did not statistically significantly change lung V5. The results showed that the proposed practical approaches enhance dose conformity in MC‐based IMRT planning of lung tumors treated with SBRT, improving target dose coverage and potentially reducing toxicities to surrounding normal organs. PACS numbers: 87.55.de, 87.55.kh |
format | Online Article Text |
id | pubmed-5718552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-57185522018-04-02 Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT Altman, Michael B. Jin, Jian‐Yue Kim, Sangroh Wen, Ning Liu, Dezhi Siddiqui, M. Salim Ajlouni, Munther I. Movsas, Benjamin Chetty, Indrin J. J Appl Clin Med Phys Radiation Oncology Physics Current commercially available planning systems with Monte Carlo (MC)‐based final dose calculation in IMRT planning employ pencil‐beam (PB) algorithms in the optimization process. Consequently, dose coverage for SBRT lung plans can feature cold‐spots at the interface between lung and tumor tissue. For lung wall (LW)‐seated tumors, there can also be hot spots within nearby normal organs (example: ribs). This study evaluated two different practical approaches to limiting cold spots within the target and reducing high doses to surrounding normal organs in MC‐based IMRT planning of LW‐seated tumors. First, “iterative reoptimization”, where the MC calculation (with PB‐based optimization) is initially performed. The resultant cold spot is then contoured and used as a simultaneous boost volume. The MC‐based dose is then recomputed. The second technique uses noncoplanar beam angles with limited path through lung tissue. Both techniques were evaluated against a conventional coplanar beam approach with a single MC calculation. In all techniques the prescription dose was normalized to cover 95% of the PTV. Fifteen SBRT lung cases with LW‐seated tumors were planned. The results from iterative reoptimization showed that conformity index (CI) and/or PTV dose uniformity ([Formula: see text]) improved in 12/15 plans. Average improvement was 13%, and 24%, respectively. Nonimproved plans had PTVs near the skin, trachea, and/or very small lung involvement. The maximum dose to 1cc volume (D1cc) of surrounding OARs decreased in 14/15 plans (average 10%). Using noncoplanar beams showed an average improvement of 7% in 10/15 cases and 11% in 5/15 cases for CI and [Formula: see text] , respectively. The D1cc was reduced by an average of 6% in 10/15 cases to surrounding OARs. Choice of treatment planning technique did not statistically significantly change lung V5. The results showed that the proposed practical approaches enhance dose conformity in MC‐based IMRT planning of lung tumors treated with SBRT, improving target dose coverage and potentially reducing toxicities to surrounding normal organs. PACS numbers: 87.55.de, 87.55.kh John Wiley and Sons Inc. 2012-11-08 /pmc/articles/PMC5718552/ /pubmed/23149794 http://dx.doi.org/10.1120/jacmp.v13i6.4007 Text en © 2012 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Radiation Oncology Physics Altman, Michael B. Jin, Jian‐Yue Kim, Sangroh Wen, Ning Liu, Dezhi Siddiqui, M. Salim Ajlouni, Munther I. Movsas, Benjamin Chetty, Indrin J. Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title | Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title_full | Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title_fullStr | Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title_full_unstemmed | Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title_short | Practical methods for improving dose distributions in Monte Carlo‐based IMRT planning of lung wall‐seated tumors treated with SBRT |
title_sort | practical methods for improving dose distributions in monte carlo‐based imrt planning of lung wall‐seated tumors treated with sbrt |
topic | Radiation Oncology Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718552/ https://www.ncbi.nlm.nih.gov/pubmed/23149794 http://dx.doi.org/10.1120/jacmp.v13i6.4007 |
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