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On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy

Stereotactic body radiation therapy (SBRT) aims to deliver a highly conformal ablative dose to a small target. Dosimetric verification of SBRT for lung tumors presents a challenge due to heterogeneities, moving targets, and small fields. Recent software (M3D) designed for dosimetric verification of...

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Autores principales: Hardcastle, Nicholas, Oborn, Bradley M., Haworth, Annette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874108/
https://www.ncbi.nlm.nih.gov/pubmed/27685114
http://dx.doi.org/10.1120/jacmp.v17i5.6186
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author Hardcastle, Nicholas
Oborn, Bradley M.
Haworth, Annette
author_facet Hardcastle, Nicholas
Oborn, Bradley M.
Haworth, Annette
author_sort Hardcastle, Nicholas
collection PubMed
description Stereotactic body radiation therapy (SBRT) aims to deliver a highly conformal ablative dose to a small target. Dosimetric verification of SBRT for lung tumors presents a challenge due to heterogeneities, moving targets, and small fields. Recent software (M3D) designed for dosimetric verification of lung SBRT treatment plans using an advanced convolution–superposition algorithm was evaluated. Ten lung SBRT patients covering a range of tumor volumes were selected. 3D CRT plans were created using the XiO treatment planning system (TPS) with the superposition algorithm. Dose was recalculated in the Eclipse TPS using the AAA algorithm, M3D verification software using the collapsed‐cone‐convolution algorithm, and in‐house Monte Carlo (MC). Target point doses were calculated with RadCalc software. Near‐maximum, median, and near‐minimum target doses, conformity indices, and lung doses were compared with MC as the reference calculation. M3D 3D gamma passing rates were compared with the XiO and Eclipse. Wilcoxon signed‐rank test was used to compare each calculation method with XiO with a threshold of significance of [Formula: see text]. M3D and RadCalc point dose calculations were greater than MC by up to 7.7% and 13.1%, respectively, with M3D being statistically significant (s.s.). AAA and XiO calculated point doses were less than MC by 11.3% and 5.2%, respectively (AAA s.s.). Median and near‐minimum and near‐maximum target doses were less than MC when calculated with AAA and XiO (all s.s.). Near‐maximum and median target doses were higher with M3D compared with MC (s.s.), but there was no difference in near‐minimum M3D doses compared with MC. M3D‐calculated ipsilateral lung V20 Gy and V5 Gy were greater than that calculated with MC (s.s.); AAA‐ and XiO‐calculated V20 Gy was lower than that calculated with MC, but not statistically different to MC for V5 Gy. Nine of the 10 plans achieved M3D gamma passing rates greater than 95% and 80%for [Formula: see text] and [Formula: see text] criteria, respectively. M3D typically calculated a higher target and lung dose than MC for lung SBRT plans. The results show a range of calculated doses with different algorithms and suggest that M3D is in closer agreement with Monte Carlo, thus discrepancies between the TPS and M3D software will be observed for lung SBRT plans. M3D provides a useful supplement to verification of lung SBRT plans by direct measurement, which typically excludes patient specific heterogeneities. PACS number(s): 87.55.D‐, 87.55.Qr, 87.55.K‐
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spelling pubmed-58741082018-04-02 On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy Hardcastle, Nicholas Oborn, Bradley M. Haworth, Annette J Appl Clin Med Phys Radiation Oncology Physics Stereotactic body radiation therapy (SBRT) aims to deliver a highly conformal ablative dose to a small target. Dosimetric verification of SBRT for lung tumors presents a challenge due to heterogeneities, moving targets, and small fields. Recent software (M3D) designed for dosimetric verification of lung SBRT treatment plans using an advanced convolution–superposition algorithm was evaluated. Ten lung SBRT patients covering a range of tumor volumes were selected. 3D CRT plans were created using the XiO treatment planning system (TPS) with the superposition algorithm. Dose was recalculated in the Eclipse TPS using the AAA algorithm, M3D verification software using the collapsed‐cone‐convolution algorithm, and in‐house Monte Carlo (MC). Target point doses were calculated with RadCalc software. Near‐maximum, median, and near‐minimum target doses, conformity indices, and lung doses were compared with MC as the reference calculation. M3D 3D gamma passing rates were compared with the XiO and Eclipse. Wilcoxon signed‐rank test was used to compare each calculation method with XiO with a threshold of significance of [Formula: see text]. M3D and RadCalc point dose calculations were greater than MC by up to 7.7% and 13.1%, respectively, with M3D being statistically significant (s.s.). AAA and XiO calculated point doses were less than MC by 11.3% and 5.2%, respectively (AAA s.s.). Median and near‐minimum and near‐maximum target doses were less than MC when calculated with AAA and XiO (all s.s.). Near‐maximum and median target doses were higher with M3D compared with MC (s.s.), but there was no difference in near‐minimum M3D doses compared with MC. M3D‐calculated ipsilateral lung V20 Gy and V5 Gy were greater than that calculated with MC (s.s.); AAA‐ and XiO‐calculated V20 Gy was lower than that calculated with MC, but not statistically different to MC for V5 Gy. Nine of the 10 plans achieved M3D gamma passing rates greater than 95% and 80%for [Formula: see text] and [Formula: see text] criteria, respectively. M3D typically calculated a higher target and lung dose than MC for lung SBRT plans. The results show a range of calculated doses with different algorithms and suggest that M3D is in closer agreement with Monte Carlo, thus discrepancies between the TPS and M3D software will be observed for lung SBRT plans. M3D provides a useful supplement to verification of lung SBRT plans by direct measurement, which typically excludes patient specific heterogeneities. PACS number(s): 87.55.D‐, 87.55.Qr, 87.55.K‐ John Wiley and Sons Inc. 2016-09-08 /pmc/articles/PMC5874108/ /pubmed/27685114 http://dx.doi.org/10.1120/jacmp.v17i5.6186 Text en © 2016 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
Hardcastle, Nicholas
Oborn, Bradley M.
Haworth, Annette
On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title_full On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title_fullStr On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title_full_unstemmed On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title_short On the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
title_sort on the use of a convolution–superposition algorithm for plan checking in lung stereotactic body radiation therapy
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874108/
https://www.ncbi.nlm.nih.gov/pubmed/27685114
http://dx.doi.org/10.1120/jacmp.v17i5.6186
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