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Photon beam modeling variations predict errors in IMRT dosimetry audits

BACKGROUND & PURPOSE: To evaluate treatment planning system (TPS) beam modeling parameters as contributing factors to IMRT audit performance. MATERIALS & METHODS: We retrospectively analyzed IROC Houston phantom audit performance and concurrent beam modeling survey responses from 337 irradia...

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Autores principales: Glenn, Mallory C., Brooks, Fre’Etta, Peterson, Christine B., Howell, Rebecca M., Followill, David S., Pollard-Larkin, Julianne M., Kry, Stephen F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863621/
https://www.ncbi.nlm.nih.gov/pubmed/34748857
http://dx.doi.org/10.1016/j.radonc.2021.10.021
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author Glenn, Mallory C.
Brooks, Fre’Etta
Peterson, Christine B.
Howell, Rebecca M.
Followill, David S.
Pollard-Larkin, Julianne M.
Kry, Stephen F.
author_facet Glenn, Mallory C.
Brooks, Fre’Etta
Peterson, Christine B.
Howell, Rebecca M.
Followill, David S.
Pollard-Larkin, Julianne M.
Kry, Stephen F.
author_sort Glenn, Mallory C.
collection PubMed
description BACKGROUND & PURPOSE: To evaluate treatment planning system (TPS) beam modeling parameters as contributing factors to IMRT audit performance. MATERIALS & METHODS: We retrospectively analyzed IROC Houston phantom audit performance and concurrent beam modeling survey responses from 337 irradiations performed between August 2017 and November 2019. Irradiation results were grouped based on the reporting of typical or atypical beam modeling parameter survey responses (<10th or >90th percentile values), and compared for passing versus failing (>7% error) or “poor” (>5% error) irradiation status. Additionally, we assessed the impact on the planned dose distribution from variations in modeling parameter value. Finally, we estimated the overall impact of beam modeling parameter variance on dose calculations, based on reported community variations. RESULTS: Use of atypical modeling parameters were more frequently seen with failing phantom audit results (p = 0.01). Most pronounced was for Eclipse AAA users, where phantom irradiations with atypical values of dosimetric leaf gap (DLG) showed a greater incidence of both poor-performing (p = 0.048) and failing phantom audits (p = 0.014); and in general, DLG value was correlated with dose calculation accuracy (r = 0.397, p < 0.001). Manipulating TPS parameters induced systematic changes in planned dose distributions which were consistent with prior observations of how failures manifest. Dose change estimations based on these dose calculations agreed well with true dosimetric errors identified. CONCLUSION: Atypical TPS beam modeling parameters are associated with failing phantom audits. This is identified as an important factor contributing to the observed failing phantom results, and highlights the need for accurate beam modeling.
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spelling pubmed-88636212022-02-23 Photon beam modeling variations predict errors in IMRT dosimetry audits Glenn, Mallory C. Brooks, Fre’Etta Peterson, Christine B. Howell, Rebecca M. Followill, David S. Pollard-Larkin, Julianne M. Kry, Stephen F. Radiother Oncol Article BACKGROUND & PURPOSE: To evaluate treatment planning system (TPS) beam modeling parameters as contributing factors to IMRT audit performance. MATERIALS & METHODS: We retrospectively analyzed IROC Houston phantom audit performance and concurrent beam modeling survey responses from 337 irradiations performed between August 2017 and November 2019. Irradiation results were grouped based on the reporting of typical or atypical beam modeling parameter survey responses (<10th or >90th percentile values), and compared for passing versus failing (>7% error) or “poor” (>5% error) irradiation status. Additionally, we assessed the impact on the planned dose distribution from variations in modeling parameter value. Finally, we estimated the overall impact of beam modeling parameter variance on dose calculations, based on reported community variations. RESULTS: Use of atypical modeling parameters were more frequently seen with failing phantom audit results (p = 0.01). Most pronounced was for Eclipse AAA users, where phantom irradiations with atypical values of dosimetric leaf gap (DLG) showed a greater incidence of both poor-performing (p = 0.048) and failing phantom audits (p = 0.014); and in general, DLG value was correlated with dose calculation accuracy (r = 0.397, p < 0.001). Manipulating TPS parameters induced systematic changes in planned dose distributions which were consistent with prior observations of how failures manifest. Dose change estimations based on these dose calculations agreed well with true dosimetric errors identified. CONCLUSION: Atypical TPS beam modeling parameters are associated with failing phantom audits. This is identified as an important factor contributing to the observed failing phantom results, and highlights the need for accurate beam modeling. 2022-01 2021-11-05 /pmc/articles/PMC8863621/ /pubmed/34748857 http://dx.doi.org/10.1016/j.radonc.2021.10.021 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Glenn, Mallory C.
Brooks, Fre’Etta
Peterson, Christine B.
Howell, Rebecca M.
Followill, David S.
Pollard-Larkin, Julianne M.
Kry, Stephen F.
Photon beam modeling variations predict errors in IMRT dosimetry audits
title Photon beam modeling variations predict errors in IMRT dosimetry audits
title_full Photon beam modeling variations predict errors in IMRT dosimetry audits
title_fullStr Photon beam modeling variations predict errors in IMRT dosimetry audits
title_full_unstemmed Photon beam modeling variations predict errors in IMRT dosimetry audits
title_short Photon beam modeling variations predict errors in IMRT dosimetry audits
title_sort photon beam modeling variations predict errors in imrt dosimetry audits
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863621/
https://www.ncbi.nlm.nih.gov/pubmed/34748857
http://dx.doi.org/10.1016/j.radonc.2021.10.021
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