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Incorporating biological modeling into patient‐specific plan verification

PURPOSE: Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability...

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Autores principales: Alexandrian, Ara N., Mavroidis, Panayiotis, Narayanasamy, Ganesh, McConnell, Kristen A., Kabat, Christopher N., George, Renil B., Defoor, Dewayne L., Kirby, Neil, Papanikolaou, Nikos, Stathakis, Sotirios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075379/
https://www.ncbi.nlm.nih.gov/pubmed/32101368
http://dx.doi.org/10.1002/acm2.12831
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author Alexandrian, Ara N.
Mavroidis, Panayiotis
Narayanasamy, Ganesh
McConnell, Kristen A.
Kabat, Christopher N.
George, Renil B.
Defoor, Dewayne L.
Kirby, Neil
Papanikolaou, Nikos
Stathakis, Sotirios
author_facet Alexandrian, Ara N.
Mavroidis, Panayiotis
Narayanasamy, Ganesh
McConnell, Kristen A.
Kabat, Christopher N.
George, Renil B.
Defoor, Dewayne L.
Kirby, Neil
Papanikolaou, Nikos
Stathakis, Sotirios
author_sort Alexandrian, Ara N.
collection PubMed
description PURPOSE: Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. METHODS: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient‐specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. RESULTS: The complication‐free tumor control probability, P (+), has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (−3.9% ± 5.8%), head‐neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). CONCLUSION: Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification.
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spelling pubmed-70753792020-03-17 Incorporating biological modeling into patient‐specific plan verification Alexandrian, Ara N. Mavroidis, Panayiotis Narayanasamy, Ganesh McConnell, Kristen A. Kabat, Christopher N. George, Renil B. Defoor, Dewayne L. Kirby, Neil Papanikolaou, Nikos Stathakis, Sotirios J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: Dose–volume histogram (DVH) measurements have been integrated into commercially available quality assurance systems to provide a metric for evaluating accuracy of delivery in addition to gamma analysis. We hypothesize that tumor control probability and normal tissue complication probability calculations can provide additional insight beyond conventional dose delivery verification methods. METHODS: A commercial quality assurance system was used to generate DVHs of treatment plan using the planning CT images and patient‐specific QA measurements on a phantom. Biological modeling was performed on the DVHs produced by both the treatment planning system and the quality assurance system. RESULTS: The complication‐free tumor control probability, P (+), has been calculated for previously treated intensity modulated radiotherapy (IMRT) patients with diseases in the following sites: brain (−3.9% ± 5.8%), head‐neck (+4.8% ± 8.5%), lung (+7.8% ± 1.3%), pelvis (+7.1% ± 12.1%), and prostate (+0.5% ± 3.6%). CONCLUSION: Dose measurements on a phantom can be used for pretreatment estimation of tumor control and normal tissue complication probabilities. Results in this study show how biological modeling can be used to provide additional insight about accuracy of delivery during pretreatment verification. John Wiley and Sons Inc. 2020-02-26 /pmc/articles/PMC7075379/ /pubmed/32101368 http://dx.doi.org/10.1002/acm2.12831 Text en © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Alexandrian, Ara N.
Mavroidis, Panayiotis
Narayanasamy, Ganesh
McConnell, Kristen A.
Kabat, Christopher N.
George, Renil B.
Defoor, Dewayne L.
Kirby, Neil
Papanikolaou, Nikos
Stathakis, Sotirios
Incorporating biological modeling into patient‐specific plan verification
title Incorporating biological modeling into patient‐specific plan verification
title_full Incorporating biological modeling into patient‐specific plan verification
title_fullStr Incorporating biological modeling into patient‐specific plan verification
title_full_unstemmed Incorporating biological modeling into patient‐specific plan verification
title_short Incorporating biological modeling into patient‐specific plan verification
title_sort incorporating biological modeling into patient‐specific plan verification
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7075379/
https://www.ncbi.nlm.nih.gov/pubmed/32101368
http://dx.doi.org/10.1002/acm2.12831
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