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Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses

In breast cancer radiotherapy, substantial radiation exposure of organs other than the treated breast cannot be avoided, potentially inducing second primary cancer or heart disease. While distant organs and large parts of nearby ones receive doses in the mGy–Gy range, small parts of the heart, lung...

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Autores principales: Simonetto, Cristoforo, Wollschläger, Daniel, Kundrát, Pavel, Ulanowski, Alexander, Becker, Janine, Castelletti, Noemi, Güthlin, Denise, Shemiakina, Elena, Eidemüller, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310522/
https://www.ncbi.nlm.nih.gov/pubmed/34275005
http://dx.doi.org/10.1007/s00411-021-00924-8
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author Simonetto, Cristoforo
Wollschläger, Daniel
Kundrát, Pavel
Ulanowski, Alexander
Becker, Janine
Castelletti, Noemi
Güthlin, Denise
Shemiakina, Elena
Eidemüller, Markus
author_facet Simonetto, Cristoforo
Wollschläger, Daniel
Kundrát, Pavel
Ulanowski, Alexander
Becker, Janine
Castelletti, Noemi
Güthlin, Denise
Shemiakina, Elena
Eidemüller, Markus
author_sort Simonetto, Cristoforo
collection PubMed
description In breast cancer radiotherapy, substantial radiation exposure of organs other than the treated breast cannot be avoided, potentially inducing second primary cancer or heart disease. While distant organs and large parts of nearby ones receive doses in the mGy–Gy range, small parts of the heart, lung and bone marrow often receive doses as high as 50 Gy. Contemporary treatment planning allows for considerable flexibility in the distribution of this exposure. To optimise treatment with regards to long-term health risks, evidence-based risk estimates are required for the entire broad range of exposures. Here, we thus propose an approach that combines data from medical and epidemiological studies with different exposure conditions. Approximating cancer induction as a local process, we estimate organ cancer risks by integrating organ-specific dose–response relationships over the organ dose distributions. For highly exposed organ parts, specific high-dose risk models based on studies with medical exposure are applied. For organs or their parts receiving relatively low doses, established dose–response models based on radiation-epidemiological data are used. Joining the models in the intermediate dose range leads to a combined, in general non-linear, dose response supported by data over the whole relevant dose range. For heart diseases, a linear model consistent with high- and low-dose studies is presented. The resulting estimates of long-term health risks are largely compatible with rate ratios observed in randomised breast cancer radiotherapy trials. The risk models have been implemented in a software tool PASSOS that estimates long-term risks for individual breast cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00411-021-00924-8.
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spelling pubmed-83105222021-08-12 Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses Simonetto, Cristoforo Wollschläger, Daniel Kundrát, Pavel Ulanowski, Alexander Becker, Janine Castelletti, Noemi Güthlin, Denise Shemiakina, Elena Eidemüller, Markus Radiat Environ Biophys Original Article In breast cancer radiotherapy, substantial radiation exposure of organs other than the treated breast cannot be avoided, potentially inducing second primary cancer or heart disease. While distant organs and large parts of nearby ones receive doses in the mGy–Gy range, small parts of the heart, lung and bone marrow often receive doses as high as 50 Gy. Contemporary treatment planning allows for considerable flexibility in the distribution of this exposure. To optimise treatment with regards to long-term health risks, evidence-based risk estimates are required for the entire broad range of exposures. Here, we thus propose an approach that combines data from medical and epidemiological studies with different exposure conditions. Approximating cancer induction as a local process, we estimate organ cancer risks by integrating organ-specific dose–response relationships over the organ dose distributions. For highly exposed organ parts, specific high-dose risk models based on studies with medical exposure are applied. For organs or their parts receiving relatively low doses, established dose–response models based on radiation-epidemiological data are used. Joining the models in the intermediate dose range leads to a combined, in general non-linear, dose response supported by data over the whole relevant dose range. For heart diseases, a linear model consistent with high- and low-dose studies is presented. The resulting estimates of long-term health risks are largely compatible with rate ratios observed in randomised breast cancer radiotherapy trials. The risk models have been implemented in a software tool PASSOS that estimates long-term risks for individual breast cancer patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00411-021-00924-8. Springer Berlin Heidelberg 2021-07-17 2021 /pmc/articles/PMC8310522/ /pubmed/34275005 http://dx.doi.org/10.1007/s00411-021-00924-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Simonetto, Cristoforo
Wollschläger, Daniel
Kundrát, Pavel
Ulanowski, Alexander
Becker, Janine
Castelletti, Noemi
Güthlin, Denise
Shemiakina, Elena
Eidemüller, Markus
Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title_full Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title_fullStr Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title_full_unstemmed Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title_short Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
title_sort estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8310522/
https://www.ncbi.nlm.nih.gov/pubmed/34275005
http://dx.doi.org/10.1007/s00411-021-00924-8
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