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A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions
The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is use...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366386/ https://www.ncbi.nlm.nih.gov/pubmed/25789764 http://dx.doi.org/10.1371/journal.pone.0120717 |
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author | Cucinotta, Francis A. |
author_facet | Cucinotta, Francis A. |
author_sort | Cucinotta, Francis A. |
collection | PubMed |
description | The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBE(max)), I developed an alternate QF model, denoted QF(γAcute) where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL∼50%) using the QF(γAcute) model compared the QFs based on RBE(max) and about 25% (CL∼35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates. |
format | Online Article Text |
id | pubmed-4366386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-43663862015-03-23 A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions Cucinotta, Francis A. PLoS One Research Article The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBE(max)), I developed an alternate QF model, denoted QF(γAcute) where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL∼50%) using the QF(γAcute) model compared the QFs based on RBE(max) and about 25% (CL∼35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates. Public Library of Science 2015-03-19 /pmc/articles/PMC4366386/ /pubmed/25789764 http://dx.doi.org/10.1371/journal.pone.0120717 Text en © 2015 Francis A. Cucinotta http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Cucinotta, Francis A. A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title | A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title_full | A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title_fullStr | A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title_full_unstemmed | A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title_short | A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions |
title_sort | new approach to reduce uncertainties in space radiation cancer risk predictions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4366386/ https://www.ncbi.nlm.nih.gov/pubmed/25789764 http://dx.doi.org/10.1371/journal.pone.0120717 |
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