Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors
Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal...
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Oxford University Press
2014
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4014156/ |
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pubmed-40141562014-05-12 Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors Sasaki, Masao S. Tachibana, Akira Takeda, Shunichi Review Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the ‘integrate-and-fire’ algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to 239Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation–environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking. Oxford University Press 2014-05 2013-12-22 /pmc/articles/PMC4014156/ /pubmed/24366315 http://dx.doi.org/10.1093/jrr/rrt133 Text en © The Author 2013. Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Sasaki, Masao S. Tachibana, Akira Takeda, Shunichi |
| spellingShingle |
Sasaki, Masao S. Tachibana, Akira Takeda, Shunichi Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| author_facet |
Sasaki, Masao S. Tachibana, Akira Takeda, Shunichi |
| author_sort |
Sasaki, Masao S. |
| title |
Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| title_short |
Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| title_full |
Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| title_fullStr |
Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| title_full_unstemmed |
Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| title_sort |
cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors |
| description |
Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the ‘integrate-and-fire’ algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to 239Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation–environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking. |
| publisher |
Oxford University Press |
| publishDate |
2014 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4014156/ |
| _version_ |
1612086634098982912 |