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ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence
ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lun...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544726/ https://www.ncbi.nlm.nih.gov/pubmed/32851496 http://dx.doi.org/10.1007/s00411-020-00866-7 |
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author | Ulanowski, Alexander Shemiakina, Elena Güthlin, Denise Becker, Janine Preston, Dale Apostoaei, A. Iulian Hoffman, F. Owen Jacob, Peter Kaiser, Jan Christian Eidemüller, Markus |
author_facet | Ulanowski, Alexander Shemiakina, Elena Güthlin, Denise Becker, Janine Preston, Dale Apostoaei, A. Iulian Hoffman, F. Owen Jacob, Peter Kaiser, Jan Christian Eidemüller, Markus |
author_sort | Ulanowski, Alexander |
collection | PubMed |
description | ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00411-020-00866-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7544726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-75447262020-10-19 ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence Ulanowski, Alexander Shemiakina, Elena Güthlin, Denise Becker, Janine Preston, Dale Apostoaei, A. Iulian Hoffman, F. Owen Jacob, Peter Kaiser, Jan Christian Eidemüller, Markus Radiat Environ Biophys Original Article ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00411-020-00866-7) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-08-26 2020 /pmc/articles/PMC7544726/ /pubmed/32851496 http://dx.doi.org/10.1007/s00411-020-00866-7 Text en © The Author(s) 2020 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/. |
spellingShingle | Original Article Ulanowski, Alexander Shemiakina, Elena Güthlin, Denise Becker, Janine Preston, Dale Apostoaei, A. Iulian Hoffman, F. Owen Jacob, Peter Kaiser, Jan Christian Eidemüller, Markus ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title | ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title_full | ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title_fullStr | ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title_full_unstemmed | ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title_short | ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
title_sort | prozes: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544726/ https://www.ncbi.nlm.nih.gov/pubmed/32851496 http://dx.doi.org/10.1007/s00411-020-00866-7 |
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