Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ulanowski, Alexander, Shemiakina, Elena, Güthlin, Denise, Becker, Janine, Preston, Dale, Apostoaei, A. Iulian, Hoffman, F. Owen, Jacob, Peter, Kaiser, Jan Christian, Eidemüller, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
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
_version_ 1783591897381470208
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
work_keys_str_mv AT ulanowskialexander prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT shemiakinaelena prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT guthlindenise prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT beckerjanine prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT prestondale prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT apostoaeiaiulian prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT hoffmanfowen prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT jacobpeter prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT kaiserjanchristian prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence
AT eidemullermarkus prozesthemethodologyandsoftwaretoolforassessmentofassignedshareofradiationinprobabilityofcanceroccurrence