Abstract
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.
Highlights
Ionising radiation is known to induce or contribute to longterm health effects
These biologically based models are not included in the current version of ProZES, because, at the time when the ProZES development started, concerns were raised whether these mechanistic models would be accepted widely enough to allow for their use in compensation claims
The generalized models provide a significantly better fit to the data, where the excess relative risk (ERR) depends on smoking intensity in a non-linear way: ERR increases with increasing smoking intensity up to about 5–10 cigarettes per day, it decreases strongly and almost vanishes for more than 20–25 cpd
Summary
Ionising radiation is known to induce or contribute to longterm health effects. If a person with a history of exposure to radiation is diagnosed with cancer, it is possible that the disease is related to the preceding exposure. Since cancer induction and promotion by ionising radiation is a fundamentally stochastic process, the relationship between radiation and cancer can only be expressed by probabilities. The probability that the observed cancer in an exposed person may be caused by past exposure to radiation is called the assigned share. The assigned share is derived based on risk models obtained from radioepidemiological studies and depends on type of cancer, age at cancer occurrence, exposure history, and other personal factors.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
