Biologically-based mechanistic models of radiation-related carcinogenesis applied to epidemiological data. (3rd October 2017)

• Record Type: Journal Article
• Authors: Rühm, Werner; Eidemüller, Markus; Kaiser, Jan Christian
• Is Part Of: International journal of radiation biology. Volume 93:Number 10(2017:Oct.)
• Page Start: 1093
• Page End: 1117
• Publication Date: 2017-10-03
• Subjects: Human carcinogenesis -- biologically-based mechanistic model -- two-stage clonal expansion model -- radiation risk; Radiation -- Physiological effect -- Periodicals; Radiobiology -- Periodicals; 571.45
• DOI: 10.1080/09553002.2017.1310405
• Languages: English
• ISSNs: 0955-3002

Abstract

Abstract: Purpose: Biologically-based mechanistic models that are used in combining current understanding of human carcinogenesis with epidemiological studies were reviewed. Assessment was made of how well they fit the data, whether they account for non-linear radiobiological low-dose effects, and whether they suggest any implications for the dose response at low doses and dose rates. However, the present paper does not make an attempt to provide a complete review of the existing literature on biologically-based models and their application to epidemiological data. Conclusion: In most studies the two-stage clonal expansion (TSCE) model of carcinogenesis was used. The model provided robust estimates of identifiable parameters and radiation risk. While relatively simple, it is flexible, so that more stages can easily be added, and tests made of various types of radiation action. In general, the model performed similarly or better than descriptive excess absolute and excess relative risk models, in terms of quality of fit and number of parameters. Only very rarely the shape of dose-response predicted by the models was investigated. For some tumors, when more detailed biological information was known, additional pathways were included in the model. The future development of these models will benefit from growing knowledge on carcinogenesis processes, and in particular from use of biobank tissue samples and advances in omics technologies. Their use appears a promising approach to investigate the radiation risk at low doses and low dose rates. However, the uncertainties involved are still considerable, and the models provide only a simplified description of the underlying complexity of carcinogenesis. Current assumptions in radiation protection including the linear-non-threshold (LNT) model are not in contradiction to what is presently known on the process of cancer development.