@article{oai:repo.qst.go.jp:00048096,
 author = {谷, 修祐 and Ｂｌｙｔｈ, Ｂｅｎｊａｍｉｎ and 尚, 奕 and 森岡, 孝満 and 柿沼, 志津子 and 島田, 義也 and 谷 修祐 and Ｂｌｙｔｈ Ｂｅｎｊａｍｉｎ and 尚 奕 and 森岡 孝満 and 柿沼 志津子 and 島田 義也},
 issue = {2},
 journal = {Journal of Cancer Prevention},
 month = {Jan},
 note = {Ongoing uncertainty regarding the risk of radiation-induced cancer after low dose exposures adds to anxiety in exposed populations, particularly following accidental exposures where informed consent was not obtained. Safe, effective and simple lifestyle changes which can be proven to help mitigate or offset radiation-induced excess cancer risk might provide individuals exposed to radiation the opportunity to pro-actively reduce their cancer risk, while such empowerment could also improve mental health and well-being. Here, we applied a multi-stage carcinogenesis mathematical model to mouse lifespan and cancer data for an intervention using adult-onset calorie restriction following irradiation in early life. We re-evaluated autopsy records with the assistance of a veterinary pathologist to determine which tumours were the probable cause of death in order to calculate age-specific mortality. The model revealed that in both irradiated and unirradiated mice, calorie restriction reduced the age-specific mortality of all solid tumours and hepatocellular carcinoma across most of the lifespan, with the mortality rate more dependent on age due to an increase in the number of ‘steps’. Conversely, irradiation did not significantly alter the number of steps, but did increase the overall transition rate between the steps. We show that the extent of the protective effect of calorie restriction is independent of the cancer induction from the radiation exposure, and discuss future avenues of research to explore the utility of calorie restriction as an example of a potential post-irradiation mitigation strategy.},
 pages = {115--120},
 title = {A multi-stage carcinogenesis model to investigate calorie restriction as a potential tool for post-irradiation mitigation of cancer risk},
 volume = {21},
 year = {2016}
}