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- 저자 : Shimada Kazumasa (검색결과 2 건)
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Shimada, Kazumasa,Kai, Michiaki The Korean Association for Radiation Protection 2021 방사선방어학회지 Vol.46 No.3
Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts. Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model). Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%-60%) for males and 24% (18%-29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information. Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.
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SUMRAY: R and Python Codes for Calculating Cancer Risk Due to Radiation Exposure of a Population
Sasaki Michiya,Furukawa Kyoji,Satoh Daiki,Shimada Kazumasa,Kudo Shin’ichi,Takagi Shunji,Takahara Shogo,Kai Michiaki 대한방사선방어학회 2023 방사선방어학회지 Vol.48 No.2
Background : Quantitative risk assessments should be accompanied by uncertainty analyses of the risk models employed in the calculations . In this study, we aim to develop a computational code named SUMRAY for use in cancer risk projections from radiation exposure taking into ac- count uncertainties . We also aim to make SUMRAY publicly available as a resource for further improvement of risk projection . Materials and Methods : SUMRAY has two versions of code written in R and Python . The risk models used in SUMRAY for all-solid-cancer mortality and incidence were those published in the Life Span Study of a cohort of the atomic bomb survivors in Hiroshima and Nagasaki . The confidence intervals associated with the evaluated risks were derived by propagating the statisti- cal uncertainties in the risk model parameter estimates by the Monte Carlo method . JRPR Results and Discussion : SUMRAY was used to calculate the lifetime or time-integrated attrib- utable risks of cancer under an exposure scenario (baseline rates , dose[s] , age[s] at exposure , age at the end of follow-up, sex) specified by the user. The results were compared with those calculated using another well-known web-based tool , Radiation Risk Assessment Tool (Rad- RAT; National Institutes of Health) , and showed a reasonable agreement within the estimated confidential interval . Compared with RadRAT, SUMRAY can be used for a wide range of ap- plications , as it allows the risk projection with arbitrarily specified risk models and/or popula- tion reference data . Conclusion : The reliabilities of SUMRAY with the present risk-model parameters and their variance-covariance matrices were verified by comparing them with those of the other codes . The SUMRAY code is distributed to the public as an open-source code under the Massachusetts Institute of Technology license .
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