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KMID : 0364920230480020090
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Journal of Radiation Protection and Research
2023 Volume.48 No. 2 p.90 ~ p.99
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SUMRAY: R and Python Codes for Calculating Cancer Risk Due to Radiation Exposure of a Population
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Michiya Sasaki
Kyoji Furukawa
Daiki Satoh
Kazumasa Shimada
Shin′ichi Kudo
Shunji Takagi
Shogo Takahara
Michiaki Kai
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Abstract
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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 account 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 statistical uncertainties in the risk model parameter estimates by the Monte Carlo method.
Results and Discussion : SUMRAY was used to calculate the lifetime or time-integrated attributable 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 (RadRAT; 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 applications, as it allows the risk projection with arbitrarily specified risk models and/or population 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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KEYWORD
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R, Python, Radiation Exposure, Lifetime Attributable Risk, Risk Model, Variance-Covariance Matrix
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