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Journal Articles

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, Shinichi*; Takagi, Shunji*; Takahara, Shogo; Kai, Michiaki*

Journal of Radiation Protection and Research, 48(2), p.90 - 99, 2023/06

This paper reports on the calculation code that is the result of the activities of the "Task Group for Development of Cancer Risk Estimation Codes Associated with Radiation Exposure (FY2020-2021)" established by the Japan Health Physics Society. In order to promote research on the estimation of cancer risk associated with radiation exposure, the Task Group decided to disclose the source code, including the algorithm and parameters used in the calculations, and to release the code under a license that permits modification and redistribution of the code. The computational code was named SUMRAY and coded in two computer languages, that is R and Python. The code is capable of calculating the accumulated excess risk using Monte Carlo methods with a 95% confidence interval. The results of SUMRAY were compared with the results of the existing codes whose source code is not publicly available, under the same calculation conditions. From the results, it was found that they were in reasonable agreement within the confidence interval. It is expected that SUMRAY, an open-source software, will be used as a common basis for cancer risk estimation studies associated with radiation exposure.

Oral presentation

Task Group for the Development of Estimation Codes for Cancer Risk caused by Radiation Exposure

Sasaki, Michiya*; Takagi, Shunji*; Kai, Michiaki*; Furukawa, Kyoji*; Kawaguchi, Isao*; Kudo, Shinichi*; Takahara, Shogo; Otoshi, Kazuki*; Shimada, Kazumasa; Satoh, Daiki

no journal, , 

To contribute to the quantitative discussion of risks associated with low-dose radiation exposure, the Japan Society of Health Physics established the "Task Group for the Development of Estimation Codes for Cancer Risk caused by Radiation Exposure". This presentation will report on the activities conducted during the two-year period from FY2020 to FY2021.

Oral presentation

Development of cancer risk estimation code due to radiation exposure, 2; R and Python codes

Satoh, Daiki; Sasaki, Michiya*; Furukawa, Kyoji*; Shimada, Kazumasa; Kudo, Shinichi*; Takahara, Shogo; Takagi, Shunji*; Kai, Michiaki*

no journal, , 

no abstracts in English

Oral presentation

Development of cancer risk estimation code due to radiation exposure, 1; Outline and feature of the risk calculation

Sasaki, Michiya*; Furukawa, Kyoji*; Satoh, Daiki; Shimada, Kazumasa; Kudo, Shinichi*; Takahara, Shogo; Takagi, Shunji*; Kai, Michiaki*

no journal, , 

no abstracts in English

Oral presentation

Development of cancer risk estimation code due to radiation exposure, 3; Verification and perspective

Kai, Michiaki*; Shimada, Kazumasa; Kudo, Shinichi*; Furukawa, Kyoji*; Satoh, Daiki; Takahara, Shogo; Takagi, Shunji*; Sasaki, Michiya*

no journal, , 

This presentation is the third in a series of three presentations on the results of the "Expert Group on the Development of Cancer Risk Estimation Codes Associated with Radiation Exposure" established by the Japan Health Physics Society, which examines the parameters employed in the codes and the calculation results, and summarizes the future prospects of the codes. In order to calculate cumulative excess risk (CER) as a measure of lifetime risk with confidence intervals, a variance-covariance matrix for the parameters of the risk model is required. However, in a study conducted by the Radiation Effects Research Foundation using the regression analysis program Epicure on large-scale epidemiological data from A-bomb survivors, the variance-covariance matrix was not disclosed. Therefore, in this study, we independently derived parameters and covariance matrices using a generalized linear model (GLM) and confirmed that the parameter values agreed well with the Epicure results. The CERs calculated using the derived parameters and covariance matrices were compared with those calculated by the U.S. code RadRAT. Although a simple comparison is not possible due to the different population baselines of the two codes, they were found to be in general agreement. The developed code is expected to contribute to the discussion of influence factors and uncertainty in risk assessment.

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