Independent analysis of the radiation risk for leukaemia in children and adults with mortality data (1950-2003) of Japanese A-bomb survivors

Improving astronaut cancer risk assessment from space radiation with an ensemble model framework

A new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty as well as model-form uncertainty associated with differing theoretical or empirical formalisms. Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future models, improve uncertainty quantification, and reduce the impact of subjective bias. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met for future space exploration missions, such as human missions to Mars. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA's current operational cancer risk projection model used to assess permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95th confidence level where models must extrapolate beyond available biological data sets. Closer agreement is seen at the median ± one sigma due to the inherent similarities in available models. Identification of potential new models, epidemiological data, and methods for statistical correlation between predictive ensemble members are discussed. Alternate ways of communicating risk and acceptable uncertainty with respect to NASA's current permissible exposure limits are explored.

ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence

ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany.

Radio-biologically motivated modeling of radiation risks of mortality from ischemic heart diseases in the Canadian fluoroscopy cohort study

Recent analyses of the Canadian fluoroscopy cohort study reported significantly increased radiation risks of mortality from ischemic heart diseases (IHD) with a linear dose-response adjusted for dose fractionation. This cohort includes 63,707 tuberculosis patients from Canada who were exposed to low-to-moderate dose fractionated X-rays in 1930s-1950s and were followed-up for death from non-cancer causes during 1950-1987. In the current analysis, we scrutinized the assumption of linearity by analyzing a series of radio-biologically motivated nonlinear dose-response models to get a better understanding of the impact of radiation damage on IHD. The models were weighted according to their quality of fit and were then mathematically superposed applying the multi-model inference (MMI) technique. Our results indicated an essentially linear dose-response relationship for IHD mortality at low and medium doses and a supra-linear relationship at higher doses (> 1.5 Gy). At 5 Gy, the estimated radiation risks were fivefold higher compared to the linear no-threshold (LNT) model. This is the largest study of patients exposed to fractionated low-to-moderate doses of radiation. Our analyses confirm previously reported significantly increased radiation risks of IHD from doses similar to those from diagnostic radiation procedures.

Dose-responses for mortality from cerebrovascular and heart diseases in atomic bomb survivors: 1950-2003

The scientific community faces important discussions on the validity of the linear no-threshold (LNT) model for radiation-associated cardiovascular diseases at low and moderate doses. In the present study, mortalities from cerebrovascular diseases (CeVD) and heart diseases from the latest data on atomic bomb survivors were analyzed. The analysis was performed with several radio-biologically motivated linear and nonlinear dose-response models. For each detrimental health outcome one set of models was identified that all fitted the data about equally well. This set was used for multi-model inference (MMI), a statistical method of superposing different models to allow risk estimates to be based on several plausible dose-response models rather than just relying on a single model of choice. MMI provides a more accurate determination of the dose response and a more comprehensive characterization of uncertainties. It was found that for CeVD, the dose-response curve from MMI is located below the linear no-threshold model at low and medium doses (0-1.4 Gy). At higher doses MMI predicts a higher risk compared to the LNT model. A sublinear dose-response was also found for heart diseases (0-3 Gy). The analyses provide no conclusive answer to the question whether there is a radiation risk below 0.75 Gy for CeVD and 2.6 Gy for heart diseases. MMI suggests that the dose-response curves for CeVD and heart diseases in the Lifespan Study are sublinear at low and moderate doses. This has relevance for radiotherapy treatment planning and for international radiation protection practices in general.

Simulation-extrapolation method to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates, 1950-2003

Analyses of the Life Span Study (LSS) of Japanese atomic bombing survivors have routinely incorporated corrections for additive classical measurement errors using regression calibration. Recently, several studies reported that the efficiency of the simulation-extrapolation method (SIMEX) is slightly more accurate than the simple regression calibration method (RCAL). In the present paper, the SIMEX and RCAL methods have been used to address errors in atomic bomb survivor dosimetry on solid cancer and leukaemia mortality risk estimates. For instance, it is shown that using the SIMEX method, the ERR/Gy is increased by an amount of about 29 % for all solid cancer deaths using a linear model compared to the RCAL method, and the corrected EAR 10(-4) person-years at 1 Gy (the linear terms) is decreased by about 8 %, while the corrected quadratic term (EAR 10(-4) person-years/Gy(2)) is increased by about 65 % for leukaemia deaths based on a linear-quadratic model. The results with SIMEX method are slightly higher than published values. The observed differences were probably due to the fact that with the RCAL method the dosimetric data were partially corrected, while all doses were considered with the SIMEX method. Therefore, one should be careful when comparing the estimated risks and it may be useful to use several correction techniques in order to obtain a range of corrected estimates, rather than to rely on a single technique. This work will enable to improve the risk estimates derived from LSS data, and help to make more reliable the development of radiation protection standards.

Cancer Mortality Among People Living in Areas With Various Levels of Natural Background Radiation

There are many places on the earth, where natural background radiation exposures are elevated significantly above about 2.5 mSv/year. The studies of health effects on populations living in such places are crucially important for understanding the impact of low doses of ionizing radiation. This article critically reviews some recent representative literature that addresses the likelihood of radiation-induced cancer and early childhood death in regions with high natural background radiation. The comparative and Bayesian analysis of the published data shows that the linear no-threshold hypothesis does not likely explain the results of these recent studies, whereas they favor the model of threshold or hormesis. Neither cancers nor early childhood deaths positively correlate with dose rates in regions with elevated natural background radiation.

Breast cancer risk and possible mechanisms of radiation-induced genomic instability in the Swedish hemangioma cohort after reanalyzed dosimetry

The cohort of 17,200 female Swedish hemangioma patients, who had been exposed to ionizing radiation because of skin hemangioma, was analyzed for breast cancer incidence with descriptive excess relative risk models and mechanistic models of carcinogenesis. The dosimetry system has recently been updated, leading to substantially reduced doses for the most highly exposed part of the Stockholm cohort. The follow-up includes persons until December 2009 with 877 breast cancer cases. All models agree on the risk estimates. The excess relative and excess absolute risk at the age of 50 years are 0.48 Gy(-1) (95% CI 0.28; 0.69) and 10.4 (10(4)PYR Gy)(-1) (95% CI 6.1; 14.4) (95% CI 6.1; 14.4), respectively. These risk estimates are about a factor of 2 higher than previous analyses of this cohort as a consequence of the re-evaluation of the dosimetry system. Explicit models incorporating effects of genomic instability were developed and applied to the hemangioma cohort. It was found that a radiation-induced transition towards genomic instability was highly significant. The models indicate that the main effect of radiation-induced genomic instability is to increase the rate of transition of non-initiated cells to initiated cells with a proliferative advantage. The magnitude of such an acceleration cannot be inferred from epidemiological data alone, but must be complemented by radiobiological measurements.

Genomic instability and radiation risk in molecular pathways to colon cancer

Colon cancer is caused by multiple genomic alterations which lead to genomic instability (GI). GI appears in molecular pathways of microsatellite instability (MSI) and chromosomal instability (CIN) with clinically observed case shares of about 15–20% and 80–85%. Radiation enhances the colon cancer risk by inducing GI, but little is known about different outcomes for MSI and CIN. Computer-based modelling can facilitate the understanding of the phenomena named above. Comprehensive biological models, which combine the two main molecular pathways to colon cancer, are fitted to incidence data of Japanese a-bomb survivors. The preferred model is selected according to statistical criteria and biological plausibility. Imprints of cell-based processes in the succession from adenoma to carcinoma are identified by the model from age dependences and secular trends of the incidence data. Model parameters show remarkable compliance with mutation rates and growth rates for adenoma, which has been reported over the last fifteen years. Model results suggest that CIN begins during fission of intestinal crypts. Chromosomal aberrations are generated at a markedly elevated rate which favors the accelerated growth of premalignant adenoma. Possibly driven by a trend of Westernization in the Japanese diet, incidence rates for the CIN pathway increased notably in subsequent birth cohorts, whereas rates pertaining to MSI remained constant. An imbalance between number of CIN and MSI cases began to emerge in the 1980s, whereas in previous decades the number of cases was almost equal. The CIN pathway exhibits a strong radio-sensitivity, probably more intensive in men. Among young birth cohorts of both sexes the excess absolute radiation risk related to CIN is larger by an order of magnitude compared to the MSI-related risk. Observance of pathway-specific risks improves the determination of the probability of causation for radiation-induced colon cancer in individual patients, if their exposure histories are known.

The risk of leukaemia in young children from exposure to tritium and carbon-14 in the discharges of German nuclear power stations and in the fallout from atmospheric nuclear weapons testing

Towards the end of 2007, the results were published from a case-control study (the "KiKK Study") of cancer in young children, diagnosed <5 years of age during 1980-2003 while resident near nuclear power stations in western Germany. The study found a tendency for cases of leukaemia to live closer to the nearest nuclear power station than their matched controls, producing an odds ratio that was raised to a statistically significant extent for residence within 5 km of a nuclear power station. The findings of the study received much publicity, but a detailed radiological risk assessment demonstrated that the radiation doses received by young children from discharges of radioactive material from the nuclear reactors were much lower than those received from natural background radiation and far too small to be responsible for the statistical association reported in the KiKK Study. This has led to speculation that conventional radiological risk assessments have grossly underestimated the risk of leukaemia in young children posed by exposure to man-made radionuclides, and particular attention has been drawn to the possible role of tritium and carbon-14 discharges in this supposedly severe underestimation of risk. Both (3)H and (14)C are generated naturally in the upper atmosphere, and substantial increases in these radionuclides in the environment occurred as a result of their production by atmospheric testing of nuclear weapons during the late 1950s and early 1960s. If the leukaemogenic effect of these radionuclides has been seriously underestimated to the degree necessary to explain the KiKK Study findings, then a pronounced increase in the worldwide incidence of leukaemia among young children should have followed the notably elevated exposure to (3)H and (14)C from nuclear weapons testing fallout. To investigate this hypothesis, the time series of incidence rates of leukaemia among young children <5 years of age at diagnosis has been examined from ten cancer registries from three continents and both hemispheres, which include registration data from the early 1960s or before. No evidence of a markedly increased risk of leukaemia in young children following the peak of above-ground nuclear weapons testing, or that incidence rates are related to level of exposure to fallout, is apparent from these registration rates, providing strong grounds for discounting the idea that the risk of leukaemia in young children from (3)H or (14)C (or any other radionuclide present in both nuclear weapons testing fallout and discharges from nuclear installations) has been grossly underestimated and that such exposure can account for the findings of the KiKK Study.

A method for determining weights for excess relative risk and excess absolute risk when applied in the calculation of lifetime risk of cancer from radiation exposure

Radiation-related risks of cancer can be transported from one population to another population at risk, for the purpose of calculating lifetime risks from radiation exposure. Transfer via excess relative risks (ERR) or excess absolute risks (EAR) or a mixture of both (i.e., from the life span study (LSS) of Japanese atomic bomb survivors) has been done in the past based on qualitative weighting. Consequently, the values of the weights applied and the method of application of the weights (i.e., as additive or geometric weighted means) have varied both between reports produced at different times by the same regulatory body and also between reports produced at similar times by different regulatory bodies. Since the gender and age patterns are often markedly different between EAR and ERR models, it is useful to have an evidence-based method for determining the relative goodness of fit of such models to the data. This paper identifies a method, using Akaike model weights, which could aid expert judgment and be applied to help to achieve consistency of approach and quantitative evidence-based results in future health risk assessments. The results of applying this method to recent LSS cancer incidence models are that the relative EAR weighting by cancer solid cancer site, on a scale of 0-1, is zero for breast and colon, 0.02 for all solid, 0.03 for lung, 0.08 for liver, 0.15 for thyroid, 0.18 for bladder and 0.93 for stomach. The EAR weighting for female breast cancer increases from 0 to 0.3, if a generally observed change in the trend between female age-specific breast cancer incidence rates and attained age, associated with menopause, is accounted for in the EAR model. Application of this method to preferred models from a study of multi-model inference from many models fitted to the LSS leukemia mortality data, results in an EAR weighting of 0. From these results it can be seen that lifetime risk transfer is most highly weighted by EAR only for stomach cancer. However, the generalization and interpretation of radiation effect estimates based on the LSS cancer data, when projected to other populations, are particularly uncertain if considerable differences exist between site-specific baseline rates in the LSS and the other populations of interest. Definitive conclusions, regarding the appropriate method for transporting cancer risks, are limited by a lack of knowledge in several areas including unknown factors and uncertainties in biological mechanisms and genetic and environmental risk factors for carcinogenesis; uncertainties in radiation dosimetry; and insufficient statistical power and/or incomplete follow-up in data from radio-epidemiological studies.