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The probability that an observed cancer was caused by radiation exposure is usually estimated using cancer rates and risk models from radioepidemiological cohorts and is called assigned share (AS). This definition implicitly assumes that an ongoing carcinogenic process is unaffected by the studied radiation exposure. However, there is strong evidence that radiation can also accelerate an existing clonal development towards cancer. In this work, we define different association measures that an observed cancer was newly induced, accelerated, or retarded. The measures were quantified exemplarily by Monte Carlo simulations that track the development of individual cells. Three biologically based two-stage clonal expansion (TSCE) models were applied. In the first model, radiation initiates cancer development, while in the other two, radiation has a promoting effect, i.e. radiation accelerates the clonal expansion of pre-cancerous cells. The parameters of the TSCE models were derived from breast cancer data from the atomic bomb survivors of Hiroshima and Nagasaki. For exposure at age 30, all three models resulted in similar estimates of AS at age 60. For the initiation model, estimates of association were nearly identical to AS. However, for the promotion models, the cancerous clonal development was frequently accelerated towards younger ages, resulting in associations substantially higher than AS. This work shows that the association between a given cancer and exposure in an affected person depends on the underlying biological mechanism and can be substantially larger than the AS derived from classic radioepidemiology.

The concept of lifetime radiation risk of stochastic detrimental health outcomes is important in contemporary radiation protection, being used either to calculate detriment-weighted effective dose or to express risks following radiation accidents or medical uses of radiation. The conventionally applied time-integrated risks of radiation exposure are computed using average values of current population and health statistical data that need to be projected far into the future. By definition, the lifetime attributable risk (AR) is an approximation to more general lifetime risk quantities and is only valid for exposures under 1 Gy. The more general quantities, such as excess lifetime risk (ELR) and risk of exposure-induced cancer, are free of dose range constraints, but rely on assumptions concerning the unknown total radiation effect on demographic and health statistical data, and are more computationally complex than AR. Consideration of highly uncertain competing risks for other radiation-attributed outcomes are required in appropriate assessments of time-integrated risks of specific outcomes following high-dose (>1 Gy) exposures, causing non-linear dose responses in the resulting ELR estimate. Being based on the current population and health statistical data, the conventionally applied time-integrated risks of radiation exposure are: (i) not well suited for projections many years into the future because of the large uncertainties in future secular trends in the population-specific disease rates; and (ii) not optimal for application to atypical groups of exposed persons not well represented by the general population. Specifically, medical patients are atypical in this respect because their prospective risks depend strongly on the original diagnosis, the treatment modality, general cure rates, individual radiation sensitivity, and genetic predisposition. Another situation challenging the application of conventional risk quantities is a projection of occupational radiation risks associated with space flight, both due to higher radiation doses and astronauts’ generally excellent health condition due to pre-selection, training, and intensive medical screening. An alternative quantity, named ‘radiation-attributed decrease of survival’ (RADS), known in past general statistical literature as ‘cumulative risk’, is recommended here for applications in space and medicine to represent the cumulative radiation risk conditional on survival until a certain age. RADS is only based on the radiation-attributed hazard rendering an insensitivity to competing risks or projections of current population statistics far into the future. Therefore, RADS is highly suitable for assessing semi-personalised radiation risks after radiation exposures from space missions or medical applications of radiation.

PURPOSE: In radiation risk analysis the state-of-the-art approach is based on descriptive models which link excess rates of cancer incidence and mortality to radiation exposure by statistical association. To estimate the number of sporadic and radiation-induced cases descriptive models apply parametric dose response function which directly determine the radiation risk. In biologically-based models of cancer risk (BBCR models) dose responses are implemented for key events on the biological level such as early mutations or clonal expansion of initiated cells. Influenced by radiation these events then shape the risk response on the epidemiological level. Although BBCR models facilitate a more comprehensive consideration of biological processes for risk assessment, their range of application in radiation research remains limited. Therefore, we emphasize their ability to improve understanding of radiation-related carcinogenesis by integrating molecular biology with epidemiology. We highlight the potential of BBCR models to harness information from adverse outcome pathways (AOPs) for risk estimation with closer links to radiobiology. The AOP concept originates from toxicology and may be applied profitably in radiation research. CONCLUSION: The conceptual design of BBCR models can be guided by the high-dimensional data environment provided by AOPs. Risk estimates from BBCR models pertain not only to classical radioepidemiological covariables such as radiation dose or attained age but also to well characterized molecular pathways. By additionally deploying biological information BBCR models facilitate finer risk stratification for a more personalized risk assessment. They leave behind the one-size-fits-all approach of descriptive modeling with the downside of more involved model development. Importantly, predictions from BBCR models can be validated against molecular measurements. Validated predictions would confirm the model design and strengthen the link between molecular biology and epidemiology. But the availability of cancer tissue in good quality from patients with known radiation exposure constitutes a major bottleneck. More ambitious initiative is needed to recover stored tissue samples and make them available for molecular investigations. To conclude, risk estimation will not only on rely on statistical association but will be quantitatively informed with radiobiological insight. Combined with the AOP framework BBCR models could improve accuracy and reduce uncertainty of radiation risk estimates in future research.

The development and application of new European software for cancer risk assessment after radiation exposure from a nuclear accident is described here. This software computes lifetime risks for several types of cancer and is intended to provide information for consideration by decision makers in the urgent and transition phases of nuclear emergencies. Such information on radiation related cancer risks can be useful to consider in the identification of protective measures (e.g., sheltering, evacuation, allocation of individuals to screening programmes and advice to take thyroid protection medication) in a differential way (i.e., for particularly susceptible sub-groups of a population). A description and application of the software for converting dose limits applicable after nuclear accidents into corresponding cancer risks, based on German population data, has already been published. Therefore, only a brief description of the main features of the software and some new illustrative results based on Swiss population data are given here, with some additional information on the input and output specification of the software.

The Adverse Outcome Pathway (AOP) framework, a systematic tool that can link available mechanistic data with phenotypic outcomes of relevance to regulatory decision-making, is being explored in areas related to radiation risk assessment. To examine the challenges including the use of AOPs to support the radiation protection community, an international horizon-style exercise was initiated through the Organisation for Economic Co-operation and Development Nuclear Energy Agency High-Level Group on Low Dose Research Radiation/Chemical AOP Joint Topical Group. The objective of the HSE was to facilitate the collection of ideas from a range of experts, to short-list a set of priority research questions that could, if answered, improve the description of the radiation dose-response relationship for low dose/dose-rate exposures, as well as reduce uncertainties in estimating the risk of developing adverse health outcomes following such exposures. The HSE was guided by an international steering committee of radiation risk experts. In the first phase, research questions were solicited on areas that can be supported by the AOP framework, or challenges on the use of AOPs in radiation risk assessment. In the second phase, questions received were refined and sorted by the SC using a best-worst scaling method. During a virtual 3-day workshop, the list of questions was further narrowed. In the third phase, an international survey of the broader radiation protection community led to an orderly ranking of the top questions. Of the 271 questions solicited, 254 were accepted and categorized into 9 themes. These were further refined to the top 25 prioritized questions. Among these, the higher ranked questions will be considered as ‘important’ to drive future initiatives in the low dose radiation protection community. These included questions on the ability of AOPs to delineate responses across different levels of biological organization, and how AOPs could be applied to address research questions on radiation quality, doses or dose-rates, exposure time patterns and deliveries, and uncertainties in low dose/dose-rate effects. A better understanding of these concepts is required to support the use of the AOP framework in radiation risk assessment. Through dissemination of these results and considerations on next steps, the JTG will address select priority questions to advance the development and use of AOPs in the radiation protection community. The major themes observed will be discussed in the context of their relevance to areas of research that support the system of radiation protection.

There is large inter-individual heterogeneity in risk of coronary heart disease (CHD). Risk factors traditionally used in primary risk assessment only partially explain this heterogeneity. Residual, unobserved heterogeneity leads to age-related attenuation of hazard rates and underestimation of hazard ratios. Its magnitude is unknown. Therefore, we aimed to estimate a lower and an approximate upper bound. Heterogeneity was parametrized by a log-normal distribution with shape parameter σ. Analysis was based on published data. From concordance indices of studies including traditional risk factors and additional diagnostic imaging data, we calculated the part of heterogeneity explained by imaging data. For traditional risk assessment, this part typically remains unexplained, thus constituting a lower bound on unobserved heterogeneity. Next, the potential impact of heterogeneity on CHD hazard rates in several large countries was investigated. CHD rates increase with age but the increase attenuates with age. Presuming this attenuation to be largely caused by heterogeneity, an approximate upper bound on σ was derived. Taking together both bounds, unobserved heterogeneity in studies without imaging information can be described by a shape parameter in the range σ = 1–2. It substantially contributes to observed age-dependences of hazard ratios and may lead to underestimation of hazard ratios by a factor of about two. Therefore, analysis of studies for primary CHD risk assessment should account for unobserved heterogeneity.

KRAS mutations of lung adenocarcinoma (LADC) are associated with smoking but little is known on other exposure-oncogene associations. Hypothesizing that different inciting agents may cause different driver mutations, we aimed to identify distinct molecular pathways to LADC, applying two entirely different approaches. First, we examined clinicopathologic features and genomic signatures of environmental exposures in the large LADC Campbell data set. Second, we designed a molecular mechanistic risk model of LADC (M3LADC) that links environmental exposure to incidence risk by mathematically emulating the disease process. This model was applied to incidence data of Japanese atom-bomb survivors which contains information on radiation and smoking exposure. Grouping the clinical data by driver mutations revealed two main distinct molecular pathways to LADC: one unique to transmembrane receptor-mutant patients that displayed robust signatures of radiation exposure and one shared between submembrane transducer-mutant patients and patients with no evident driver mutation that carried the signature of smoking. Consistently, best fit of the incidence data was achieved with a M3LADC with two pathways: in one LADC risk increased with radiation exposure and in the other with cigarette consumption. We conclude there are two main molecular pathways to LADC associated with different environmental exposures. Future molecular measurements in lung cancer tissue of atom-bomb survivors may allow to further test quantitatively the M3LADC-predicted link of radiation to transmembrane receptor mutations. Moreover, the developed molecular mechanistic model showed that for low doses, as relevant e.g. for medical imaging, smokers have the same radiation risk compared with never smokers.

International audience; A comprehensive characterization of lung adenocarcinoma (LADC) clinical features is currently missing. We prospectively evaluated Caucasian patients with early-stage LADC. Patients with LADC diagnosed between 2011 and 2015 were prospectively assessed for lung resection with curative intent. Fifty clinical, pathologic, radiologic, and molecular variables were recorded. Patients were followed till death/study conclusion. The main findings were compared to a separate cohort from France. Of 1943 patients evaluated, 366 were enrolled (18.8%; 181 female; 75 never-smokers; 28% of registered Bavarian cases over the study period). Smoking and obstruction were significantly more prevalent in GLAD compared with adult Bavarians (P < 0.0001). Ever-smoker tumors were preferentially localized to the upper lobes. We observed 120 relapses and 74 deaths over 704 cumulative follow-up years. Median overall and disease-free survival were >7.5 and 3.6 years, respectively. Patients aged 65 years, resected >60 days postdiagnosis, with abnormal FVC/DLCO VA , N2/N3 stage, or solid histology had significantly decreased survival estimates. These were fit into a weighted locoregional LADC death risk score that outperformed pTNM7 in predicting survival in the GLAD and in our second cohort. We define the clinical gestalt of locoregional LADC and provide a new clinical tool to predict survival, findings that may aid future management and research design

BackgroundSurvival after curative resection of early-stage lung adenocarcinoma (LUAD) varies and prognostic biomarkers are urgently needed.MethodsLarge-format tissue samples from a prospective cohort of 200 patients with resected LUAD were immunophenotyped for cancer hallmarks TP53, NF1, CD45, PD-1, PCNA, TUNEL and FVIII, and were followed for a median of 2.34 (95% CI 1.71–3.49) years.ResultsUnsupervised hierarchical clustering revealed two patient subgroups with similar clinicopathological features and genotype, but with markedly different survival: “proliferative” patients (60%) with elevated TP53, NF1, CD45 and PCNA expression had 50% 5-year overall survival, while “apoptotic” patients (40%) with high TUNEL had 70% 5-year survival (hazard ratio 2.23, 95% CI 1.33–3.80; p=0.0069). Cox regression and machine learning algorithms including random forests built clinically useful models: a score to predict overall survival and a formula and nomogram to predict tumour phenotype. The distinct LUAD phenotypes were validated in The Cancer Genome Atlas and KMplotter data, and showed prognostic power supplementary to International Association for the Study of Lung Cancer tumour–node–metastasis stage and World Health Organization histologic classification.ConclusionsTwo molecular subtypes of LUAD exist and their identification provides important prognostic information.

Mathematical models are able to reflect biological processes and to capture epidemiologic data. Thus, they may help elucidate roles of risk factors in disease progression. We propose to account for smoking, hypertension, and dyslipidemia in a previously published process-oriented model that describes the development of atherosclerotic lesions resulting in myocardial infarction (MI). The model is sex-specific and incorporates individual heterogeneity. It was applied to population-based individual risk factors and MI rates (Cooperative Health Research in the Region of Augsburg (KORA) study) together with subclinical atherosclerotic lesion data (Pathobiological Determinants of Atherosclerosis in Youth (PDAY) study). Different model variants were evaluated, testing the association of risk factors with different disease processes. Best fits were obtained for smoking affecting a late-stage disease process, suggesting a thrombogenic role. Hypertension was mainly related to complicated, vulnerable lesions. Dyslipidemia was consistent with increasing the number of initial lesions. By accounting for heterogeneity, individual hazard ratios differ from the population average. The mean individual hazard ratio for smoking was twice the population-based hazard ratio for men and even more for women. Atherosclerotic lesion progression and MI incidence data can be related in a mathematical model to illuminate how risk factors affect different phases of this pathological process.

Analyzing epidemiological data with simplified mathematical models of disease development provides a link between the time-course of incidence and the underlying biological processes. Here we point out that considerable modeling flexibility is gained if the model is solved by simulation only. To this aim, a model of atherosclerosis is proposed: a Markov Chain with continuous state space which represents the coronary artery intimal surface area involved with atherosclerotic lesions of increasing severity. Myocardial infarction rates are assumed to be proportional to the area of most severe lesions. The model can be fitted simultaneously to infarction incidence rates observed in the KORA registry, and to the age-dependent prevalence and extent of atherosclerotic lesions in the PDAY study. Moreover, the simulation approach allows for non-linear transition rates, and to consider at the same time randomness and inter-individual heterogeneity. Interestingly, the fit revealed significant age dependence of parameters in females around the age of menopause, qualitatively reproducing the known vascular effects of female sex hormones. For males, the incidence curve flattens for higher ages. According to the model, frailty explains this flattening only partially, and saturation of the disease process plays also an important role. This study shows the feasibility of simulating subclinical and epidemiological data with the same mathematical model. The approach is very general and may be extended to investigate the effects of risk factors or interventions. Moreover, it offers an interface to integrate quantitative individual health data as assessed, for example, by imaging. &nbsp

Risks to health are the prime consideration in all human situations of ionizing radiation exposure and therefore of relevance to radiation protection in all occupational, medical, and public exposure situations. Over the past few decades, advances in therapeutic strategies have led to significant improvements in cancer survival rates. However, a wide range of long-term complications have been reported in cancer survivors, in particular circulatory diseases and their major risk factors, metabolic diseases. However, at lower levels of exposure, the evidence is less clear. Under real-life exposure scenarios, including radiotherapy, radiation effects in the whole organism will be determined mainly by the response of normal tissues receiving relatively low doses, and will be mediated and moderated by systemic effects. Therefore, there is an urgent need for further research on the impact of low-dose radiation. In this article, we review radiation-associated risks of circulatory and metabolic diseases in clinical, occupational or environmental exposure situations, addressing epidemiological, biological, risk modelling, and systems biology aspects, highlight the gaps in knowledge and discuss future directions to address these gaps.

Purpose:Thyroid cancer of papillary histology (PTC) is the dominant type in radio-epidemiological cohorts established after nuclear accidents or warfare. Studies on post-Chernobyl PTC and on thyroid cancer in the life span study (LSS) of Japanese a-bomb survivors consistently revealed high radiation risk after exposure during childhood and adolescence. For post-Chernobyl risk assessment overexpression of the CLIP2 gene was proposed as molecular biomarker to separate radiogenic from sporadic PTC. Based on such binary marker a biologically-based risk model of PTC carcinogenesis has been developed for observational Chernobyl data. The model featured two independent molecular pathways of disease development, of which one was associated with radiation exposure. To gain credibility the concept for a mechanistic risk model must be based on general biological features which transcend findings in a single cohort. The purpose of the present study is therefore to demonstrate portability of the model concept by application to PTC incidence data in the LSS. By exploiting the molecular two-path concept we improve the determination of the probability of radiation causing cancer (POC). Materials and methods:The current analysis uses thyroid cancer incidence data of the LSS with thyroid cancer diagnoses and papillary histology (n = 292) from the follow-up period between 1958 and 2005. Risk analysis was performed with both descriptive and biologically-based models. Results:Judged by goodness-of-fit all applied models described the data almost equally well. They yielded similar risk estimates in cohorts post-Chernobyl and LSS. The preferred mechanistic model was selected by biological plausibility. It reflected important features of an imperfect radiation marker which are not easily addressed by descriptive models. Precise model predictions of marker prevalence in strata of epidemiological covariables can be tested by molecular measurements. Application of the radiation-related molecular pathway from our preferred model in retrospective risk assessment decreases the threshold dose for 50% POC from 0.33 (95% confidence interval (CI) 0.18; 0.64) Gy to 0.04 (95% CI 0.01; 0.19) Gy for females and from 0.43 (95% CI 0.17; 1.84) Gy to 0.19 (95% CI 0.05; 1.00) Gy for males. These improvements are still not sufficient to separate radiation-induced from sporadic PTC cases at very low doses Conclusions:Successful application of our preferred mechanistic model to LSS incidence data confirms and improves the biological two-path concept of radiation-induced PTC. Model predictions suggest further molecular validation studies to consolidate the basis of biologically-based risk estimation.

The CONFIDENCE dissemination workshop “Coping with uncertainties for improved modelling and decision making in nuclear emergencies” was held in December 2–5, 2019 (Bratislava, Slovak Republic). About 90 scientists and decision makers attended the workshop. The dissemination workshop allowed the presentation of the CONFIDENCE project results, demonstration of the applicability of the developed methods and tools in interactive discussion sessions and the collection of feedback from the participants. The results were disseminated not only in the form of presentations and posters but also through interactive workshops where all participants were involved in round table working groups. A fictive accidental release scenario taking place at a nuclear power plant was developed and used by each work package in the workshop to provide the basis for interactive sessions and discussions.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2) outbreak has disrupted social and economic life over large parts of the world [1]. The countermeasures designed and enforced by organisations and governments in order to contain the pandemic have had debated impact on its spread, but especially on societal structure and economic output [2]. In a fashion only precedented by war, an infectious pandemic challenges vulnerabilities of our societal structure, economic activity, and health care [3]. While the battle for the discovery of the disease's origins, pathogenesis, and cure is ongoing, a deeper understanding of its pattern of spread is sought [4]. Geographic patterns of disease burden are essential in understanding our societal and biological vulnerabilities to disease, as well as the progress of our worldwide battle against disease [5]. To this end, the way the SARS-CoV 2 pandemic has affected different countries could provide valuable clues to the nature of the disease, as well as our societal and economic weaknesses that propagate it., Geographic disease patterns of the SARS-CoV 2 and the H1N1 Influenza pandemics are cross-examined with socioeconomic indices, revealing that the two outbreaks are fundamentally different and that SARS-CoV 2 spread is linked with economic output.

Background Radiotherapy treatment of head and neck cancer affects local arteries and increases the risk of stroke. This study aimed at a closer characterization of this damage and its development in time with a longitudinal study set up. Methods Male patients treated between 2011 and 2016 for hypopharyngeal carcinoma were identified from the in-house clinical data base. They were included into the study if besides the planning CT at least one additional CT image was available from follow-up (13 patients) or at least two MRI scans (16 patients of which 2 were already included). All patients received radiotherapy, and chemotherapy was administered to 16 patients. The time from the beginning of radiotherapy to the last available image ranged from 2 months to 4.5 years. For six segments of the carotid arteries, the number and volume of atherosclerotic plaques were determined from the CT scans, and the intima media thickness from the MRI scans. Information on comorbid cardiovascular disease, hypertension and diabetes mellitus was retrieved from medical records. Results Total plaque volume rose from 0.25 cm3 before to 0.33 cm3 after therapy but this was not significant (p = 0.26). The mean number of plaques increased from 5.7 to 8.1 (p = 0.002), and the intima media thickened from 1.17 mm to 1.35 mm (p = 0.002). However, the mean intima media thickness practically did not change in patients with comorbid diabetes mellitus (p-value for homogeneity: 0.03). For patients without diabetes mellitus, dynamics of both plaque number and intima media thickness, was consistent with an increase until about one year after therapy and no further progression thereafter. Conclusion Our study confirmed the thickening of artery walls and the increase in the number of plaques. Results imply that definitive radiation damage to the artery walls can be determined not earlier than about one year after radiotherapy and there is no substantial deterioration thereafter. Reasons for the absence of an observable intima media thickening in patients with diabetes are unclear.

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. Electronic supplementary material The online version of this article (10.1007/s00411-020-00866-7) contains supplementary material, which is available to authorized users.

Exposure-lag-response associations shed light on the duration of pathogenesis for radiation-induced diseases. To investigate such relations for lung cancer mortality in the German uranium miners of the Wismut company, we apply distributed lag non-linear models (DLNMs) which offer a flexible description of the lagged risk response to protracted radon exposure. Exposure-lag functions are implemented with B-Splines in Cox models of proportional hazards. The DLNM approach yielded good agreement of exposure-lag-response surfaces for the German cohort and for the previously studied cohort of American Colorado miners. For both cohorts, a minimum lag of about 2 year for the onset of risk after first exposure explained the data well, but possibly with large uncertainty. Risk estimates from DLNMs were directly compared with estimates from both standard radio-epidemiological models and biologically based mechanistic models. For age > 45 year, all models predict decreasing estimates of the Excess Relative Risk (ERR). However, at younger age, marked differences appear as DLNMs exhibit ERR peaks, which are not detected by the other models. After comparing exposure-responses for biological processes in mechanistic risk models with exposure-responses for hazard ratios in DLNMs, we propose a typical period of 15 year for radon-related lung carcinogenesis. The period covers the onset of radiation-induced inflammation of lung tissue until cancer death. The DLNM framework provides a view on age-risk patterns supplemental to the standard radio-epidemiological approach and to biologically based modeling.

Summary This study showed a clear dose-response relationship for the CLIP2 radiation marker in post-Chernobyl papillary thyroid carcinoma cohorts for young patients and hints to different molecular mechanisms in tumors induced at low doses compared to moderate/high doses., A previous study on papillary thyroid carcinomas (PTC) in young patients who were exposed to 131iodine from the Chernobyl fallout revealed an exclusive gain of chromosomal band 7q11.23 in exposed cases compared to an age-matched control cohort. CLIP2, a gene located within band 7q11.23 was shown to be differentially expressed between exposed and non-exposed cases at messenger RNA and protein level. Therefore, a standardized procedure for CLIP2 typing of PTCs has been developed in a follow-up study. Here we used CLIP2 typing data on 117 post-Chernobyl PTCs from two cohorts of exposed patients with individual dose estimates and 24 non-exposed controls to investigate a possible quantitative dose-response relationship of the CLIP2 marker. The ‘Genrisk-T’ cohort consisted of 45 PTCs and the ‘UkrAm’ cohort of 72 PTCs. Both cohorts differed in mean dose (0.59 Gy Genrisk-T, 1.2 Gy UkrAm) and mean age at exposure (AaE) (2 years Genrisk-T, 8 years UkrAm), whilst the median latency (16 years Genrisk-T, 18 years UkrAm) was comparable. We analyzed the association between the binary CLIP2 typing and continuous thyroid dose with logistic regression. A clear positive dose-response relationship was found for young PTC cases [age at operation (AaO) < 20 years, AaE < 5 years]. In the elder age group a higher proportion of sporadic tumors is assumed due to a negligible dose response, suggesting different molecular mechanisms in sporadic and radiation-induced cases. This is further supported by the association of elder patients (AaO > 20 years) with positivity for BRAF V600E mutation.

The latest A-bomb survivor data for cardiovascular diseases are analysed to investigate whether in the first years after the bombings the baseline rates of proximal survivors were markedly different compared with those of the distal survivors. This phenomenon relates to a healthy survivor selection effect. This question is important for the decision whether to include or exclude the early years of follow-up when analysing the biological effects from acute low and high dose exposures following the nuclear weapons explosions in Hiroshima and Nagasaki. The present study shows that for cerebrovascular diseases and heart diseases the baseline rates are not significantly different in the first two decades of follow-up. Thus, for these two detrimental health outcomes, there is no need to exclude distal survivors and the first decades of follow-up time when investigating the shapes of the related dose-responses.

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. Electronic supplementary material The online version of this article (10.1007/s00411-017-0722-5) contains supplementary material, which is available to authorized users.

Biologically-based mechanistic models that are used in combining current understanding of human carcinogenesis with epidemiological studies were reviewed. Assessment was made of how well they fit the data, whether they account for non-linear radiobiological low-dose effects, and whether they suggest any implications for the dose response at low doses and dose rates. However, the present paper does not make an attempt to provide a complete review of the existing literature on biologically-based models and their application to epidemiological data.In most studies the two-stage clonal expansion (TSCE) model of carcinogenesis was used. The model provided robust estimates of identifiable parameters and radiation risk. While relatively simple, it is flexible, so that more stages can easily be added, and tests made of various types of radiation action. In general, the model performed similarly or better than descriptive excess absolute and excess relative risk models, in terms of quality of fit and number of parameters. Only very rarely the shape of dose-response predicted by the models was investigated. For some tumors, when more detailed biological information was known, additional pathways were included in the model. The future development of these models will benefit from growing knowledge on carcinogenesis processes, and in particular from use of biobank tissue samples and advances in omics technologies. Their use appears a promising approach to investigate the radiation risk at low doses and low dose rates. However, the uncertainties involved are still considerable, and the models provide only a simplified description of the underlying complexity of carcinogenesis. Current assumptions in radiation protection including the linear-non-threshold (LNT) model are not in contradiction to what is presently known on the process of cancer development.

A substantial increase in papillary thyroid carcinoma (PTC) among children exposed to the radioiodine fallout has been one of the main consequences of the Chernobyl reactor accident. Recently, the investigation of PTCs from a cohort of young patients exposed to the post-Chernobyl radioiodine fallout at very young age and a matched nonexposed control group revealed a radiation-specific DNA copy number gain on chromosomal band 7q11.23 and the radiation-associated mRNA overexpression of CLIP2. In this study, we investigated the potential role of CLIP2 as a radiation marker to be used for the individual classification of PTCs into CLIP2-positive and -negative cases-a prerequisite for the integration of CLIP2 into epidemiological modelling of the risk of radiation-induced PTC. We were able to validate the radiation-associated CLIP2 overexpression at the protein level by immunohistochemistry (IHC) followed by relative quantification using digital image analysis software (P=0.0149). Furthermore, we developed a standardized workflow for the determination of CLIP2-positive and -negative cases that combines visual CLIP2 IHC scoring and CLIP2 genomic copy number status. In addition to the discovery cohort (n=33), two independent validation cohorts of PTCs (n=115) were investigated. High sensitivity and specificity rates for all three investigated cohorts were obtained, demonstrating robustness of the developed workflow. To analyse the function of CLIP2 in radiation-associated PTC, the CLIP2 gene regulatory network was reconstructed using global mRNA expression data from PTC patient samples. The genes comprising the first neighbourhood of CLIP2 (BAG2, CHST3, KIF3C, NEURL1, PPIL3 and RGS4) suggest the involvement of CLIP2 in the fundamental carcinogenic processes including apoptosis, mitogen-activated protein kinase signalling and genomic instability. In our study, we successfully developed and independently validated a workflow for the typing of PTC clinical samples into CLIP2-positive and CLIP2-negative and provided first insights into the CLIP2 interactome in the context of radiation-associated PTC.

Thyroid cancer is one of the major health concerns after the accident in the Fukushima Dai-ichi nuclear power station (NPS). Currently, ultrasonography surveys are being performed for persons residing in the Fukushima Prefecture at the time of the accident with an age of up to 18 years. Here, the expected thyroid cancer prevalence in the Fukushima Prefecture is assessed based on an ultrasonography survey of Ukrainians, who were exposed at an age of up to 18 years to 131I released during the Chernobyl NPS accident, and on differences in equipment and study protocol in the two surveys. Radiation risk of thyroid cancer incidence among survivors of the atomic bombings of Hiroshima and Nagasaki and preliminary estimates of thyroid dose due to the Fukushima accident were used for the prediction of baseline and radiation-related thyroid cancer risks. We estimate a prevalence of thyroid cancer of 0.027 % (95 % CI 0.010 %; 0.050 %) for the first screening campaign in the Fukushima Prefecture. Compared with the incidence rate in Japan in 2007, the ultrasonography survey is predicted to increase baseline thyroid cancer incidence by a factor of 7.4 (95 % CI 0.95; 17.3). Under the condition of continued screening, thyroid cancer during the first fifty years after the accident is predicted to be detected for about 2 % of the screened population. The prediction of radiation-related thyroid cancer in the most exposed fraction (a few ten thousand persons) of the screened population of the Fukushima Prefecture has a large uncertainty with the best estimates of the average risk of 0.1–0.3 %, depending on average dose. Electronic supplementary material The online version of this article (doi:10.1007/s00411-013-0508-3) contains supplementary material, which is available to authorized users.

The article Dose-responses for mortality from cerebrovascular and heart diseases in atomic bomb survivors: 1950-2003, written by Helmut Schollnberger.

After an explosion of a radiological dispersal device, decision-makers need to implement countermeasures as soon as possible to minimise the radiation-induced risks to the population. In this work, the authors present a tool, which can help providing information about the approximate size of source term and radioactive contamination based on a Gaussian Plume model with the use of available measurements for liquid or aerosolised radioactivity. For two-field tests, the source term and spatial distribution of deposited radioactivity are estimated. A sensitivity analysis of the dependence on deposition velocity is carried out. In case of weak winds, a diffusive process along the wind direction is retained in the model.

After an accidental radioactive contamination by aerosols in inhabited areas, the radiation exposure to man is determined by complex interactions between different factors such as dry or wet deposition, different types of ground surfaces, chemical properties of the radionuclides involved and building development as well as dependence on bomb construction e.g. design and geometry. At short-term, the first rainfall is an important way of natural decontamination: deposited radionuclides are washed off from surfaces and in urban areas the resulting contaminated runoff enters the sewer system and is collected in a sewage plant. Up to now the potential exposure caused by this process has received little attention and is estimated here with simulation models. The commercial rainfall-runoff model for urban sewer systems KANAL++ has been extended to include transport of radionuclides from surfaces through the drainage to various discharge facilities. The flow from surfaces is modeled by unit hydrographs, which produce boundary conditions for a system of 1d coupled flow and transport equations in a tube system. Initial conditions are provided by a map of surface contamination which is produced by geo-statistical interpolation of γ-dose rate measurements taking into account the detector environment. The corresponding methodology is implemented in the Inhabited Area Monitoring Module (IAMM) software module as part of the European decision system JRODOS. A hypothetical scenario is considered where a Radiation Dispersal Device (RDD) with Cs-137 is detonated in a small inhabited area whose drainage system is realistically modeled. The transition of deposited radionuclides due to rainfall into the surface runoff is accounted for by different nuclide-specific entrainment coefficients for paved and unpaved surfaces. The concentration of Cs-137 in water is calculated at the nodes of the drainage system and at the sewage treatment plant. The external exposure to staff of the treatment plant is estimated. For Cs-137 radiation levels in the plant are low since wash-off of cesium from surfaces is an ineffective process.

We propose a stochastic model for use in epidemiological analysis, describing the age-dependent development of atherosclerosis with adequate simplification. The model features the uptake of monocytes into the arterial wall, their proliferation and transition into foam cells. The number of foam cells is assumed to determine the health risk for clinically relevant events such as stroke. In a simulation study, the model was checked against the age-dependent prevalence of atherosclerotic lesions. Next, the model was applied to incidence of atherosclerotic stroke in the cohort of male workers from the Mayak nuclear facility in the Southern Urals. It describes the data as well as standard epidemiological models. Based on goodness-of-fit criteria the risk factors smoking, hypertension and radiation exposure were tested for their effect on disease development. Hypertension was identified to affect disease progression mainly in the late stage of atherosclerosis. Fitting mechanistic models to incidence data allows to integrate biological evidence on disease progression into epidemiological studies. The mechanistic approach adds to an understanding of pathogenic processes, whereas standard epidemiological methods mainly explore the statistical association between risk factors and disease outcome. Due to a more comprehensive scientific foundation, risk estimates from mechanistic models can be deemed more reliable. To the best of our knowledge, such models are applied to epidemiological data on cardiovascular diseases for the first time.

Networks of monitoring stations have been set up in many European countries to detect the passage of a radioactive cloud in the event of a large-scale nuclear emergency. The layout and spatial density of these networks differs according to the needs and criteria defined by national authorities. Germany and the Netherlands decided to set up relatively dense networks for the detection of weak radioactive plumes and, additionally, environmental radioactivity from deposited aerosols. Plausible evaluation criteria are presented here to assess important properties which determine the reliability and efficiency of sections of these networks. As a test case the existing sampling design of the Dutch and German networks with 193 sensors in an area of 200 km around the nuclear power plant near the city of Lingen (Emsland) in the German federal state of Lower Saxony has been selected. For a hypothetical accident scenario 292 radioactive plumes have been simulated which are shaped by recorded weather conditions of the year 2007. To quantify the network performance frequency distributions of the proposed evaluation parameters have been analyzed. Simulation results show that 95% of the plumes are detected within 4 h after the release. Maximal values of the γ-dose rate 1 m above the ground mostly occur near the source within a radius of 5 km. However, under certain weather conditions maximal ground values may also be found more than 50 km away from the source. Within a circle of radius 90 km 98% of the recorded maximal γ-dose rates of the plumes were found by 62% of the 193 sensors. But only in a joint network of German and Dutch sensors all simulated plumes triggered an alarm. This result encourages efforts of close international collaboration, e.g. between EU member states, in network design and operation. Test series which involved the removal of sensors either randomly or in a controlled way showed that the network configuration is fit for the intended purpose of detecting a large majority of plumes. But already a small reduction in the number of sensors would degrade the performance. Whereas the joint network triggers alarms reliably, it fails to detect the true plume maxima. The assessment criteria can be used for a revision of existing networks or for planning purposes in countries such as those applying for EU membership.

A recent analysis of leukaemia mortality in Japanese A-bomb survivors has applied descriptive models, collected together from previous studies, to derive a joint excess relative risk estimate (ERR) by multi-model inference (MMI) (Walsh and Kaiser in Radiat Environ Biophys 50:21–35, 2011). The models use a linear-quadratic dose response with differing dose effect modifiers. In the present study, a set of more than 40 models has been submitted to a rigorous statistical selection procedure which fosters the parsimonious deployment of model parameters based on pairwise likelihood ratio tests. Nested models were consequently excluded from risk assessment. The set comprises models of the excess absolute risk (EAR) and two types of non-standard ERR models with sigmoidal responses or two line spline functions with a changing slope at a break point. Due to clearly higher values of the Akaike Information Criterion, none of the EAR models has been selected, but two non-standard ERR models qualified for MMI. The preferred ERR model applies a purely quadratic dose response which is slightly damped by an exponential factor at high doses and modified by a power function for attained age. Compared to the previous analysis, the present study reports similar point estimates and confidence intervals (CI) of the ERR from MMI for doses between 0.5 and 2.5 Sv. However, at lower doses, the point estimates are markedly reduced by factors between two and five, although the reduction was not statistically significant. The 2.5 % percentiles of the ERR from the preferred quadratic-exponential model did not fall below zero risk in exposure scenarios for children, adolescents and adults at very low doses down to 10 mSv. Yet, MMI produced risk estimates with a positive 2.5 % percentile only above doses of some 300 mSv. Compared to CI from a single model of choice, CI from MMI are broadened in cohort strata with low statistical power by a combination of risk extrapolations from several models. Reverting to MMI can relieve the dilemma of needing to choose between models with largely different consequences for risk assessment in public health. Electronic supplementary material The online version of this article (doi:10.1007/s00411-012-0437-6) contains supplementary material, which is available to authorized users.

A conceptual design is presented to use measurements of localized absorbed dose in inner cities for production of high resolution maps of the radioactive contamination following a nuclear emergency or radiological attack. The doses are derived from luminescent detectors pre-fixed at places of high importance (e.g. public squares). For such an environmental dosimeter, BeO is used, which can be read out using optically stimulated luminescence (OSL). A suitable casing of black Perspex has been developed to give a sufficiently accurate estimate of the air kerma value at the detector position. The dosimeter is characterized according to light tightness, dose response and angular photon energy dependence. A short overview of the approach for map production is also given.

We would like to thank Drs. Richardson and Cole (Richardson and Cole 2011) for taking an interest in a recent paper on multi-model inference (MMI) based on the Japanese A-bomb data for leukaemia mortality from members of our group of research collaborators (Walsh and Kaiser 2011). We also appreciate the time that they took to consider our methodology. This methodology has been successfully applied in many other fields of research— physics, biology, environmental science, etc.,—where one can find a large number of papers (e.g. Liddle et al. 2006; Zhang and Townsend 2009; Lavoue and Droz 2009). However, we consider the application to be new in the field of radiation epidemiology and are particularly interested in refining and improving our initial approach. We prefer to use the MMI terminology rather than ‘‘model averaging’’ since model averaging implies that the related uncertainties from model combinations are reduced—in reality with MMI, the uncertainties are increased to account for uncertainties between several models that describe the data almost equally well. Richardson and Cole correctly state in their comments that ‘‘model averaging is one approach to characterizing uncertainty in risk estimates in epidemiological studies in which there is low statistical power to discriminate between alternative model forms.’’ Therefore, we are very surprised and concerned that they caution against our approach in general and state that both of our recommendations concerning the choice of models for risk assessment should be viewed cautiously. Their arguments are based on two examples: one using two models from the appendix of Richardson et al. (2009) and another offering a simply constructed hypothetical numerical illustration of how our approach can lead to biased results. We will consider these two points below and also a third point that illustrates how our initial approach can be improved.

Locations of gamma dose rate sensors have often been chosen by administrative or geometrical criteria. Nowadays computational capacity allows for a more realistic basis. We use simulations of potential radioactive plumes based on weather data of one year to investigate the threats to regions without own nuclear power plants and to find good numbers and locations of sensors to detect such plumes. We optimise sensor locations by minimising a cost function that can take into account numbers of undetected plumes, their dose to the region in general, or on the population. Besides we assess the effect of administrative constraints, be it that sensors have to cover administrative units, or that optimisation is done for sub-regions separately. Finally we evaluate the robustness of the approach if less or other plumes are used. The main findings are that sensors at boundaries are often best, but also typical paths of plumes may be important, and that administrative constraints may necessitate much more sensors. The small numbers of sensors actually deployed in these regions seem sufficient. However, the latter may be an artefact of the low number of plumes we considered. Altogether, combined with other considerations, this approach can contribute to better decisions about gamma dose rate sensor locations.

The ERMIN model is a new implement developed to enable estimation of the radiological consequences in inhabited areas of accidents in nuclear installations. Similarly, AGRICP is a model developed to enable estimation of the radiological consequences of contamination of agricultural production areas. This paper provides a short overview of the background of the two models and describes the features enabled through their implementation in the ARGOS decision support system. The integration allows calculation of both dose rates and doses in particular areas, and can be used to evaluate the effectiveness and costs of countermeasure strategies.

The Urban Remediation Working Group of the International Atomic Energy Agency's EMRAS (Environmental Modelling for Radiation Safety) program was organized to address issues of remediation assessment modelling for urban areas contaminated with dispersed radionuclides. The present paper describes the second of two modelling exercises. This exercise was based on a hypothetical dispersal of radioactivity in an urban area from a radiological dispersal device, with reference surface contamination at selected sites used as the primary input information. Modelling endpoints for the exercise included radionuclide concentrations and external dose rates at specified locations, contributions to the dose rates from individual surfaces, and annual and cumulative external doses to specified reference individuals. Model predictions were performed for a "no action" situation (with no remedial measures) and for selected countermeasures. The exercise provided an opportunity for comparison of three modelling approaches, as well as a comparison of the predicted effectiveness of various countermeasures in terms of their short-term and long-term effects on predicted doses to humans.

The European Model for Inhabited Areas (ERMIN) was developed to allow a user to explore different recovery options following the contamination of an urban environment with radioactive material and to refine an appropriate strategy for the whole region affected. The input data include a description of the environment, initial deposition of radionuclides on to a reference surface and a description of countermeasures. Output information includes the average doses to members of the public from external exposure to gamma and beta radiation from deposited radionuclides and inhalation of resuspended radioactivity, the contamination on urban surfaces, the activity concentration in air from resuspension, the doses to workers undertaking the recovery work, the quantity and activity of waste generated and the cost and work required to implement the countermeasure. ERMIN has been designed to be implemented as a tool that supports the approach of decision-makers and allows the area to be broken down into smaller regions where different conditions prevail and different countermeasure packages are enacted.

This article gives an overview on the conceptual design of the Inhabited Areas Monitoring Module IAMM which will be introduced into European decision support systems for nuclear emergencies. It will improve the use of monitoring data of radioactive contamination in urban environments for decision making. IAMM converts the dated gamma dose rate (GDR) measurements from geo-referenced locations into maps of surface contamination with an enhanced spatial resolution. Depending on the availability of the monitoring data, IAMM relies on two modes of operation. If there are only a few measurements, these are taken to improve the maps from a deposition model using data assimilation. If the number of measurements is sufficient to apply spatial interpolation IAMM will rely entirely on monitoring data. Suitable geo-referenced data points will be interpreted by IAMM with respect to their detector environment using the concept of location factors. The endpoints of IAMM can be used directly for decision making or dose calculations with either simple dose models or the more refined EuRopean Model for INhabited areas (ERMIN).