Human-mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human-mouse difference in radiation-related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age-related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per-lifespan basis (0.72- and 0.71-fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species-independent radiation-induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation-associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Thresholds for carcinogens

Current regulatory cancer risk assessment principles and practices assume a linear dose-response relationship-the linear no-threshold (LNT) model-that theoretically estimates cancer risks occurring following low doses of carcinogens by linearly extrapolating downward from experimentally determined risks at high doses. The two-year rodent bioassays serve as experimental vehicles to determine the high-dose cancer risks in animals and then to predict, by extrapolation, the number of carcinogen-induced tumors (tumor incidence) that will arise during the lifespans of humans who are exposed to environmental carcinogens at doses typically orders of magnitude below those applied in the rodent assays. An integrated toxicological analysis is conducted herein to reconsider an alternative and once-promising approach, tumor latency, for estimating carcinogen-induced cancer risks at low doses. Tumor latency measures time-to-tumor following exposure to a carcinogen, instead of tumor incidence. Evidence for and against the concept of carcinogen-induced tumor latency is presented, discussed, and then examined with respect to its relationship to dose, dose rates, and the dose-related concepts of initiation, tumor promotion, tumor regression, tumor incidence, and hormesis. Considerable experimental evidence indicates: (1) tumor latency (time-to-tumor) is inversely related to the dose of carcinogens and (2) lower doses of carcinogens display quantifiably discrete latency thresholds below which the promotion and, consequently, the progression and growth of tumors are delayed or prevented during a normal lifespan. Besides reconciling well with the concept of tumor promotion, such latency thresholds also reconcile favorably with the existence of thresholds for tumor incidence, the stochastic processes of tumor initiation, and the compensatory repair mechanisms of hormesis. Most importantly, this analysis and the arguments presented herein provide sound theoretical, experimental, and mechanistic rationales for rethinking the foundational premises of low-dose linearity and updating the current practices of cancer risk assessment to include the concept of carcinogen thresholds.

A mechanistic model for atherosclerosis and its application to the cohort of Mayak workers

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.

Minimizing second cancer risk following radiotherapy: current perspectives

Secondary cancer risk following radiotherapy is an increasingly important topic in clinical oncology with impact on treatment decision making and on patient management. Much of the evidence that underlies our understanding of secondary cancer risks and our risk estimates are derived from large epidemiologic studies and predictive models of earlier decades with large uncertainties. The modern era is characterized by more conformal radiotherapy technologies, molecular and genetic marker approaches, genome-wide studies and risk stratifications, and sophisticated biologically based predictive models of the carcinogenesis process. Four key areas that have strong evidence toward affecting secondary cancer risks are 1) the patient age at time of radiation treatment, 2) genetic risk factors, 3) the organ and tissue site receiving radiation, and 4) the dose and volume of tissue being irradiated by a particular radiation technology. This review attempts to summarize our current understanding on the impact on secondary cancer risks for each of these known risk factors. We review the recent advances in genetic studies and carcinogenesis models that are providing insight into the biologic processes that occur from tissue irradiation to the development of a secondary malignancy. Finally, we discuss current approaches toward minimizing the risk of radiation-associated secondary malignancies, an important goal of clinical radiation oncology.

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.

Low-Dose Radiation Risks of Computerized Tomography and Cone Beam Computerized Tomography: Reducing the Fear and Controversy

Regulations for protecting humans against stochastic biological effects from ionizing radiation are based on the linear no-threshold (LNT) risk assessment model, which states that any amount of radiation exposure may lead to cancer in a population. Based on the LNT model, risk from low-dose radiation increases linearly with increasing doses of radiation. Imaging procedures in medicine and dentistry are an important source of low-dose ionizing radiation. The increased use of computerized tomography (CT) and cone beam computerized tomography (CBCT) has raised health concerns regarding exposure to low-dose ionizing radiation. In oral and maxillofacial surgery and implant dentistry, CBCT is now at the forefront of this controversy. Although caution has been expressed, there have been no direct studies linking radiation exposure from CT and CBCT used in dental imaging with cancer induction. This article describes the concerns about radiation exposure in dental imaging regarding the use of CT.

Role of melatonin on electromagnetic radiation-induced oxidative stress and Ca2+ signaling molecular pathways in breast cancer

AIMS

Exposure to electromagnetic radiation (EMR) may increase breast cancer risk by inducing oxidative stress and suppressing the production of melatonin. Aim of the present review is to discuss the mechanisms and risk factors of EMR and oxidative stress-induced breast cancer, to summarize the controlled studies evaluating measures for prevention, and to conclude with evidence-based strategies for prevention.

MATERIALS

Review of the relevant literature and results from our recent basic studies, as well as critical analyses of published systematic reviews were obtained from the Pubmed and the Science Citation Index.

RESULTS

It has been proposed that chronic exposure to EMR may increase the risk of breast cancer by suppressing the production of melatonin; this suppression may affect the development of breast cancer either by increasing levels of circulation of estrogen or through over production of free oxygen radicals. Most epidemiological studies have also indicated overall effect of EMR exposure in premenopausal women, particularly for estrogen receptor positive breast tumors. Enhanced voltage-dependent Ca(2+) current and impaired inhibitory G-protein function, and derangement of intracellular organelles with a Ca(2+) buffering effect, such as endoplasmic reticulum and mitochondria have been also shown to contribute to disturbed Ca(2+) signaling in breast cancer.

CONCLUSION

Melatonin may modulate breast cancer through modulation of enhanced oxidative stress and Ca(2+) influx in cell lines. However, there is not enough evidence on increased risk of breast cancer related to EMR exposure.

Breast cancer risk in atomic bomb survivors from multi-model inference with incidence data 1958-1998

Breast cancer risk from radiation exposure has been analyzed in the cohort of Japanese a-bomb survivors using empirical models and mechanistic two-step clonal expansion (TSCE) models with incidence data from 1958 to 1998. TSCE models rely on a phenomenological representation of cell transition processes on the path to cancer. They describe the data as good as empirical models and this fact has been exploited for risk assessment. Adequate models of both types have been selected with a statistical protocol based on parsimonious parameter deployment and their risk estimates have been combined using multi-model inference techniques. TSCE models relate the radiation risk to cell processes which are controlled by age-increasing rates of initiating mutations and by changes in hormone levels due to menopause. For exposure at young age, they predict an enhanced excess relative risk (ERR) whereas the preferred empirical model shows no dependence on age at exposure. At attained age 70, the multi-model median of the ERR at 1 Gy decreases moderately from 1.2 Gy(-1) (90% CI 0.72; 2.1) for exposure at age 25 to a 30% lower value for exposure at age 55. For cohort strata with few cases, where model predictions diverge, uncertainty intervals from multi-model inference are enhanced by up to a factor of 1.6 compared to the preferred empirical model. Multi-model inference provides a joint risk estimate from several plausible models rather than relying on a single model of choice. It produces more reliable point estimates and improves the characterization of uncertainties. The method is recommended for risk assessment in practical radiation protection.

Toward improved ionizing radiation safety standards

Ionizing radiation safety standards developed by the International Commission on Radiological Protection (ICRP) during the past 50-plus years have provided guidance for effective protection of workers and the public from the potentially harmful effects of exposure to ionizing radiation, including cancer. Earlier standards were based primarily on radiation dose rate to organs of the body. More recent recommendations have calculated cancer risk as a function of cumulative dose using a linear no-threshold cancer risk model based on the acute high dose rate exposures received by the Japanese atomic bomb survivors. The underlying assumption in these current recommendations is that risk of radiation-induced cancer is proportional to cumulative dose without threshold. In conflict with this position are the studies of protracted exposures from internally-deposited radionuclides in people and laboratory animals that have demonstrated that cancer induction risk is a function of average dose rate for protracted exposures to ionizing radiation. At lower average dose rates, cancer latency can exceed natural lifespan leading to a virtual threshold. This forum statement proposes that the conflict of these two cancer risk models is explained by the fact that the increased risk of cancer observed in the atomic bomb survivor studies was primarily the result of acute high dose rate promotion of ongoing biological processes that lead to cancer rather than cancer induction. In addition, ionizing radiation-induced cancer is not the result of a simple stochastic event in a single living cell but rather a complex deterministic systemic effect in living tissues. It is recommended that the ICRP consider revising its position in light of this important distinction between cancer promotion and cancer induction.

A new view of radiation-induced cancer

Biologically motivated mathematical models are important for understanding the mechanisms of radiation-induced carcinogenesis. Existing models fall into two categories: (1) short-term formalisms, which focus on the processes taking place during and shortly after irradiation (effects of dose, radiation quality, dose rate and fractionation), and (2) long-term formalisms, which track background cancer risks throughout the entire lifetime (effects of age at exposure and time since exposure) but make relatively simplistic assumptions about radiation effects. Grafting long-term mechanisms on to short-term models is badly needed for modelling radiogenic cancer. A combined formalism was developed and applied to cancer risk data in atomic bomb survivors and radiotherapy patients and to background cancer incidence. The data for nine cancer types were described adequately with a set of biologically meaningful parameters for each cancer. These results suggest that the combined short-long-term approach is a potentially promising method for predicting radiogenic cancer risks and interpreting the underlying biological mechanisms.

Cancer risks after radiation exposure in middle age

BACKGROUND

Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages.

METHODS

We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure.

RESULTS

Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site-dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not.

CONCLUSION

For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis become increasingly important as the age at exposure increases. Radiation-induced cancer risks after exposure in middle age may be up to twice as high as previously estimated, which could have implications for occupational exposure and radiological imaging.

Attributable risk for radiation in the presence of other risk factors

Two motivations for studying radiation risk are (1) to quantify the magnitude of risk as an aid to setting radiation protection standards and (2) to understand causality as an aid to assigning compensation for radiation exposed individuals whose disease or death may have been related to radiation exposure. Although it has long been known that radiation risk is modified by factors such as sex, age, and time, it is now apparent that radiation risk may also be modified by other risk factors, such as smoking, inflammation, genotype, and certain pathogens. Even apart from considerations of etiological interaction, the relative contribution of radiation to total burden of disease or death may depend on the level of background (spontaneous) risk of disease or death owing to those other factors if the joint effects do not multiply. Therefore, ignoring those other factors in assessing probability of causation for radiation (attributable fraction in epidemiological data) involves making a strong assumption about the joint effects. The concepts are discussed in detail and illustrated using results from studies on the Japanese atomic-bomb survivors.

The balance between initiation and promotion in radiation-induced murine carcinogenesis

Studies of radiation carcinogenesis in animals allow detailed investigation of how the risk depends on age at exposure and time since exposure and of the mechanisms that determine this risk, e.g., induction of new pre-malignant cells (initiation) and enhanced proliferation of already existing pre-malignant cells (promotion). To assist the interpretation of these patterns, we apply a newly developed biologically based mathematical model to data on several types of solid tumors induced by acute whole-body radiation in mice. The model includes both initiation and promotion and analyzes pre-malignant cell dynamics on two different time scales: comparatively short-term during irradiation and long-term during the entire life span. Our results suggest general mechanistic similarities between radiation carcinogenesis in mice and in human atomic bomb survivors. The excess relative risk (ERR) in mice decreases with age at exposure up to an exposure age of 1 year, which corresponds to mid-adulthood in humans; the pattern for older ages at exposure, for which there is some evidence of increasing ERRs in atomic bomb survivors, cannot be evaluated using the data set analyzed here. Also similar to findings in humans, initiation dominates the ERR at young ages in mice, when there are few background pre-malignant cells, and promotion becomes important at older ages.

Modeling deterministic effects in hematopoietic system caused by chronic exposure to ionizing radiation in large human cohorts

A new model of the hematopoietic system for humans chronically exposed to ionizing radiation allows for quantitative description of the initial hematopoiesis inhibition and subsequent increase in the risks of late stochastic effects such as leukemia. This model describes the dynamics of the hematopoietic stem cell compartment as well as the dynamics of each of the three blood cell types (leukocytes, erythrocytes, and platelets). The model parameters are estimated from the results of other experiments. They include the steady-state numbers of hematopoietic stem cells and peripheral blood cell lines for an unexposed organism, amplification parameters for each blood cell line, parameters describing the proliferation and apoptosis, parameters of feedback functions regulating the steady-state numbers, and characteristics of radiosensitivity in respect to cell death and non-lethal cell damages. The dynamic model of hematopoiesis is applied to the data on a subcohort of the Techa River residents with hematological measurements (e.g., blood counts) performed in 1950-1956 (which totals to about 3,500 exposed individuals). Among well-described effects observed in these data are the slope values of the dose-effect curves describing the hematopoietic inhibition and the dose rate patterns of the fractions of cytopenic states (e.g., leukopenia, thrombocytopenia). The model has been further generalized by inclusion of the component describing the risk of late stochastic effects. The risks of the development of late effects (such as leukemia) in population groups with specific patterns of early reactions in hematopoiesis (such as leukopenia induced by ionizing radiation) are investigated using simulation studies and compared to data.

Modeling progression in radiation-induced lung adenocarcinomas

Quantitative multistage carcinogenesis models are used in radiobiology to estimate cancer risks and latency periods (time from exposure to clinical cancer). Steps such as initiation, promotion and transformation have been modeled in detail. However, progression, a later step during which malignant cells can develop into clinical symptomatic cancer, has often been approximated simply as a fixed lag time. This approach discounts important stochastic mechanisms in progression and evidence on the high prevalence of dormant tumors. Modeling progression more accurately is therefore important for risk assessment. Unlike models of earlier steps, progression models can readily utilize not only experimental and epidemiological data but also clinical data such as the results of modern screening and imaging. Here, a stochastic progression model is presented. We describe, with minimal parameterization: the initial growth or extinction of a malignant clone after formation of a malignant cell; the likely dormancy caused, for example, by nutrient and oxygen deprivation; and possible escape from dormancy resulting in a clinical cancer. It is shown, using cohort simulations with parameters appropriate for lung adenocarcinomas, that incorporating such processes can dramatically lengthen predicted latency periods. Such long latency periods together with data on timing of radiation-induced cancers suggest that radiation may influence progression itself.

Use of the individual data of the A-bomb survivors for biologically based cancer models

All recent analyses of the data on solid cancer incidence of the atomic bomb survivors are corrected for migration and random dose errors. In the usual treatment with grouped data and regression calibration, the calibration of doses depends on the used dose response. For solid cancers, it usually is linear. Here, an individual likelihood is presented which works without further adjustment for all dose responses. When the same assumptions are made as in the usual Poisson regression, equivalent results are obtained. But, the individual likelihood has the potential to use more detailed models for dose errors and to estimate non-linear dose responses without recalibration. As an example for the potential of the individual data set for the analysis of risk at low doses, signals for a saturating bystander effect are investigated.

Modeling excess lung cancer risk among screened arm participants in the Mayo Lung Project

BACKGROUND

The Mayo Lung Project (MLP) was a randomized clinical trial designed to test whether periodic screening by chest x-ray reduced lung cancer (LC) mortality in men who were high-risk smokers. Among MLP participants, there were more deaths from LC in the screening arm both at the trial's end and after long-term follow-up. Overdiagnosis was cited widely as an explanation for the MLP results, whereas a role for excess LC risk attributable to undergoing numerous chest x-ray screenings largely was unexamined. The authors of this report examined the consistency of the MLP data with a modified 2-stage clonal expansion (TSCE) model of excess LC risk.

METHODS

By using a simulation model calibrated to the initial MLP data, the authors examined the expected statistical variance of LC incidence and mortality between the screening and control arms. A Bayesian estimation framework using a modified version of the TSCE model to evaluate the role of excess LC risk attributable to chest x-ray screening was derived and applied to the MLP data.

RESULTS

Simulation experiments indicated that the overall difference in LC deaths and incidence between the study arm and the control arm was unlikely (P = .0424 and P = .0104, respectively) assuming no excess risk of LC. The authors estimated that the 10-year excess LC risk for a man aged 60 years who smoked and who received 10 chest x-ray screenings was 0.574% (P = .0021).

CONCLUSIONS

The excess LC risk observed among screening arm participants was found to be statistically significant with respect to the TSCE model framework in part because of the incorporation of key risk correlates of age and screen frequency into the estimation framework.

A new view of radiation-induced cancer: integrating short- and long-term processes. Part II: second cancer risk estimation

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

A new view of radiation-induced cancer: integrating short- and long-term processes. Part I: approach

Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track pre-malignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrate a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age.

Predicting future excess events in risk assessment

Risk characterization in a study population relies on cases of disease or death that are causally related to the exposure under study. The number of such cases, so-called "excess" cases, is not just an indicator of the impact of the risk factor in the study population, but also an important determinant of statistical power for assessing aspects of risk such as age-time trends and susceptible subgroups. In determining how large a population to study and/or how long to follow a study population to accumulate sufficient excess cases, it is necessary to predict future risk. In this study, focusing on models involving excess risk with possible effect modification, we describe a method for predicting the expected magnitude of numbers of excess cases and assess the uncertainty in those predictions. We do this by extending Bayesian APC models for rate projection to include exposure-related excess risk with possible effect modification by, e.g., age at exposure and attained age. The method is illustrated using the follow-up study of Japanese Atomic-Bomb Survivors, one of the primary bases for determining long-term health effects of radiation exposure and assessment of risk for radiation protection purposes. Using models selected by a predictive-performance measure obtained on test data reserved for cross-validation, we project excess counts due to radiation exposure and lifetime risk measures (risk of exposure-induced deaths (REID) and loss of life expectancy (LLE)) associated with cancer and noncancer disease deaths in the A-Bomb survivor cohort.

Canadian National Dose Registry of radiation workers: overview of research from 1951 through 2007

The National Dose Registry (NDR) of Canada is a unique resource for a direct estimation of the potential health risks associated with low doses of ionizing radiation. This is the largest national occupational radiation exposure database, comprising records for about 600,000 nuclear, industrial, medical and dental workers. An analysis of the NDR data based on a cohort of about 200,000 workers first exposed before 1984 and followed through 1987 and 1988 for mortality and cancer incidence, respectively, revealed that the mortality from most causes of death considered was lower than that in the general population, which is typical of occupational cohorts. Although the same was also observed for cancer incidence, there was a significant increase in the incidence of thyroid cancer and melanoma which, however, was not clearly related to radiation exposure. A significant dose-response was found for mortality from all causes, all cancers, lung cancer, cardiovascular diseases, accidents, for incidence of all cancers, cancers of the rectum and lung, leukaemia, all cancers except lung, and all cancers except leukaemia. In addition, in male workers, a significant dose-response was found for the incidence of colon, pancreatic, and testicular cancers. The estimates of cancer risks (mortality and incidence) were higher than those in most other occupational cohorts and in the studies on atomic bomb survivors. The biologically based dose-response models used to describe lung cancer incidence in the NDR showed that for a protracted exposure to low radiation doses there was a significant radiation effect on the promotion and malignant conversion, but not on the initiation stage of carcinogenesis. This stands in contrast to the findings for high-dose acute exposures in A-bomb survivors, where the initiation and possibly promotion were found to be affected by radiation exposure. Evidence of an inverse dose-rate effect (i.e. an increase in the risk with a protraction of a given cumulative dose) was found in the NDR cohort.