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Nuclear radiation and radioactive fallouts resulting from a nuclear weapon detonation or reactor accidents could result in injuries affecting multiple sensitive organs, defined as acute radiation syndrome (ARS). Rapid and early estimation of injuries to sensitive organs using markers of radiation response is critical for identifying individuals who could potentially exhibit ARS; however, there are currently no biodosimetry assays approved for human use. We developed a sensitive microRNA (miRNA)–based blood test for radiation dose reconstruction with ±0.5 Gy resolution at critical dose range. Radiation dose–dependent changes in miR-150-5p in blood were internally normalized by a miRNA, miR-23a-3p, that was nonresponsive to radiation. miR-23a-3p was not highly expressed in blood cells but was abundant in circulation and was released primarily from the lung. Our assay showed the capability for dose estimation within hours to 1 week after exposure using a drop of blood from mice. We tested this biodosimetry assay for estimation of absorbed ionizing radiation dose in mice of varying ages and after exposure to both improvised nuclear device (IND)–spectrum neutrons and gamma rays. Leukemia specimens from patients exposed to fractionated radiation showed depletion of miR-150-5p in blood. We bridged the exposure of these patients to fractionated radiation by comparing responses after fractionated versus single acute exposure in mice. Although validation in nonhuman primates is needed, this proof-of-concept study suggests the potential utility of this assay in radiation disaster management and clinical applications.

Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically.Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organat-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sorensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison.Results: Different ML algorithms result in similar test mean absolute errors: similar to 8 mm for liver LR, IS, and spleen AP, IS; similar to 5 mm for liver AP and spleen LR; similar to 80% for abdomen sDSC; and similar to 60% to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (+5-mm error for spleen IS, -10% sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60).Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria. (C) 2020 Society of Photo Optical Instrumentation Engineers (SPIE)

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average

The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated on virtual surrogates of patient anatomy called phantoms. Currently, phantoms are built to represent categories of patients based on reasonable yet simple criteria. This often results in phantoms that are too generic to accurately represent individual anatomies. We present a novel approach that combines imaging data and ML to build individualized phantoms automatically. We design a pipeline that, given features of patients treated in the pre-3D planning era when only 2D radiographs were available, as well as a database of 3D Computed Tomography (CT) imaging with organ segmentations, uses ML to predict how to assemble a patient-specific phantom. Using 60 abdominal CTs of pediatric patients between 2 to 6 years of age, we find that our approach delivers significantly more representative phantoms compared to using current phantom building criteria, in terms of shape and location of two considered organs (liver and spleen), and shape of the abdomen. Furthermore, as interpretability is often central to trust ML models in medical contexts, among other ML algorithms we consider the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA), that learns readable mathematical expression models. We find that the readability of its output does not compromise prediction performance as GP-GOMEA delivered the best performing models.

The majority of existing computational phantoms are designed to represent workers in typical standing anatomical postures with fixed arm and leg positions. However, workers found in accident-related scenarios often assume varied postures. This paper describes the development and application of two phantoms with adjusted postures specified by data acquired from a motion capture system to simulate unique human postures found in a 1999 criticality accident that took place at a JCO facility in Tokai-Mura, Japan. In the course of this accident, two workers were fatally exposed to extremely high levels of radiation. Implementation of the emergent techniques discussed produced more accurate and more detailed dose estimates for the two workers than were reported in previous studies. A total-body dose of 6.43 and 26.38 Gy was estimated for the two workers, who assumed a crouching and a standing posture, respectively. Additionally, organ-specific dose estimates were determined, including a 7.93 Gy dose to the thyroid and 6.11 Gy dose to the stomach for the crouching worker and a 41.71 Gy dose to the liver and a 37.26 Gy dose to the stomach for the standing worker. Implications for the medical prognosis of the workers are discussed, and the results of this study were found to correlate better with the patient outcome than previous estimates, suggesting potential future applications of such methods for improved epidemiological studies involving next-generation computational phantom tools.

High-frequency Q-band (37 GHz) electron paramagnetic resonance (EPR) dosimetry allows to perform fast (i.e., measurement time

Quantification of radiation dose to normal tissue during radiotherapy is critical for assessing risk for radiotherapy-related late effects, including subsequent neoplasms (SNs). Case-control studies of SNs typically reconstruct absorbed radiation dose to the specific SN location using individual treatment parameters. A simplified method estimates the maximum prescribed target dose to the body region in which the SN arises. We compared doses and risk estimates from these methods using data from case-control studies of subsequent brain tumors (64 cases, 244 controls) and breast cancer (94 cases, 358 controls) nested within the Childhood Cancer Survivor Study (≥5-year survivors of childhood cancer diagnosed 1970–1986). The weighted kappa statistic [95% confidence interval (CI)] evaluating agreement between categorical (>0–9.9/10–19.9/20–29.9/≥30 Gy) body-region and tumor location-specific doses was 0.95 (0.91–0.98) for brain and 0.76 (0.69–0.82) for breast. The body-region and location-specific doses were assigned to the same dose category for a smaller proportion of patients treated with fields delivering a heterogeneous dose across the tissue of interest (e.g., partial brain field = 57.1%; mantle field = 61.3%) than patients treated with fields delivering a more homogeneous dose (e.g., whole brain field = 100%). Excess odds ratios per Gy (95% CI) from conditional logistic regression were 1.25 (0.33–6.33) and 1.20 (0.31–6.14) for brain tumors and 0.21 (0.05–0.77) and 0.10 (0.02–0.44) for breast cancer, using location-specific and body-region doses, respectively. We observed that body-region doses can approximate location-specific doses when the tissue of interest is clearly in the radiation field or outside the treated body region. Agreement is lower when there is greater ambiguity of SN location relative to the treatment field.

Epidemiologic studies that include patients who underwent radiation therapy for the treatment of cancer aim to quantify the relationship between radiotherapy and the risk of subsequent late effects. Because of the long follow-up period required to observe late effects, these studies are conducted retrospectively. The studies routinely include patients treated across numerous institutions using a wide range of technologies and represent treatments over several decades. As a result, determining the dose throughout the patient's body is uniquely challenging. Therefore, estimating doses throughout the patient's body for epidemiologic studies requires special methodologies that are generally applied to a wide range of radiotherapy techniques. Over ten years ago, the MD Anderson Late Effects Group described various dose reconstruction methods for therapeutic and diagnostic radiation exposure for epidemiologic studies. Here we provide an update to the most widely used dose reconstruction methodology for epidemiologic studies, analytical model calculations combined with a 3D age-specific computational phantom. In particular, we describe the various adaptations (and enhancements) of that methodology, as well as how they have been used in radiation epidemiology studies and may be used in future studies.

OBJECTIVES Diagnostic medical radiation workers in Korea have been officially monitored for their occupational radiation doses since 1996. The purpose of this study was to design models for reconstructing unknown individual radiation doses to which diagnostic radiation technologists were exposed before 1996. METHODS Radiation dose reconstruction models were developed by using cross-sectional survey data and the personal badge doses of 8167 radiologic technologists. The models included calendar year and age as predictors, and the participants were grouped into six categories according to their sex and facility type. The annual doses between 1971 and 1995 for those who were employed before 1996 were estimated using these models. RESULTS The calendar year and age were inversely related to the estimated radiation doses in the models of all six groups. The annual median estimated doses decreased from 9.45 mSv in 1971 to 1.26 mSv in 1995, and the associated dose variation also decreased with time. The estimated median badge doses from 1996 (1.22 mSv) to 2011 (0.30 mSv) were similar to the measured doses (1.68 mSv to 0.21 mSv) for the same years. Similar results were observed for all six groups. CONCLUSIONS The reconstruction models developed in this study may be useful for estimating historical occupational radiation doses received by medical radiologic technologists in Korea.

Radiation dose reconstruction is used to estimate exposure to radiation that has occurred externally, e.g., from an atomic bomb, or internally, e.g., from radionuclide ingestion. This commentary reviews techniques for biological dosimetry that have been developed to estimate radiation doses from internal exposures, but which can also be used to estimate external exposures. The author argues for increased development and use of these biological tools.

During the initial phases of the National Institute for Occupational Safety and Health Radiation Dose Reconstruction Program, all calculations of organ doses due to internally deposited radionuclides were performed using the Integrated Modules for Bioassay Analysis program. However, limitations associated with this program, including the need to calculate separate internal dose assessments for each radionuclide, created inefficiencies in the processing of claims. As a result, the National Institute for Occupational Safety and Health developed and introduced a suite of tools to expedite the process. The first of these was the Chronic Annual Dose Workbook program. This innovative tool permits a dose reconstructor to calculate, in a single step, an organ dose that involves up to 255 separate intakes in any combination of radionuclides, intake modes, and absorption types. In addition, the program enables dose reconstructors to determine the specific radionuclide characteristics that will deliver the highest organ dose for a specific intake. Furthermore, the results are displayed in a format that is compatible with the Interactive RadioEpidemiological Program, which is used by the U.S. Department of Labor in establishing the probability of causation. The value of the probability of causation, in combination with other information, subsequently enables the U.S. Department of Labor to render a decision on compensability. These developments have played a major role in enabling the dose reconstruction teams to meet the claim processing goals with increased efficiency and accuracy.

Over the past 65 years, hundreds of thousands of workers have been engaged in nuclear weapons-related activities for the U.S. Department of Energy or its predecessor agencies. To date, almost 27,000 such employees (or their survivors) have filed claims under Part B of the Energy Employees Occupational Illness Compensation Program Act of 2000, which provides monetary compensation and medical benefits to energy employees who have developed certain types of cancer that have been determined, under the guidelines of the program, to have resulted from occupational radiation exposure covered under the Act. Although it is difficult to predict the number of cancer claims that will be evaluated under this program, the number could double or triple. In each case, the processing of a claim requires that the National Institute for Occupational Safety and Health reconstruct the radiation dose received by the employee followed by a determination by the U.S. Department of Labor as to whether the employee was "at least as likely as not" to have sustained the cancer as a result of his or her occupational exposure to ionizing radiation. Although some of the dose assessments are straightforward, many are extremely complex due to (1) missing, non-interpretable, or undocumented records; (2) a wide variety of external and internal exposure conditions; and/or (3) highly variable work assignments and work loads. The program objectives are to process claims in an effective, efficient, and timely manner. One of the initial challenges was to develop the necessary infrastructure to meet these objectives. Subsequent challenges included documenting that assessments are fair and scientifically consistent. Ensuring that each claimant receives the "benefit of the doubt" in any cases where the required background information and data are ambiguous or not available is also an important objective. Fortunately, there are some aspects of the processing requirements that have tended to reduce the complexity, two examples being that compensation is based on exposures that occurred during covered employment after a cancer has developed and that the required dose estimates are for individual body organs, not effective doses. Throughout the process, every effort has been made to ensure that the dose assessments have the support of the best available science.

Stakeholders have raised numerous issues regarding the scientific basis of radiation dose reconstruction for compensation. These issues can be grouped into three broad categories: data issues, dosimetry issues, and compensation issues. Data issues include demographic data of the worker, changes in site operations over time (both production and exposure control), characterization of episodic vs. chronic exposures, and the use of coworker data. Dosimetry issues include methods for assessment of ambient exposures, missed dose, unmonitored dose, and medical x-ray dose incurred as a condition of employment. Specific issues related to external dose include the sensitivity, angular and energy dependence of personal monitors, exposure geometries, and the accompanying uncertainties. Those related to internal dose include sensitivity of bioassay methods, uncertainties in biokinetic models, appropriate dose coefficients, and modeling uncertainties. Compensation issues include uncertainties in the risk models and use of the 99 th percentile of the distribution of probability of causation for awarding compensation. A review of the scientific literature and analysis of each of these issues distinguishes factors that play a major role in the compensation decision from those that do not.

EPR Dosimetry/Tooth Enamel/Sensitivity. Electron paramagnetic resonance (EPR) has been successfully used as a physical technique for g amma radiation dose reconstruction using calcified tissues. To minimize potential discrepancies between EPR readings in future studies, the effects of cavity response factor, tooth position and donor gender on the estimated gamma radiation dose were studied. It was found that the EPR response per sample mass used for assessment of doses in teeth outside of the 70–100 mg range should be corrected by a factor which is a function of the sample mass. In the EPR measurements, the difference in sensitivity of different tooth positions to γ-radiation was taken into consideration. It was determined that among all the premolars and molars tooth positions, the relative standard deviation of sensitivity was 6.5%, with the wisdom teeth and the first molars having the highest and lowest sensitivity to γ-radiation, respectively. The current results reveal no effect of the donor gender on the sensitivity to γ-radiation.

A methodology was developed for reduction of uncertainties in estimates of internal dose for residents of the Techa Riverside communities, who were exposed as a result of releases of radionuclides from the Mayak plutonium production facility in 1949-56. The 'Techa River Dosimetry System' (TRDS) was specifically elaborated for reconstruction of doses. A preliminary analysis of uncertainty for doses estimated using the current version of the TRDS showed large ranges in the uncertainty of internal absorbed dose and led to suggestions of methods to reduce uncertainties. The new methodological approaches described in this paper will allow for significant reduction of uncertainties of 90Sr- dose. The major sources of reduction are: making use of individual measured values of 90Sr and through development of a Household Registry to associate unmeasured persons with measured persons living in the same household(s). © The Author 2007. Published by Oxford University Press. All rights reserved.

Radiation dose reconstruction systems for large-scale epidemiological studies are sophisticated both in providing estimates of dose and in representing dosimetry uncertainty. For example, a computer program was used by the Hanford Thyroid Disease Study to provide 100 realizations of possible dose to study participants. The variation in realizations reflected the range of possible dose for each cohort member consistent with the data on dose determinates in the cohort. Another example is the Mayak Worker Dosimetry System 2013 which estimates both external and internal exposures and provides multiple realizations of "possible" dose history to workers given dose determinants. This paper takes up the problem of dealing with complex dosimetry systems that provide multiple realizations of dose in an epidemiologic analysis. In this paper we derive expected scores and the information matrix for a model used widely in radiation epidemiology, namely the linear excess relative risk (ERR) model that allows for a linear dose response (risk in relation to radiation) and distinguishes between modifiers of background rates and of the excess risk due to exposure. We show that treating the mean dose for each individual (calculated by averaging over the realizations) as if it was true dose (ignoring both shared and unshared dosimetry errors) gives asymptotically unbiased estimates (i.e. the score has expectation zero) and valid tests of the null hypothesis that the ERR slope β is zero. Although the score is unbiased the information matrix (and hence the standard errors of the estimate of β) is biased for β≠0 when ignoring errors in dose estimates, and we show how to adjust the information matrix to remove this bias, using the multiple realizations of dose. The use of these methods in the context of several studies including, the Mayak Worker Cohort, and the U.S. Atomic Veterans Study, is discussed.

The radioecological situation in the East-Kazakhstan region was studied by electron paramagnetic resonance (EPR), radiography and nuclear spectroscopy methods. The eastern part of this region borders the Semipalatinsk nuclear test site. Radiation doses for 33 residents of this region were measured by EPR dosimetry in tooth enamel. It was found that for 25% of the residents the measured radiation doses do not exceed the background level. The rest of the doses exceed the background level by a factor of 2–4 on the average. A new method of nuclear explosion dating was developed on the basis of the EPR measurements of the free radical concentration in annual tree rings. Their maximal concentration corresponds to the years when nuclear explosions were executed. The obtained results correlate well with the commonly accepted radiography method. Both methods show a maximum of radionuclide levels in the years of nuclear testing. The plutonium-239 content in residents hair samples was found to be equal to (0.8±0.2)·10−9 g/kg and with activity of 1.9±0.4 Bq/kg. This is by a factor of 20 higher than the permitted content for the population.

Between 1949 and 1989 the Semipalatinsk nuclear test site (SNTS), an area of 19,000 square km in northeastern Kazakhstan, was the location of over 400 nuclear test explosions with a total explosive energy of 6.6 Mt TNT (trinitrotoluene or trotyl) equivalent. It is estimated that the bulk of the radiation exposure to the population resulted from three tests, conducted in 1949, 1951, and 1953 although estimations of radiation doses received by the local population have varied significantly. Analysis of the published ESR dose reconstruction results for residents of the villages near the SNTS show that they do not correlate well with other methods of dose assessment (e.g. model dose calculation and thermo luminescence dosimetry (TLD) in bricks). The most significant difference in dose estimations was found for the population of Dolon, which was exposed as result of the first Soviet nuclear test in 1949. Published results of ESR measurements in tooth enamel are considerably lower than other dose estimations. Detailed analysis of these results is provided and a possible explanation for this discrepancy and ways to eliminate it are suggested.

The aim of this paper is to analyze the lower limit of detection (LLD), linearity of dose response, variation of radiation sensitivity between different tooth enamel samples, and time/temperature stability of EPR biodosimetry in tooth enamel. The theoretical LLD is shown to be 0.46 mGy, which is far lower than the measured value of about 30 mGy. The main issues to lowering LLD are the differentiation of the radiation-induced component against the total EPR spectrum and the complex nature of the dose dependence of the EPR signal. The following questions are also discussed in detail: need for exfoliated or extracted teeth from persons of interest, accounting for background radiation contribution; conversion of tooth enamel absorbed dose to effective dose; accounting for internal exposure specifically from bone-seeking radionuclides. Conclusions on future development of EPR retrospective biodosimetry are made.

Two classes of anatomic models currently exist for use in both radiation protection and radiation dose reconstruction: stylized mathematical models and tomographic voxel models. The former utilize 3D surface equations to represent internal organ structure and external body shape, while the latter are based on segmented CT or MR images of a single individual. While tomographic models are clearly more anthropomorphic than stylized models, a given model's characterization as being anthropometric is dependent upon the reference human to which the model is compared. In the present study, data on total body mass, standing/sitting heights and body mass index are collected and reviewed for the US population covering the time interval from 1971 to 2000. These same anthropometric parameters are then assembled for the ORNL series of stylized models, the GSF series of tomographic models (Golem, Helga, Donna, etc), the adult male Zubal tomographic model and the UF newborn tomographic model. The stylized ORNL models of the adult male and female are found to be fairly representative of present-day average US males and females, respectively, in terms of both standing and sitting heights for ages between 20 and 60-80 years. While the ORNL adult male model provides a reasonably close match to the total body mass of the average US 21-year-old male (within approximately 5%), present-day 40-year-old males have an average total body mass that is approximately 16% higher. For radiation protection purposes, the use of the larger 73.7 kg adult ORNL stylized hermaphrodite model provides a much closer representation of average present-day US females at ages ranging from 20 to 70 years. In terms of the adult tomographic models from the GSF series, only Donna (40-year-old F) closely matches her age-matched US counterpart in terms of average body mass. Regarding standing heights, the better matches to US age-correlated averages belong to Irene (32-year-old F) for the females and Golem (38-year-old M) for the males. Both Helga (27-year-old F) and Donna, however, provide good matches to average US sitting heights for adult females, while Golem and Otoko (male of unknown age) yield sitting heights that are slightly below US adult male averages. Finally, Helga is seen as the only GSF tomographic female model that yields a body mass index in line with her average US female counterpart at age 26. In terms of dose reconstruction activities, however, all current tomographic voxel models are valuable assets in attempting to cover the broad distribution of individual anthropometric parameters representative of the current US population. It is highly recommended that similar attempts to create a broad library of tomographic models be initiated in the United States and elsewhere to complement and extend the limited number of tomographic models presently available for these efforts.

The aim of this work was to estimate absorbed doses received by a victim of the radiation accident with a 60 Co source in Henan province, China. With a Monte Carlo stochastic simulation method, an estimation method for doses to the radiation accident victim was made. It utilised a mathematical model of adult man (MIRD) and a relative applied computer program was developed. By means of the simulated conditions of the accident, the absorbed doses to the victim's main organ and total body doses were estimated. The results estimated by our Monte Carlo method are close to those of experimental simulation measurement of the accident. With its convenience and rapidity, this method will be valuable for radiation dose reconstruction for victims in radiation accidents.

With the aim of carrying out a long-term medical follow-up with radiation dose reconstruction, a cohort of subjects was selected among inhabitants of the most contaminated area in Russia following the Chernobyl accident (the western districts of Bryansk region). The cohort is comprised of 1065 subjects who were under 10 years old at the time of the accident. Most of them were examined on health status in the Chernobyl Sasakawa Health and Medical Cooperation Project. Since the main findings of studies in the project were thyroid abnormalities, selection of subjects was conducted on the basis of the plausible estimates of radiation dose to the thyroid. To estimate thyroid doses, the data from direct measurements of 131I in the thyroid and questionnaire data on individual dietary habits in May 1986 were used. Reasonable approximations were applied to reconstruct individual doses from available data, including doses for those persons who had not been measured for thyroidal radioiodine. The distribution of internal radiation dose to the thyroid among cohort members was obtained. The individual doses to particular subjects are estimated with inevitably essential degree of uncertainty. However, the distribution of subjects into wide dose intervals, from under 200 mGy to over 2 Gy, seems to be an acceptable approach for cohort study in radiation epidemiology.

A computer routine was developed for automatic deconvolution of electron paramagnetic resonance spectra of tooth enamel samples for individual radiation dose reconstruction in the low dose region. The deconvolution routine uses the non-linear least square fit of a model simulating a tooth enamel spectrum by superposition of derivative Gaussian functions to obtain the amplitude of the dosimetric radiation induced signal. The parameters of the model and of the routine were optimized on a dose response level using a criterion of the least standard deviation of the derived radiation induced signal amplitude from the regression line vs. the nominal doses for the series of spectra of samples irradiated in known doses in the range 0-500 mGy. It was found that for the series of spectra of the heterogeneous samples (every sample is prepared from different teeth of different persons), it is essential to vary in the least square fit the parameters describing the shape of the native background signal in order to obtain the best accuracy. In the case of the series of spectra of the homogeneous (pooled) samples, almost the same accuracy of the results was obtained using the procedures with varied and fixed background signal parameters. The standard error of the dose reconstruction by the optimized deconvolution procedure was estimated as about 30 mGy for heterogeneous samples and 20 mGy for homogeneous samples.

A software code was developed for fitting the EPR tooth enamel spectrum using linear combinations of Gaussian functions. The software is fastening EPR dose reconstruction for routine applications. The verification of the software was done comparing with the selective saturation method as an independent procedure of signal evaluation. Both methods were applied for the dose reconstruction of 13 teeth mostly from the Techa riverside. The evaluated doses by the two methods demonstrate an excellent agreement.

Radiation epidemiology seeks to describe and quantify the risk of health effects, often cancer, in populations exposed to ionizing and non-ionizing radiation. To do so, it is important to estimate organ or tissue doses for large numbers of exposed individuals with a moderate to high degree of certainty. Unlike dosimetry for establishing compliance with regulations, which relies on doses estimated for representative, maximally exposed, or highest-risk persons, dosimetry for analytical epidemiological studies usually requires developing new dosimetric models or tailoring existing ones to reach a higher level of individualization. The majority of radiation epidemiological studies conducted to date have required the reconstruction of dose to individuals or study populations that were exposed many years ago. This presents a major challenge to researchers, because measurements often are not available or do not exist in forms that are directly usable for calculating radiation doses on an individual basis. The goal of this special issue of Radiation Research is to introduce readers to an array of dosimetric methods and applications that have been developed to reconstruct radiation exposures for epidemiological studies. We intend to fill a void in the technical literature by describing these methods in terms understandable to epidemiologists, dosimetrists and statisticians so that these research tools can be used by other members of the research community. This publication follows the 1995 National Academy of Sciences/National Research Council report entitled Radiation Dose Reconstruction for Epidemiologic Uses(1). That book summarized the views and expertise of a group of scientists who participated in a workshop on dose reconstruction for environmental radiation exposures. Since then, the methods for dose reconstruction have continued to advance, and the knowledge gained from epidemiological studies of populations exposed to radiation from many different sources has grown. It is therefore timely to publish a special issue of Radiation Research devoted to the methods used to estimate medical, occupational and environ

In the years after the Chernobyl reactor accident, many studies of the radiation exposure levels and resulting health effects in the countries of the CIS have been conducted. The increasing incidence of childhood thyroid cancers in Belarus and Ukraine has stimulated worldwide multi- and bilateral cooperations with those countries and Russia in order to optimize benefits for those directly affected, but also to enlarge current knowledge of the consequences of reactor accidents. An international workshop on dose reconstruction was held in Bad Honnef, June 6 to 9, 1994, to address the problems which arise in dose reconstruction. The main objectives of this workshop were to bring together the best professional expertise and scientific knowledge and to achieve a better, multi-disciplinary harmonisation of the different scientific approaches. After intensive discussions the participants of this workshop formulated the following scientific recommendations for radiation dose reconstruction.

Chromosome translocations are persistent indicators of prior exposure to ionizing radiation and the development of 'chromosome painting' to efficiently detect translocations has resulted in a powerful biological dosimetry tool for radiation dose reconstruction. However, the actual stability of the translocation frequency with time after exposure must be measured before it can be used reliably to obtain doses for individuals exposed years or decades previously. Human chromosome painting probes were used here to measure reciprocal translocation frequencies in cells from two tissues of 8 rhesus monkeys (Macaca mulatta) irradiated almost three decades previously. Six of the monkeys were exposed in 1965 to whole-body (fully penetrating) radiation and two were unexposed controls. The primates were irradiated as juveniles to single doses of 0.56, 1.13, 2.00, or 2.25 Gy. Blood lymphocytes (and skin fibroblasts from one individual) were obtained for cytogenetic analysis in 1993, near the end of the animals' lifespans. Results show identical dose-response relationships 28 y after exposure in vivo and immediately after exposure in vitro. Because chromosome aberrations are induced with identical frequencies in vivo and in vitro, these results demonstrate that the translocation frequencies induced in 1965 have not changed significantly during the almost three decades since exposure. Finally, our emerging biodosimetry data for individual radiation workers are now confirming the utility of reciprocal translocations measured by FISH in radiation dose reconstruction.

The Hanford Environmental Dose Reconstruction (HEDR) Project was initiated in response to public, state, and Native American concerns about the potential health effects from the historical releases of radioactive materials from the Hanford Site. To allay concerns that DOE, the site owner and operator, was controlling the study, an independent Technical Steering Panel (TSP)—made up of technical specialists and public, state, and Native American representatives—was formed to direct the study. The TSP instituted a management process based on complete openness and public involvement in all aspects of the project including the definition of scope, budget, and priorities. This open approach—which could be called conducting “science in a fishbowl”—required more time and energy to ensure that concerns were understood and that stakeholders understood the radiation dose reconstruction process and results.

A brief description is provided of the basic concepts related to 'internal dose' and how it differs from doses that result from radioactive materials and direct radiation outside of the body. The principles of radiation dose reconstruction, as applied to both internal and external doses, are discussed on the basis of a recent publication prepared by the US National Council on Radiation Protection and Measurements. Finally, ideas are introduced related to residual radioactive contamination in the environment that has resulted from the releases from damaged reactors and also to the management of wastes that may be generated in both regional cleanup and decommissioning of the Fukushima nuclear power plant.

The Mayak Production Association (MPA) was established in the late 1940s in accordance with a special Decree of the USSR Government for the production of nuclear weapons. In early years of MPA operation, due to the lack of experience and absence of effective methods of RW management, the enterprise had extensive routine (designed) and non-routine (accidental) releases of gaseous radioactive wastes to the atmosphere. These practices resulted in additional technogenic radiation exposure of residents inhabiting populated areas near the MPA. The primary objective of ongoing studies under JCCRER Project 1.4 is to estimate doses to the residents of Ozersk due to releases of radioactive substances from the stacks of MPA. Preliminary scoping studies have demonstrated that releases of radioactive iodine (131I) from the stacks of the Mayak Radiochemical Plant represented the major contribution to the dose to residents of Ozersk and of other nearby populated areas. The behavior of 131I in the environment and of 131I migration through biological food chains (vegetation-cows-milk-humans) indicated a need for use of special mathematical models to perform the estimation of radiation doses to the population. The goal of this work is to select an appropriate model of the iodine migration in biological food chainsmore » and to justify numerical values of the model parameters.« less

This brief report documents the selection of parameters needed to support individual-dose calculations from 131I released into the environment with gaseous effluents from the Mayak Production Association.

An updated deterministic version of the Techa River Dosimetry System (TRDS-2009D) has been developed to estimate individual doses from external exposure and intake of radionuclides for residents living on the Techa River contaminated as a result of radioactive releases from the Mayak plutonium facility in 1949–1956. The TRDS-2009D is designed as a flexible system that uses, depending on the input data for an individual, various elements of system databases to provide the dosimetric variables requested by the user. Several phases are included in the computation schedule. The first phase includes calculations with use of a common protocol for all cohort members based on village-average-intake functions and external dose rates; individual data on age, gender and history of residence are included in the first phase. This phase results in dose estimates similar to those obtained with system TRDS-2000 used previously to derive risks of health effects in the Techa River Cohort. The second phase includes refinement of individual internal doses for those persons who have had body-burden measurements or exposure parameters specific to the household where he/she lived on the Techa River. The third phase includes summation of individual doses from environmental exposure and from radiological examinations. The results of TRDS-2009Dmore » dose calculations have demonstrated for the ETRC members on average a moderate increase in RBM dose estimates (34%) and a minor increase (5%) in estimates of stomach dose. The calculations for the members of the ETROC indicated similar small changes for stomach, but significant increase in RBM doses (400%). Individual-dose assessments performed with use of TRDS-2009D have been provided to epidemiologists for exploratory risk analysis in the ETRC and ETROC. These data provide an opportunity to evaluate the possible impact on radiogenic risk of such factors as confounding exposure (environmental and medical), changes in the Techa River source-term data and the change of the approach to individual internal dose estimation (90Sr-body burden measurements and family correlations vs. village averages). Our further plan is to upgrade the TRDS-2009D and to complete a stochastic version of the dosimetry system.« less

Approximately two years after promulgation of the Energy Employees Occupational Illness Compensation Program Act, the National Institute for Occupational Safety and Health Office of Compensation and Analysis Support selected a contractor team to perform many aspects of the radiation dose reconstruction process. The project scope and schedule necessitated the development of an organization involving a comparatively large number of health physicists. From the initial stages, there were many technical and managerial challenges that required continuous planning, integration, and conflict resolution. This paper identifies those challenges and describes the resolutions and lessons learned. These insights are hopefully useful to managers of similar scientific projects, especially those requiring significant data, technical methods, and calculations. The most complex challenge has been to complete defensible, individualized dose reconstructions that support timely compensation decisions at an acceptable production level. Adherence to applying claimant-favorable and transparent science consistent with the requirements of the Act has been the key to establishing credibility, which is essential to this large and complex project involving tens of thousands of individual stakeholders. The initial challenges included garnering sufficient and capable scientific staff, developing an effective infrastructure, establishing necessary methods and procedures, and integrating activities to ensure consistent, quality products. The continuing challenges include maintaining the project focus on recommending a compensation determination (rather than generating an accurate dose reconstruction), managing the associated very large data and information management challenges, and ensuring quality control and assurance in the presence of an evolving infrastructure. The lessons learned concern project credibility, claimant favorability, project priorities, quality and consistency, and critical path project activities.