Fluoroscopy X-Ray Organ-Specific Dosimetry System (FLUXOR) for Estimation of Organ Doses and Their Uncertainties in the Canadian Fluoroscopy Cohort Study.

As part of ongoing efforts to assess lifespan disease mortality and incidence in 63,715 patients from the Canadian Fluoroscopy Cohort Study (CFCS) who were treated for tuberculosis between 1930 and 1969, we developed a new FLUoroscopy X-ray ORgan-specific dosimetry system (FLUXOR) to estimate radiation doses to various organs and tissues. Approximately 45% of patients received medical procedures accompanied by fluoroscopy, including artificial pneumothorax (air in pleural cavity to collapse of lungs), pneumoperitoneum (air in peritoneal cavity), aspiration of fluid from pleural cavity and gastrointestinal series. In addition, patients received chest radiographs for purposes of diagnosis and monitoring of disease status. FLUXOR utilizes age-, sex- and body size-dependent dose coefficients for fluoroscopy and radiography exams, estimated using radiation transport simulations in up-to-date computational hybrid anthropomorphic phantoms. The phantoms include an updated heart model, and were adjusted to match the estimated mean height and body mass of tuberculosis patients in Canada during the relevant time period. Patient-specific data (machine settings, exposure duration, patient orientation) used during individual fluoroscopy or radiography exams were not recorded. Doses to patients were based on parameter values inferred from interviews with 91 physicians practicing at the time, historical literature, and estimated number of procedures from patient records. FLUXOR uses probability distributions to represent the uncertainty in the unknown true, average value of each dosimetry parameter. Uncertainties were shared across all patients within specific subgroups of the cohort, defined by age at treatment, sex, type of procedure, time period of exams and region (Nova Scotia or other provinces). Monte Carlo techniques were used to propagate uncertainties, by sampling alternative average values for each parameter. Alternative average doses per exam were estimated for patients in each subgroup, with the total average dose per individual determined by the number of exams received. This process was repeated to produce alternative cohort vectors of average organ doses per patient. This article presents estimates of doses to lungs, female breast, active bone marrow and heart wall. Means and 95% confidence intervals (CI) of average organ doses across all 63,715 patients were 320 (160, 560) mGy to lungs, 250 (120, 450) mGy to female breast, 190 (100, 340) mGy to heart wall and 92 (47, 160) mGy to active bone marrow. Approximately 60% of all patients had average doses to the four studied organs of less than 10 mGy, 10% received between 10 and 100 mGy, 25% between 100 and 1,000 mGy, and 5% above 1,000 mGy. Pneumothorax was the medical procedure that accounted for the largest contribution to cohort average doses. The major contributors to uncertainty in estimated doses per procedure for the four organs of interest are the uncertainties in exposure duration, tube voltage, tube output, and patient orientation relative to the X-ray tube, with the uncertainty in exposure duration being most often the dominant source. Uncertainty in patient orientation was important for doses to female breast, and, to a lesser degree, for doses to heart wall. The uncertainty in number of exams was an important contributor to uncertainty for ∼30% of patients. The estimated organ doses and their uncertainties will be used for analyses of incidence and mortality of cancer and non-cancer diseases. The CFCS cohort is an important addition to existing radio-epidemiological cohorts, given the moderate-to-high doses received fractionated over several years, the type of irradiation (external irradiation only), radiation type (X rays only), a balanced combination of both genders and inclusion of people of all ages.

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

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

Organ-specific dose coefficients derived from Monte Carlo simulations for historical (1930s to 1960s) fluoroscopic and radiographic examinations of tuberculosis patients.

This work provides dose coefficients necessary to reconstruct doses used in epidemiological studies of tuberculosis patients treated from the 1930s through the 1960s, who were exposed to diagnostic imaging while undergoing treatment. We made use of averaged imaging parameters from measurement data, physician interviews, and available literature of the Canadian Fluoroscopy Cohort Study and, on occasion, from a similar study of tuberculosis patients from Massachusetts, United States, treated between 1925 and 1954. We used computational phantoms of the human anatomy and Monte Carlo radiation transport methods to compute dose coefficients that relate dose in air, at a point 20 cm away from the source, to absorbed dose in 58 organs. We selected five male and five female phantoms, based on the mean height and weight of Canadian tuberculosis patients in that era, for the 1-, 5-, 10-, 15-year old and adult ages. Using high-performance computers at the National Institutes of Health, we simulated 2,400 unique fluoroscopic and radiographic exposures by varying x-ray beam quality, field size, field shuttering, imaged anatomy, phantom orientation, and computational phantom. Compared with previous dose coefficients reported for this population, our dosimetry system uses improved anatomical phantoms constructed from computed tomography imaging datasets. The new set of dose coefficients includes tissues that were not previously assessed, in particular, for tissues outside the x-ray field or for pediatric patients. In addition, we provide dose coefficients for radiography and for fluoroscopic procedures not previously assessed in the dosimetry of this cohort (i.e. pneumoperitoneum and chest aspirations). These new dose coefficients would allow a comprehensive assessment of exposures in the cohort. In addition to providing newly derived dose coefficients, we believe the automation and methods developed to complete these dosimetry calculations are generalizable and can be applied to other epidemiological studies interested in an exposure assessment from medical x-ray imaging. These epidemiological studies provide important data for assessing health risks of radiation exposure to help inform the current system of radiological protection and efforts to optimize the use of radiation in medical studies.

Methods to account for uncertainties in exposure assessment in studies of environmental exposures.

BACKGROUND: Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies. MAIN TEXT: We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates. CONCLUSIONS: In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Method for analyzing left-censored bioassay data in large cohort studies.

In retrospective epidemiological studies of large cohorts of workers exposed to radioactive materials, it is often necessary to analyze large numbers of bioassay data sets containing censored values, or values recorded as less than a detection limit. Censored bioassay data create problems for all bioassay analysis methods, including analytical techniques based on least-squares regression to estimate intakes. A method is presented here that uses a simple empirically-derived equation for imputing replacement values for urine uranium concentration results reported as zero or less than a detection limit, that produces minimal bias in intakes estimated using least-square regression methods with the assumption of lognormally distributed measurement errors.

Beyond dose assessment: using risk with full disclosure of uncertainty in public and scientific communication.

Evaluations of radiation exposures of workers and the public traditionally focus on assessments of radiation dose, especially annual dose, without explicitly evaluating the health risk associated with those exposures, principally the risk of radiation-induced cancer. When dose is the endpoint of an assessment, opportunities to communicate the significance of exposures are limited to comparisons with dose criteria in regulations, doses due to natural background or medical x-rays, and doses above which a statistically significant increase of disease has been observed in epidemiologic studies. Risk assessment generally addresses the chance (probability) that specific diseases might be induced by past, present, or future exposure. The risk of cancer per unit dose will vary depending on gender, age, exposure type (acute or chronic), and radiation type. It is not uncommon to find that two individuals with the same effective dose will have substantially different risks. Risk assessment has shown, for example, that: (a) medical exposures to computed tomography scans have become a leading source of future risk to the general population, and that the risk would be increased above recently published estimates if the incidence of skin cancer and the increased risk from exposure to x-rays compared with high-energy photons were taken into account; (b) indoor radon is a significant contributor to the baseline risk of lung cancer, particularly among people who have never smoked; and (c) members of the public who were exposed in childhood to I in fallout from atmospheric nuclear weapons tests and were diagnosed with thyroid cancer later in life would frequently meet criteria established for federal compensation of cancers experienced by energy workers and military participants at atmospheric weapons tests. Risk estimation also enables comparisons of impacts of exposures to radiation and chemical carcinogens and other hazards to life and health. Communication of risk with uncertainty is essential for reaching informed consent, whether communicating to a larger community debating the tradeoffs of risks and benefits of an action that involves radiation exposure or communicating at the level of a physician and patient.

Interactive RadioEpidemiological Program (IREP): a web-based tool for estimating probability of causation/assigned share of radiogenic cancers.

The Interactive RadioEpidemiological Program (IREP) is a Web-based, interactive computer code that is used to estimate the probability that a given cancer in an individual was induced by given exposures to ionizing radiation. IREP was developed by a Working Group of the National Cancer Institute and Centers for Disease Control and Prevention, and was adopted and modified by the National Institute for Occupational Safety and Health (NIOSH) for use in adjudicating claims for compensation for cancer under the Energy Employees Occupational Illness Compensation Program Act of 2000. In this paper, the quantity calculated in IREP is referred to as "probability of causation/assigned share" (PC/AS). PC/AS for a given cancer in an individual is calculated on the basis of an estimate of the excess relative risk (ERR) associated with given radiation exposures and the relationship PC/AS = ERR/ERR+1. IREP accounts for uncertainties in calculating probability distributions of ERR and PC/AS. An accounting of uncertainty is necessary when decisions about granting claims for compensation for cancer are made on the basis of an estimate of the upper 99% credibility limit of PC/AS to give claimants the "benefit of the doubt." This paper discusses models and methods incorporated in IREP to estimate ERR and PC/AS. Approaches to accounting for uncertainty are emphasized, and limitations of IREP are discussed. Although IREP is intended to provide unbiased estimates of ERR and PC/AS and their uncertainties to represent the current state of knowledge, there are situations described in this paper in which NIOSH, as a matter of policy, makes assumptions that give a higher estimate of the upper 99% credibility limit of PC/AS than other plausible alternatives and, thus, are more favorable to claimants.