Biological and physical retrospective dosimetry are recognised as key techniques to provide individual estimates of dose following unplanned exposures to ionising radiation. Whilst there has been a relatively large amount of recent development in the biological and physical procedures, development of statistical analysis techniques has failed to keep pace. The aim of this paper is to review the current state of the art in uncertainty analysis techniques across the ‘EURADOS Working Group 10—Retrospective dosimetry’ members, to give concrete examples of implementation of the techniques recommended in the international standards, and to further promote the use of Monte Carlo techniques to support characterisation of uncertainties. It is concluded that sufficient techniques are available and in use by most laboratories for acute, whole body exposures to highly penetrating radiation, but further work will be required to ensure that statistical analysis is always wholly sufficient for the more complex exposure scenarios.

Introduction In the event of a radiological accident or incident, the aim of biological dosimetry is to convert the yield of a specific biomarker of exposure to ionizing radiation into an absorbed dose. Since the 1980s, various tools have been used to deal with the statistical procedures needed for biological dosimetry, and in general those who made several calculations for different biomarkers were based on closed source software. Here we present a new open source program, Biodose Tools, that has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry). Material and Methods The application has been developed using the R programming language and the shiny package as a framework to create a user-friendly online solution. Since no unique method exists for the different mathematical processes, several meetings and periodic correspondence were held in order to reach a consensus on the solutions to be implemented. Results The current version 3.6.1 supports dose-effect fitting for dicentric and translocation assay. For dose estimation Biodose Tools implements those methods indicated in international guidelines and a specific method to assess heterogeneous exposures. The app can include information on the irradiation conditions to generate the calibration curve. Also, in the dose estimate, information about the accident can be included as well as the explanation of the results obtained. Because the app allows generating a report in various formats, it allows traceability of each biological dosimetry study carried out. The app has been used globally in different exercises and training, which has made it possible to find errors and improve the app itself. There are some features that still need consensus, such as curve fitting and dose estimation using micronucleus analysis. It is also planned to include a package dedicated to interlaboratory comparisons and the incorporation of Bayesian methods for dose estimation. Conclusion Biodose Tools provides an open-source solution for biological dosimetry laboratories. The consensus reached helps to harmonize the way in which uncertainties are calculated. In addition, because each laboratory can download and customize the app’s source code, it offers a platform to integrate new features.

Children and young adults with heart disease appear to be at increased risk of developing cancer, although the reasons for this are unclear. A cohort of 11,270 individuals, who underwent cardiac catheterizations while aged ≤ 22 years in the UK, was established from hospital records. Radiation doses from cardiac catheterizations and CT scans were estimated. The cohort was matched with the NHS Central Register and NHS Transplant Registry to determine cancer incidence and transplantation status. Standardized incidence ratios (SIR) with associated confidence intervals (CI) were calculated. The excess relative risk (ERR) of lymphohaematopoietic neoplasia was also calculated using Poisson regression. The SIR was raised for all malignancies (2.32, 95% CI 1.65, 3.17), lymphoma (8.34, 95% CI 5.22, 12.61) and leukaemia (2.11, 95% CI 0.82, 4.42). After censoring transplant recipients, post-transplant, the SIR was reduced to 0.90 (95% CI 0.49, 1.49) for all malignancies. All lymphomas developed post-transplant. The SIR for all malignancies developing 5 years from the first cardiac catheterization (2 years for leukaemia/lymphoma) remained raised (3.01, 95% CI 2.09, 4.19) but was again reduced after censoring transplant recipients (0.98, 95% CI 0.48, 1.77). The ERR per mGy bone marrow dose for lymphohaematopoietic neoplasia was reduced from 0.541 (95% CI 0.104, 1.807) to 0.018 (95% CI − 0.002, 0.096) where transplantation status was accounted for as a time-dependent background risk factor. In conclusion, transplantation appears to be a large contributor to elevated cancer rates in this patient group. This is likely to be mainly due to associated immunosuppression, however, radiation exposure may also be a contributing factor.

Classical methods of assessing the uncertainty associated with radiation doses estimated using cytogenetic techniques are now extremely well defined. However, several authors have suggested that a Bayesian approach to uncertainty estimation may be more suitable for cytogenetic data, which are inherently stochastic in nature. The Bayesian analysis framework focuses on identification of probability distributions (for yield of aberrations or estimated dose), which also means that uncertainty is an intrinsic part of the analysis, rather than an ‘afterthought’. In this paper Bayesian, as well as some more advanced classical, data analysis methods for radiation cytogenetics are reviewed that have been proposed in the literature. A practical overview of Bayesian cytogenetic dose estimation is also presented, with worked examples from the literature.

Purpose: Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. Materials and methods: Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises. Results: The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed. Conclusions: Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.