1 Introduction
1.1 Biological dosimetry
Biological dosimetry aims at estimating the absorbed dose in an individual in which an exposure to ionising radiation (IR) is suspected, by means of analysing biomarkers with a clear dose-effect relationship (International Atomic Energy Agency 2011). A great majority of biomarkers of dose exposure come from the analysis of the induced DNA damage, most of them analysed using cytogenetic techniques such as dicentric, translocation, or micronucleus assays. Dose assessment is based on converting an observed yield of aberrations (e.g., the frequency of dicentrics present in peripheral blood lymphocytes) into an absorbed dose using a pre-established calibration curve. This process requires mathematical models and the assumptions on statistical probability distribution of the aberration in question. First, to establish a calibration curve, blood samples must be uniformly irradiated at several doses and the observed distribution of aberrations is mostly assumed to follow a Poisson distribution. More precisely, it is assumed that for low-LET (linear energy transfer) radiation types, uniform exposures result in dicentric counts that follow a Poisson distribution (Bauchinger and Schmid 1973; A. A. Edwards, Lloyd, and Purrott 1979). High-LET radiation types tend to show overdispersion (Virsik and Harder 1981; Brame and Groer 2002; Puig and Barquinero 2011). Overdispersion also often arises in micronuclei counts and \(\gamma\)-H2AX foci data (Vral, Fenech, and Thierens 2011; Einbeck et al. 2018). Then the observed yields at different doses are used to construct a calibration curve assuming a Linear-Quadratic (LQ) or Linear (L) model, depending on the radiation quality. While low-LET exposures show a linear-quadratic dose-effect relationship, high-LET exposures tend to be linear, (A. A. Edwards, Lloyd, and Purrott 1980). The coefficients of these models can be estimated using maximum likelihood or iteratively reweighted least squares approaches (Merkle 1983).
In case of a radiation accident, the observed distribution of aberration counts is tested for deviations from the Poisson distribution to distinguish homogeneous and heterogeneous exposures. The observed count is inverse regressed by the calibration curve and uncertainties are usually calculated considering only the uncertainty relative to the yield observed or considering also uncertainties coming from the calibration curve (A. A. Edwards 1978; Merkle 1983; Savage et al. 2000). Procedures to consider both sources of uncertainties are not simple, as the error is made up of two components: (a) uncertainties from the distribution of observed counts, and (b) uncertainties from the LQ or L models of the calibration curve (Merkle 1983; Savage et al. 2000). In case of a heterogeneous exposure, additional distributional assumptions have to be made (Dolphin 1969; Masao S. Sasaki and Miyata 1968; Pujol et al. 2016). Bayesian methods have been recently proposed to consider both sources of uncertainty, and for both, whole-body (homogeneous) and partial-body (heterogeneous) dose assessment (Ainsbury et al. 2014; Moriña et al. 2015; Higueras et al. 2015).
1.2 Software implementations
The tools to deal with some or all of the statistical procedures used in biological dosimetry have evolved since the 1980s (see Ainsbury and Barquinero 2009). These tools ranged from adapted Microsoft Excel sheets to specific programs (the later summarised in Table 1.1). The specific programs were based on closed source software and, for example, used a single methodology to calculate uncertainties. Thus, improvements were restricted to those who manage the source code, and the end-user cannot modify or implement any improvement. In addition, there is a possibility of misunderstanding or misuse of these closed programs.
| Name | Language(s) | Framework(s) | Author(s) |
|---|---|---|---|
| MLPOL | Fortran 77 | – | A. Edwards (c1994) |
| MLREG | – | – | Bundesamt für Strahlenschutz (c1996) |
| DOSGEN | Pascal | Turbo Pascal | Garcia Lima and Tomas Zerquera (1996) |
| CABAS | Object Pascal | Delphi | Deperas et al. (2007) |
| Dose Estimate | Object Pascal | Delphi 6 | Ainsbury and Lloyd (2010) |
Recently, using the R project for statistical computing (R Core Team 2022) some scripts have been written for biological dosimetry purposes. Although R programming is based on open source code, most end-users performing biodosimetry are usually not familiar with programming and mathematical or statistical modelling that is required to use or implement R source code. It is therefore required to provide tools with GUIs that are easy to use and that provide the required functionality to obtain reliable dose estimates.
In this paper we present {biodosetools} (styled as Biodose Tools), an R package developed using {shiny} (Chang et al. 2022), to offer an online and easy-to-use solution to be used by biological dosimetry laboratories, as well as a tool for statisticians to manually perform the underlying calculations in R. The package is available from the Comprehensive R Archive Network (CRAN) at and GitHub at and can be distributed under the GPL-3.0 license (GNU General Public License v3.0). Biodose Tools has been developed under the umbrella of RENEB (Running the European Network of Biological and retrospective Physical dosimetry).
1.3 R Shiny as a statistical tool
1.3.1 R
R is a general purpose package that includes support for a wide variety of modern statistical and graphical methods (many of which have been contributed by users). It is available for GNU/Linux, macOS, and Windows. The R Foundation for Statistical Computing holds and administers the copyright of the R software and documentation. R is available under the terms of the Free Software Foundation’s GNU General Public License in source code form.
1.3.2 R Shiny
Shiny is an R package that makes it easy to build interactive web apps straight from R, combining the computational power of R with the interactivity of the modern web.
- Access to a powerful web framework for building web applications using R.
- Being in complete control of the mathematics and statistics behind.
- Rethink what biologists and laboratories need in their everyday workflow.
- Build a complete, fully documented tool.
- Provide an Open Source tool to the community.
1.3.3 Modules
Different modules can be built for each function (fitting, dose estimation, …) while being totally independent from one another.
This opens up the possibility to implement statistical methods not included in the IAEA Manual (2011) (International Atomic Energy Agency 2011).