A new inverse regression model applied to radiation biodosimetry

Abstract

Biological dosimetry based on chromosome aberration scoring in peripheral blood lymphocytes enables timely assessment of the ionizing radiation dose absorbed by an individual. Here, new Bayesian-type count data inverse regression methods are introduced for situations where responses are Poisson or two-parameter compound Poisson distributed. Our Poisson models are calculated in a closed form, by means of Hermite and negative binomial (NB) distributions. For compound Poisson responses, complete and simplified models are provided. The simplified models are also expressible in a closed form and involve the use of compound Hermite and compound NB distributions. Three examples of applications are given that demonstrate the usefulness of these methodologies in cytogenetic radiation biodosimetry and in radiotherapy. We provide R and SAS codes which reproduce these examples.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios

“…The MULTIBIODOSE simplification provides more conservative dose estimation uncertainties than the ISO method, due to the fact that the largest covariance of the calibration curve fitting is negative ( Table 1). The IAEA simplification is not always conservative, in contrast to the more detailed Merkle methodology which includes both scoring uncertainty and error on the curve coefficients (IAEA 2011;Higueras et al 2015), and thus which results in the largest uncertainties.…”

“…Several publications have demonstrated that distributions other than the Poisson may be more applicable to yields of chromosome aberrations (e.g. Ainsbury et al 2013;Higueras et al 2015, Oliveira et al 2016, for example, and thus it is possible that radiation dose estimates may be more accurately described by alternative distributions to the Poisson. The Bayesian approach allows much more accurate characterization of the dose distribution as it is based on the combined information regarding the potential dose and exposure scenario, the calibration coefficients and the yield of aberrations.…”

“…Following calculation of the uncertainties using the data as described above for each of the ISO, MULTIBIODOSE, Merkle and IAEA and Bayesian methodologies, probabilities of correct rapid categorization as either <1 Gy; 1-2 Gy or 2þ Gy were then calculated using the cumulative standard normal distribution for doses in increments of 0.05 Gy from 0-3 Gy (results not reported), for each assay/exposure scenario, or using radir to implement the Bayesian methodology of Higueras et al (2015). For the calibrative density calculations, the absorbed dose prior distribution was assumed to be uniform; the calibration curve information was collected in a univariate prior assumed to be gamma distributed.…”

State-of-the-Art Advances in Radiation Biodosimetry for Mass Casualty Events Involving Radiation Exposure

“…However, there is currently growing acceptance of Bayesian methodologies, since they allow inclusion of previous information about the circumstances of exposure ( 70 – 72 ). More user-friendly software platforms based on Bayesian modeling have been developed to estimate radiation exposures including CytobayesJ and radir, a cytogenetic biodosimetry dose estimation application tailored for use in the R software platform ( 73 , 74 ).…”

Toxicogenetic biomonitoring of occupational risk induced by ionizing radiation

“…Numerous effects of radiation have been identified in association with mutations and genomic instability (breaks and actions in repair genes), angiogenesis (vascularisation and hypoxia), apoptosis (changes in p53), proliferative signaling (EGCR and TGF-α), suppression of cell cycle (ATM, p53 lock), energy dysregulation (HIF, c-MyC, glycolytic pathways inhibitors), tumor promotion and inflammation (p53 and ROS) and activation and inactivation of metastases (hypoxia, lactate) (Boss et al, 2014). Given the risks of cancer, studies suggest biological dosimeters guided by genetic instability mechanisms, is noted in summary in Table 3, especially for chromo-somal aberrations that may be markers for evaluation of dose and biological effects induced by IR (Higueras et al, 2015). Numerous effects of radiation (Table 3) have been identified in association with mutations and genomic instability (breaks and actions in repair genes), angiogenesis (vascularization and hypoxia), apoptosis (changes in p53), proliferative signaling (EGCR and TGF-α), suppression of cell cycle (ATM, locking p53), energy deregulation (HIF, c-MyC, glycolytic pathways inhibitors), tumor promotion and inflammation (p53 and ROS) and activation and inactivation of metastases (hypoxia, lactate) (Boss et al, 2014;Lee et al, 2014).…”