Improving radiation dose estimation using the gamma-H2AX biomarker

TL;DR

This paper introduces Bayesian methods combined with Laplace approximation for more accurate and efficient radiation dose estimation using gamma-H2AX biomarker data, accounting for uncertainty in exposure timing.

Contribution

The paper presents novel Bayesian approaches that incorporate timing uncertainty and utilize Laplace approximation to improve radiation dose estimation accuracy and efficiency.

Findings

Bayesian methods improve dose estimation accuracy.

Laplace approximation reduces computational time.

Models are practical for real gamma-H2AX data analysis.

Abstract

To predict the health effects of accidental or therapeutic radiation exposure, one must estimate the radiation dose that person received. A well-known ionising radiation biomarker, phosphorylated gamma-H2AX protein, is used to evaluate cell damage and is thus suitable for the dose estimation process. In this paper, we present new Bayesian methods that, in contrast to approaches where estimation is carried out at predetermined post-irradiation times, allow for uncertainty regarding the time since radiation exposure and, as a result, produce more precise results. We also use the Laplace approximation method, which drastically cuts down on the time needed to get results. Real data are used to illustrate the methods, and analyses indicate that the models might be a practical choice for the gamma-H2AX biomarker dose estimation process.