Abstract

Purpose

The traditional workflow for biological dosimetry based on manual scoring of dicentric chromosomes is very time consuming. Especially for large-scale scenarios or for low-dose exposures, high cell numbers have to be analyzed, requiring alternative scoring strategies. Semi-automatic scoring of dicentric chromosomes provides an opportunity to speed up the standard workflow of biological dosimetry. Due to automatic metaphase and chromosome detection, the number of counted chromosomes per metaphase is variable. This can potentially introduce overdispersion and statistical methods for conventional, manual scoring might not be applicable to data obtained by automatic scoring of dicentric chromosomes, potentially resulting in biased dose estimates and underestimated uncertainties. The identification of sources for overdispersion enables the development of methods appropriately accounting for increased dispersion levels.

Materials and methods

Calibration curves based on in vitro irradiated (137-Cs; 0.44 Gy/min) blood from three healthy donors were analyzed for systematic overdispersion, especially at higher doses (>2 Gy) of low LET radiation. For each donor, 12 doses in the range of 0–6 Gy were scored semi-automatically. The effect of chromosome number as a potential cause for the observed overdispersion was assessed. Statistical methods based on interaction models accounting for the number of detected chromosomes were developed for the estimation of calibration curves, dose and corresponding uncertainties. The dose estimation was performed based on a Bayesian Markov-Chain-Monte-Carlo method, providing high flexibility regarding the implementation of priors, likelihood and the functional form of the association between predictors and dicentric counts. The proposed methods were validated by simulations based on cross-validation.

Results

Increasing dose dependent overdispersion was observed for all three donors as well as considerable differences in dicentric counts between donors. Variations in the number of detected chromosomes between metaphases were identified as a major source for the observed overdispersion and the differences between donors. Persisting overdispersion beyond the contribution of chromosome number was modeled by a Negative Binomial distribution. Results from cross-validation suggested that the proposed statistical methods for dose estimation reduced bias in dose estimates, variability between dose estimates and improved the coverage of the estimated confidence intervals. However, the 95% confidence intervals were still slightly too permissive, suggesting additional unknown sources of apparent overdispersion.

Conclusions

A major source for the observed overdispersion could be identified, and statistical methods accounting for overdispersion introduced by variations in the number of detected chromosomes were developed, enabling more robust dose estimation and quantification of uncertainties for semi-automatic counting of dicentric chromosomes.

摘要

目的：传统的基于双中心染色体手动评分的生物剂量测定工作流程非常耗时。特别是对于大规模场景或低剂量暴露，必须分析高细胞数，这需要其他评分策略。双中心染色体的半自动评分为加快生物剂量测定的标准工作流程提供了机会。由于自动进行了中期和染色体检测，每个中期的计数染色体数是可变的。这可能会引入常规方法的过度分散和统计方法，手动评分可能不适用于通过自动对双着丝粒染色体评分获得的数据，从而可能导致剂量估计偏差和不确定性低估。

材料和方法：分析了基于来自三位健康供体的体外照射（137-Cs; 0.44 Gy / min）血液的校准曲线的系统过度分散，特别是在较高剂量（> 2 Gy）的低LET辐射下。对于每个供体，在0-6 Gy范围内对12个剂量进行半自动评分。评估了染色体数目作为观察到的过度分散的潜在原因的影响。开发了基于相互作用模型的统计方法，该方法考虑了检测到的染色体数，用于估计校准曲线，剂量和相应的不确定性。剂量估算是基于贝叶斯M​​arkov-Chain-Monte-Carlo方法进行的，在先验方法，可能性和预测变量与双中心计数之间的关联的功能形式的实现方面具有很高的灵活性。

结果：对于所有三个供体，观察到剂量依赖性过度分散的增加，并且在供体之间的双着丝粒计数上存在相当大的差异。中期之间检测到的染色体数目的变化被确定为观察到的过度分散和供体之间差异的主要来源。通过负二项分布来模拟超出染色体数目贡献的持续过度分散。交叉验证的结果表明，建议的剂量估计统计方法可减少剂量估计的偏差，剂量估计之间的差异并提高估计的置信区间的覆盖范围。但是，95％的置信区间仍然略微太宽容，表明出现了明显的过度分散的其他未知来源。

结论：可以确定观察到的过度分散的主要来源，并开发了统计方法，该方法解释了由于检测到的染色体数目变化而引入的过度分散，从而可以更可靠地估计剂量并定量分析不确定性或对双中心染色体进行半自动计数。