Application of a semi-automated dicentric scoring system in triage and monitoring occupational radiation exposure

The dicentric chromosome assay (DCA) is considered the gold standard for radiation biodosimetry, but it is limited by its long dicentric scoring time and need for skilled scorers. The automation of scoring dicentrics has been considered a strategy to overcome the constraints of DCA. However, the studies on automated scoring methods are limited compared to those on conventional manual DCA. Our study aims to assess the performance of a semi-automated scoring method for DCA using ex vivo and in vivo irradiated samples. Dose estimations of 39 blind samples irradiated ex vivo and 35 industrial radiographers occupationally exposed in vivo were estimated using the manual and semi-automated scoring methods and subsequently compared. The semi-automated scoring method, which removed the false positives of automated scoring using the dicentric chromosome (DC) scoring algorithm, had an accuracy of 94.9% in the ex vivo irradiated samples. It also had more than 90% accuracy, sensitivity, and specificity to distinguish binary dose categories reflecting clinical, diagnostic, and epidemiological significance. These data were comparable to those of manual DCA. Moreover, Cohen's kappa statistic and McNemar's test showed a substantial agreement between the two methods for categorizing in vivo samples into never and ever radiation exposure. There was also a significant correlation between the two methods. Despite of comparable results with two methods, lower sensitivity of semi-automated scoring method could be limited to assess various radiation exposures. Taken together, our findings show the semi-automated scoring method can provide accurate dose estimation rapidly, and can be useful as an alternative to manual DCA for biodosimetry in large-scale accidents or cases to monitor radiation exposure of radiation workers

Automated method · Biodosimetry · Biology · Chromosome · Cohen's kappa · Dicentric chromosome · Ex vivo · False positive paradox · In vivo · Irradiation · Kappa · Machine learning · McNemar's test · Nuclear medicine · Radiology · Statistics · Carcinogens and Genotoxicity Assessment · Computer Science · Mathematics · Medicine · Radiation Dose and Imaging · Radioactivity and Radon Measurements · Artificial Intelligence