Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Abstract

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning

“…There is a need for large scale biodosimetry testing, which requires efficient screening techniques to differentiate exposed individuals from non-exposed individuals and to determine the severity of exposure 2 . Current diagnostic techniques, including the cytogenetic gold standard 3 – 6 , may require several days to provide accurate dose estimates 1 , 7 of large cohorts. To address the need for faster diagnostic techniques that accurately measure radiation exposures, gene signatures based on transcriptomic data have been introduced 7 – 10 .…”

Radiation Dose Estimation by Completely Automated Interpretation of the Dicentric Chromosome Assay

“…Samples from previous exercises (designated HS## and CS## in Table 1) were analyzed with these curves before versus after application of matched image selection models. After completing training on ADCI (12) , authors from HC and CNL performed these analyses independently.…”

Applications of Fluorescence in Situ Hybridization in Radiation Cytogenetic Biodosimetry and Population Monitoring

“…Timely evaluation of dicentric chromosomes for dose estimation in mass casualty radiological or nuclear incidents provides a bottleneck due to restricted number of cytogenetic biodosimetry laboratories and trained personnel. Several efforts have been made in the recent past to make the DCA suitable for mass casualty incidents: (I) optimization of chromosome preparation [35], (II) automated scoring [36][37][38][39][40][41][42], (III) sample tracking [43], (IV) establishment of cytogenetic networks [44][45][46] and (V) development of electronic tools for sharing digital images among the national and international cytogenetic biodosimetry laboratories to increase surge capacity for dicentric scoring [47][48][49][50]. Additionally, attempts have been made to minimize the inter-laboratory variation(s) in dicentric chromosome analysis by developing a Standard Operating Procedure (SOP), a common calibration curve and participation in annual inter-laboratory comparison exercise for competency [47,48,51].…”