Farrah Flegal

Validation of the cytokinesis-block micronucleus (CBMN) assay for use as a triage biological dosimetry tool

Traditionally, the dicentric chromosome assay (DCA) has been used to derive biological dose estimates for unknown radiological exposures. While sensitive, this assay requires highly trained evaluators and is extremely time consuming. The cytokinesis-block micronucleus (CBMN) assay has been suggested as an alternative to the DCA, as it is much faster to evaluate samples and requires less technical expertise. In order to validate this assay for triage biodosimetry, dose-response curves were generated for six donors at eight doses of gamma-radiation (0-4.0 Gy). Each sample was evaluated by 12 individuals, among three different laboratories and the incidence of micronuclei was determined after counting 50-500 binucleated cells. This study demonstrated that the CBMN assay was capable of detecting radiation doses >or=1 Gy after scoring only 200 binucleated cells. As such, the CBMN assay may provide a sensitive and reliable technique for deployment as an initial screening tool in a large-scale radiological emergency where large numbers of biological dose estimates are required.

RENEB intercomparisons applying the conventional Dicentric Chromosome Assay (DCA)

Abstract Purpose: Two quality controlled inter-laboratory exercises were organized within the EU project ‘Realizing the European Network of Biodosimetry (RENEB)’ to further optimize the dicentric chromosome assay (DCA) and to identify needs for training and harmonization activities within the RENEB network. Materials and methods: The general study design included blood shipment, sample processing, analysis of chromosome aberrations and radiation dose assessment. After manual scoring of dicentric chromosomes in different cell numbers dose estimations and corresponding 95% confidence intervals were submitted by the participants. Results: The shipment of blood samples to the partners in the European Community (EU) were performed successfully. Outside the EU unacceptable delays occurred. The results of the dose estimation demonstrate a very successful classification of the blood samples in medically relevant groups. In comparison to the 1st exercise the 2nd intercomparison showed an improvement in the accuracy of dose estimations especially for the high dose point. Conclusions: In case of a large-scale radiological incident, the pooling of ressources by networks can enhance the rapid classification of individuals in medically relevant treatment groups based on the DCA. The performance of the RENEB network as a whole has clearly benefited from harmonization processes and specific training activities for the network partners.

Evaluation of the annual Canadian biodosimetry network intercomparisons

Abstract Purpose: To evaluate the importance of annual intercomparisons for maintaining the capacity and capabilities of a well-established biodosimetry network in conjunction with assessing efficient and effective analysis methods for emergency response. Materials and methods: Annual intercomparisons were conducted between laboratories in the Canadian National Biological Dosimetry Response Plan. Intercomparisons were performed over a six-year period and comprised of the shipment of 10–12 irradiated, blinded blood samples for analysis by each of the participating laboratories. Dose estimates were determined by each laboratory using the dicentric chromosome assay (conventional and QuickScan scoring) and where possible the cytokinesis block micronucleus (CBMN) assay. Dose estimates were returned to the lead laboratory for evaluation and comparison. Results: Individual laboratories performed comparably from year to year with only slight fluctuations in performance. Dose estimates using the dicentric chromosome assay were accurate about 80% of the time and the QuickScan method for scoring the dicentric chromosome assay was proven to reduce the time of analysis without having a significant effect on the dose estimates. Although analysis with the CBMN assay was comparable to QuickScan scoring with respect to speed, the accuracy of the dose estimates was greatly reduced. Conclusions: Annual intercomparisons are necessary to maintain a network of laboratories for emergency response biodosimetry as they evoke confidence in their capabilities.

Automating dicentric chromosome detection from cytogenetic biodosimetry data

We present a prototype software system with sufficient capacity and speed to estimate radiation exposures in a mass casualty event by counting dicentric chromosomes (DCs) in metaphase cells from many individuals. Top-ranked metaphase cell images are segmented by classifying and defining chromosomes with an active contour gradient vector field (GVF) and by determining centromere locations along the centreline. The centreline is extracted by discrete curve evolution (DCE) skeleton branch pruning and curve interpolation. Centromere detection minimises the global width and DAPI-staining intensity profiles along the centreline. A second centromere is identified by reapplying this procedure after masking the first. Dicentrics can be identified from features that capture width and intensity profile characteristics as well as local shape features of the object contour at candidate pixel locations. The correct location of the centromere is also refined in chromosomes with sister chromatid separation. The overall algorithm has both high sensitivity (85 %) and specificity (94 %). Results are independent of the shape and structure of chromosomes in different cells, or the laboratory preparation protocol followed. The prototype software was recoded in C++/OpenCV; image processing was accelerated by data and task parallelisation with Message Passaging Interface and Intel Threading Building Blocks and an asynchronous non-blocking I/O strategy. Relative to a serial process, metaphase ranking, GVF and DCE are, respectively, 100 and 300-fold faster on an 8-core desktop and 64-core cluster computers. The software was then ported to a 1024-core supercomputer, which processed 200 metaphase images each from 1025 specimens in 1.4 h.

Towards large scale automated interpretation of cytogenetic biodosimetry data

Cytogenetic biodosimetry is the definitive test for assessing exposure to ionizing radiation. It involves manual assessment of the frequency of dicentric chromosomes (DCs) on a microscope slide, which potentially contains hundreds of metaphase cells. We developed an algorithm that can automatically and accurately locate centromeres in DAPI-stained metaphase chromosomes and that will detect DCs. In this algorithm, a set of 200-250 metaphase cell images are ranked and sorted. The 50 top-ranked images are used in the triage DC assay (DCA). To meet the requirement of DCA in a mass casualty event, we are accelerating our algorithm through parallelization. In this paper, we present our finding in accelerating our ranking and segmentation algorithms. Using data parallelization on a desktop system, the ranking module was up to 4-fold faster than the serial version and the Gradient Vector Flow module (GVF) used in our segmentation algorithm was up to 8-fold faster. Large scale data parallelization of the ranking module processed 18,694 samples in 11.40 hr. Task parallelization of Image ranking with parallelized labeling on a desktop computer reduced processing time by 20% of a serial process, and GVF module recoded with parallelized matrix inversion reduced time by 70%. Overall, we estimate that the automated DCA will require around 1 min per sample on a 64-core computing system. Our long-term goal is to implement these algorithms on a high performance computer cluster to assess radiation exposures for thousands of individuals in a few hours.

Radiation Dose Estimation by Automated Cytogenetic Biodosimetry

The dose from ionizing radiation exposure can be interpolated from a calibration curve fit to the frequency of dicentric chromosomes (DCs) at multiple doses. As DC counts are manually determined, there is an acute need for accurate, fully automated biodosimetry calibration curve generation and analysis of exposed samples. Software, the Automated Dicentric Chromosome Identifier (ADCI), is presented which detects and discriminates DCs from monocentric chromosomes, computes biodosimetry calibration curves and estimates radiation dose. Images of metaphase cells from samples, exposed at 1.4-3.4 Gy, that had been manually scored by two reference laboratories were reanalyzed with ADCI. This resulted in estimated exposures within 0.4-1.1 Gy of the physical dose. Therefore, ADCI can determine radiation dose with accuracies comparable to standard triage biodosimetry. Calibration curves were generated from metaphase images in ~10 h, and dose estimations required ~0.8 h per 500 image sample. Running multiple instances of ADCI may be an effective response to a mass casualty radiation event.

Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing.

Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa‐stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2‐4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ = 1.3, TPR = 0.52 and PPV = 0.83, while at σ = 1.6, the TPR = 0.65 and PPV = 0.72. At low dose and σ = 1.3, TPR = 0.67 and PPV = 0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures. Microsc. Res. Tech. 79:393–402, 2016.

Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell selection

Accurate digital image analysis of abnormal microscopic structures relies on high quality images and on minimizing the rates of false positive (FP) and negative objects in images. Cytogenetic biodosimetry detects dicentric chromosomes (DCs) that arise from exposure to ionizing radiation, and determines radiation dose received based on DC frequency. Improvements in automated DC recognition increase the accuracy of dose estimates by reclassifying FP DCs as monocentric chromosomes or chromosome fragments. We also present image segmentation methods to rank high quality digital metaphase images and eliminate suboptimal metaphase cells. A set of chromosome morphology segmentation methods selectively filtered out FP DCs arising primarily from sister chromatid separation, chromosome fragmentation, and cellular debris. This reduced FPs by an average of 55% and was highly specific to these abnormal structures (≥97.7%) in three samples. Additional filters selectively removed images with incomplete, highly overlapped, or missing metaphase cells, or with poor overall chromosome morphologies that increased FP rates. Image selection is optimized and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Applying the same image segmentation filtering procedures to both calibration and test samples reduced the average dose estimation error from 0.4 Gy to <0.2 Gy, obviating the need to first manually review these images. This reliable and scalable solution enables batch processing for multiple samples of unknown dose, and meets current requirements for triage radiation biodosimetry of high quality metaphase cell preparations.