Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents

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Mutation Research/Genetic Toxicology and Environmental Mutagenesis journal homepage: www.elsevier.com/locate/gentox Community address: www.elsevier.com/locate/mutres

Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents夽 H. Romm a,∗ , E. Ainsbury b , S. Barnard b , L. Barrios c , J.F. Barquinero d , C. Beinke e , M. Deperas f , E. Gregoire d , A. Koivistoinen g , C. Lindholm g , J. Moquet b , U. Oestreicher a , R. Puig c , K. Rothkamm b , S. Sommer h , H. Thierens i , V. Vandersickel i , A. Vral i , A. Wojcik f a

Bundesamt fuer Strahlenschutz, Germany Public Health England, United Kingdom c Universitat Autonoma de Barcelona, Spain d Institut de Radioprotection et de Sûreté Nucleaire, France e Bundeswehr Institute of Radiobiology, Germany f Stockholm University, Sweden g Radiation and Nuclear Safety Authority, Finland h Institute of Nuclear Chemistry and Technology, Poland i University of Ghent, Belgium b

a r t i c l e

i n f o

Article history: Received 2 May 2013 Accepted 7 May 2013 Available online xxx Keywords: Dicentric assay Automatic scoring Biological dosimetry Radiation accident Emergency preparedness Radiation dose assessment

a b s t r a c t Mass casualty scenarios of radiation exposure require high throughput biological dosimetry techniques for population triage in order to rapidly identify individuals who require clinical treatment. The manual dicentric assay is a highly suitable technique, but it is also very time consuming and requires well trained scorers. In the framework of the MULTIBIODOSE EU FP7 project, semi-automated dicentric scoring has been established in six European biodosimetry laboratories. Whole blood was irradiated with a Co-60 gamma source resulting in 8 different doses between 0 and 4.5 Gy and then shipped to the six participating laboratories. To investigate two different scoring strategies, cell cultures were set up with short term (2–3 h) or long term (24 h) colcemid treatment. Three classifiers for automatic dicentric detection were applied, two of which were developed specifically for these two different culture techniques. The automation procedure included metaphase finding, capture of cells at high resolution and detection of dicentric candidates. The automatically detected dicentric candidates were then evaluated by a trained human scorer, which led to the term ‘semi-automated’ being applied to the analysis. The six participating laboratories established at least one semi-automated calibration curve each, using the appropriate classifier for their colcemid treatment time. There was no significant difference between the calibration curves established, regardless of the classifier used. The ratio of false positive to true positive dicentric candidates was dose dependent. The total staff effort required for analysing 150 metaphases using the semi-automated approach was 2 min as opposed to 60 min for manual scoring of 50 metaphases. Semi-automated dicentric scoring is a useful tool in a large scale radiation accident as it enables high throughput screening of samples for fast triage of potentially exposed individuals. Furthermore, the results from the participating laboratories were comparable which supports networking between laboratories for this assay. © 2013 Elsevier B.V. All rights reserved.

1. Introduction

夽 The research leading to these results has received funding from the European Union’s Seventh Framework Program (FP7/2007–2013) under grant agreement No. 241536. ∗ Corresponding author at: Bundesamt fuer Strahlenschutz, Ingolstaedter Landstr. 1, 85764 Neuherberg, Germany. Tel.: +49 30 18333 2214; fax: +49 30 18333 2205. E-mail address: [email protected] (H. Romm).

In the case of a large scale radiation accident a considerable number of individuals may be exposed to unknown doses of radiation. There is therefore a need for fast and reliable tools to estimate the actual absorbed dose of potentially exposed individuals. Furthermore there is a need to identify those individuals who require urgent medical intervention amongst the concerned public (the ‘worried well’). In order to obtain a detailed overview of the radiation exposure characteristics, it has been shown that it is necessary

1383-5718/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

Please cite this article in press as: H. Romm, et al., Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res.: Genet. Toxicol. Environ. Mutagen. (2013), http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

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to apply a multiparameter approach, using several biological and physical assays [1]. The dicentric assay is the gold standard of biological dosimetry, and many years of experience have demonstrated its reliability and capability to assess radiation dose and guide medical treatment. However, the classical assay requires lengthy scoring procedures and so, for an emergency situation involving a large number of people, it is desirable to increase the throughput of the method. In recent years several strategies have been developed to address this need: mutual assistance networks have been established at both national [2,3] and international level [4–9]. The dicentric assay methods have been harmonized and standardized for purposes of quality assurance and to ensure that the operational status of the dicentric assay is maintained [4,10–12]. As recommended in IAEA [10] successful international intercomparisons have been frequently performed [4,13–15]. Scoring strategies have recently been adapted to enhance throughput by reducing the number of cells to be analyzed [16,17] and/or by scoring cells in a less restrictive manner [18,19]. Additionally, scoring exercises have been performed using web based scoring of images [20,21]. Promising results have already been shown by a network of eight collaborating European laboratories in the frame of the MULTIBIODOSE project [22]. The aim of this multi-disciplinary project is to analyze a variety of biodosimetric tools and to adapt them to different mass casualty scenarios. Within the project, the successful application of the dicentric assay in triage mode has been confirmed. It has been demonstrated that high throughput scoring in cases of large scale radiation accidents results in accurate dosimetry following acute, partial-body and protracted exposures [23]. This paper details the establishment of software-based automated scoring of dicentric chromosomes for individual dose assessment [24,25] in a European network of biodosimetry laboratories. The key research question was whether automatic dicentric scoring provides a reliable and reproducible method of dose assessment. Six laboratories established their own calibration curves for the automatic scoring of dicentric chromosomes after gammaray exposure. Three classifiers for automatic dicentric detection were applied, two of which were developed for culture techniques with short term (2–3 h) and long term (24 h) colcemid treatment. The procedure of automation included metaphase finding, image capture at high resolution and detection of dicentric candidates. Automatically detected dicentric candidates were evaluated by a trained human scorer, resulting in the term semi-automated analysis. The impact of using the different standard protocols of culturing and slide preparation, as well as the two different classifiers, on the dose response was investigated. The scoring software works by selecting which metaphase spreads are acceptable for analysis and which should be rejected because of poor quality or too many objects. Therefore, it was also necessary to define the scoring criteria. In this case, the frequency and morphology of dicentric candidates identified by the software were used to establish dose response curves and estimate doses. As the semi-automated scoring procedure introduces several new experimental variables, it was necessary to investigate whether the technique produces comparable results between the 6 participating labs and thus whether networking between labs is possible.

2. Materials and methods 2.1. Irradiations To set up dose response curves for automated scoring of dicentric chromosomes after acute whole body exposure, whole blood was 60 Co-gamma irradiated in vitro in a water bath at 37 ◦ C at the irradiation facilities of the University of Ghent. The range covered 8 different doses between 0 and 4.5 Gy (dose rate: 0.27 Gy/min). Blood was taken from 14 healthy donors with informed consent and the approval of a local ethics committee. Blood from 6 donors was used for 0 Gy controls; blood from 8 donors was used for the irradiated samples. After irradiation, samples were held at

37 ◦ C for a minimum of 2 h before shipping at room temperature according to the existing IATA packing instruction (PI 650). 2.2. Shipment of blood Aliquots of 2 ml whole blood per dose point was dispensed into heparinized vials using a vacutainer system by Becton Dickinson. The shipments of irradiated blood samples together with sham-irradiated controls were sent from Ghent to the six participating laboratories, by DHL express service. The samples were transported as biological material characterized as UN 3373 Biological Substance Category B. Each package of blood samples contained a temperature logger and TLD dosemeter to document the temperature during transport and measure the environmental radiation dose received by the samples, as recommended by ISO standards [12]. During transport the temperature loggers remained in most cases within the range of 5 to 30 ◦ C. No abnormalities in mitogen stimulation and dicentric yields were observed. TLD readings indicated generally 0 Gy, with the highest dose recorded being 0.5 mGy, which is well below the detection limit of the assay of ∼100 mGy and which can therefore be ignored. 2.3. Cell cultures Each laboratory set up at least two blood cultures per sample following their own standard protocols, which are established according to the IAEA recommendations [10] and ISO standards [11,12]. In general aliquots of blood are set up in medium (MEM or RPMI 1640, with 10–20% fetal calf serum) and stimulated with Phytohemagglutinin (PHA) as mitogen. To investigate two different scoring techniques, cell cultures were set up in two participating labs (lab 2 & 4) with short term (2–3 h) colcemid treatment using Fluorescence plus Giemsa (FpG) staining; in 4 labs (lab 1, 3, 5 & 6) long term (24 h) colcemid treatment with simple Giemsa staining was used. The method of cell cycle control (only metaphases in 1st mitoses scored) therefore depended on the standard protocol already established in each participating lab. One lab failed in culturing the 3.0 and 4.5 Gy samples and therefore samples irradiated with 3.0 and 5.0 Gy from another gamma-beam source with similar dose rate were included. Further information regarding variation in the protocols and methods at each laboratory was collected using a questionnaire. 2.4. Analysis of dicentrics The resulting slides were analyzed using the automatic scoring system Metafer 4 by MetaSystems (Altlussheim, Germany) including the software modules for metaphase finding (MSearch), autocapturing of high resolution images at 63× magnification (with oil, AutoCapt) and automatic detection of dicentric candidates (DCScore). Here, in addition to the first established classifier (BfS-First-Classifier), two further classifiers (Table 1) for automatic dicentric detection were applied, which had been developed separately for the two different culture techniques (IRSNClassifier for short term colcemid or BfS-Classifier for long term colcemid) used in this study. Based on the classifier parameters, four laboratories rejected images with more than 55 objects, labs 2 and 4 included images with a maximum of 100 objects. Scorer involvement was required only to evaluate the automatically detected dicentric candidates, resulting in a semi-automated scoring approach. The scoring procedure in the semi-automatic mode is different from conventional manual scoring. While only complete cells (46 centromeres) are analyzed and all observed aberrations are counted in conventional scoring, scoring in automatic mode is restricted to the dicentric candidates detected by the software. An experienced scorer decides if the detected dicentric candidate (which is marked in a black frame, Fig. 1) can be confirmed as a dicentric chromosome or has to be rejected as a false positive. Further information such as undetected dicentrics (false negatives), number of acentric fragments or completeness of cells is not recorded. 2.5. Training in automation and semi-automatic dicentric analysis Before evaluation of dicentric candidates started, practical training in automation was provided for scorers at all six participating labs. The complete procedure of automatic analysis was demonstrated, and a number of potential pitfalls and sources of error were discussed. The scoring criteria for dicentric chromosomes were also harmonized before semi-automatic scoring commenced, using a gallery of 80 images containing dicentric candidates. The gallery was shared using the MULTIBIODOSE website (www.multibiodose.eu). The 80 images of 80 metaphases were evaluated by all 8 partners of MULTIBIODOSE and the results were then discussed in detail, with further training given where needed. The aim was to educate the partners in the handling of the complex software, to facilitate the production of optimal high resolution images and to ensure that the task was performed in a homogenous way by all participants. 2.6. Statistical analysis Each lab established at least one dose effect curve for automatic dicentric scoring. A common dose effect curve was obtained by pooling the data from all partners.

Please cite this article in press as: H. Romm, et al., Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res.: Genet. Toxicol. Environ. Mutagen. (2013), http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

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Fig. 1. Three dicentric candidates – detected by the DCScore software – are marked with a black frame. The scoring protocol is reduced to numbers of automatic detected dicentrics (ADics) and numbers of confirmed dicentrics (CDics).

The curves for the individual labs as well as the pooled curve were fitted using an iteratively reweighted least squared procedure, implemented with Dose Estimate [26]. Analysis of variance was used to investigate the interactions of the different experimental variables and chi-squared testing was used to determine if there were any differences between the curves. The dose effect curves were validated by the laboratories in a second step, whereby dose estimates were performed with slides prepared during simulation of three exposure scenarios of acute whole and partial body exposure and protracted exposure, with 3 different doses for each scenario (which will be described in future in a separate paper).

3. Results 3.1. Harmonisation of scoring criteria The results obtained in the frame of the harmonisation procedures analysing 80 images in a web based gallery showed a consensus of approximately 58 dicentric chromosomes. The mean number counted was 55 dicentrics, ranging from 42 to 66 dicentrics among the 8 laboratories. Identical results in all 8 laboratories were obtained for 43 images (53.8%), 7 laboratories agreed on a further 24 (30.0%) cells and 6 laboratories for another 9 (11.3%) cells. Only the remaining 4 (5.0%) images proved controversial and no consensus could be reached. The Chi-squared test was used to compare the actual scored values from each lab with the average number of dicentrics, which was not significantly different (p = 0.32). Overall, no laboratory counted a number of dicentrics which was significantly different from the mean (p > 0.05).

3.2. Characteristics of the classifier For the following investigations, all six participating laboratories used the same software modules by MetaSystems with full automatic finding of metaphases and capturing of images in a high resolution mode (63× magnification plus oil, 1280 × 1024 pixel, 256 gray levels). However, for the automatic detection of the dicentric candidates with the DCScore software module, three different classifiers were applied. From the 1980s until 2008 (until version 3.6.4 of the Metafer 4 software), only one classifier had been developed (BfS-First-Class.) and integrated in the DCscore module. This was based on a dataset of 1200 Giemsa stained metaphases with 1304 dicentric chromosomes included. This classifier was able to detect 654 dicentric chromosomes (50.2%) in the above mentioned dataset [27]. In 2008 the DCScore module was modified and a new classifier was established at IRSN (IRSN-Class.) [24]. The corresponding dataset included 5423 Fluorescence plus Giemsa stained metaphases (2 h of colcemid) with 1587 marked dicentrics. In 2010, one further classifier was established at BfS (BfS-Class.), which was based on 3804 Giemsa stained metaphases (24 h of colcemid) with 2303 marked dicentrics. In Table 2 the efficiency of the three classifiers in analysing the two new datasets is compared. The number of remaining (detectable) dicentrics in the analyzed images depends on the number of rejected images. Dicentrics detected by the software were regarded as either True Positives (TP) or False Positives (FP, Fig. 2). Undetected dicentrics were regarded as False Negatives (FN). When slides are analyzed, this evaluation takes about 1 s per image and

Please cite this article in press as: H. Romm, et al., Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res.: Genet. Toxicol. Environ. Mutagen. (2013), http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

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Fig. 2. Examples of rejected, false positive dicentric candidates (left: rendered image, right captured image). (A) overlapping chromosomes, (B) and (C) touching chromosomes, (D) twisted chromosome, (E) normal chromosome.

Table 1 The parameters of the DCScore BfS-Classifier (IRSN-Classifier) used in this study. DCScore classifier, version 3.6.7 Image processing operations for metaphase cell images: MedianV(3) SBLocMinSM16(2) SBLocMinSM16(2) SharpenS(5,5) SegThrMeta(0) NormGLevObjs(0,0) SegThrMeta(30) ExtendImage Image processing operations for chromosome images: SharpenS-IZP(5,4) MedianV(3) Average(3,2) Minimum chromosome width (pixel): Maximum chromosome width (pixel): Minimum chromosome length (pixel): Minimum chromosome area (pixel): Maximum chromosome area (pixel): Minimum chromosome aspect ratio (in/100): Minimum chromosome area/contour length ratio (in/100): Minimum number of chromosomes in metaphase cell: Maximum number of chromosomes in metaphase cell: Use automatic separation?: Maximum separation cost: Maximum centromere width: Centromere quality – use objects from . . .: Centromere quality – use objects to . . .: Centromere quality – specify objects in %: Centromere quality – minimum value: Result display delay time (sec). Result display – wait for key?: Saturation area for maximum gray level (pixel): Width profile 2 threshold (in %): Width profile 3 threshold (in %): Width profile gradient mean margin (in %): Medial axis quadratic deviation margin (in %): Length normalization – use objects from: Length normalization – use objects to . . .: Length normalization – specify objects in %: Normalize length to: Measurement range for profile valley areas: Measurement range for profile local means: Measurement range for profile local minima:

14 32 16 200 3000 126 229 9 55 (100) 0 3000 100 11 20 0 575 (700) 0 0 0 25 50 10 10 6 (8) 38 0 57 91 56 50

can thus be carried out very quickly by a trained human scorer. Both new classifiers were able to reduce the amount of FP and reduce the workload by a factor of two, and thus they made scoring of dicentric candidates much faster. However, the number of FN, which describes how many dicentrics remained undetected in the

analyzed cells, is also very important. This was in the range of about 50–60%. Here each classifier showed a higher efficiency in their original datasets (a clear “home” advantage). The percentage of FP and FN are combined as error index, which shows the similar performance of the two new classifiers and both perform better than the original classifier. 3.3. Establishment of dose effect curves by automated scoring In order to establish dose effect curves with the automatic scoring system, the six laboratories captured images from their own slides with one of the two different preparation methods (2–3 h or 24 h colcemid). The results of the automatic scoring in four labs, which used the IRSN-Classifier for dicentric detection are given in Table 3. Lab 1 and lab 3 captured images after 24 h colcemid culture time. Lab 2 and 4 used FpG stained slides after 2 or 3 h colcemid culture time. In general each laboratory captured enough cells to set up a clear dose effect relationship (numbers of cells ranged from 19,000 to 27,000 analyzed images per curve). The mean number of observed objects and chromosomes per cell was in the range of 41–45 and 30–32 depending on the preparation used. In all four labs the distribution of dicentrics followed in general a Poisson distribution. ANOVA showed no significant difference between the labs (p > 0.130). Although there was no significant difference between the two preparation methods in terms of the number of captured cells (p = 0.574), the number of scored cells (p = 0.430) or the overall yield of dicentrics (p = 0.980), the non-significant difference in slide preparation may still have some impact on the applicability and accuracy of the classifier which in turn may affect the number of detected dicentrics or the number of rejected cells. In Table 4 the results of the automatic scoring with the BfSClassifier are shown. All captured slides were prepared from samples after 24 h colcemid culture time. In this case, a significant difference was detected between the scoring labs in this group (p = 0.019). Two labs (1 & 3) used the same dataset of images for both investigations (Table 3 and 4) and can be compared directly. The number of rejected cells is higher for the IRSN-Classifier in comparison to the BfS-Classifier (Lab 1: 51.0% vs. 35.7% and Lab 3: 24.0% vs. 16.1%), which shows that the impact of the slide preparation on the classifier seemed to be different between the labs,

Table 2 Performance of three different classifiers in analysing two different data sets from images of human peripheral blood lymphocytes. The frequencies of true (TP, dicentrics) and false (FP, no dicentrics) positives and true (TN, Objects) and false (FN, undetected dicentrics) negatives during the detection of dicentric chromosomes are given. Classifier

No of images

Rejected images

Rejected rate

No of objects

No of dicentrics

No of chromosomes

TP count

TN count

FP count

FN count

FP %

FN %

Error index

Performance with IRSN dataset BfS-first-classifier 5423 768 5423 828 IRSN-classifier 5423 294 BfS-classifier

14.2% 15.3% 5.4%

207,954 192,346 215,822

1376 1365 1503

159,106 146,439 170,040

572 606 584

207,182 192,045 215,501

772 301 321

804 759 919

0.37 0.16 0.15

58.4 55.6 61.1

0.323 0.168 0.177

Performance with BfS dataset 1175 BfS-first-classifier 3804 IRSN-classifier 3804 1231 3804 631 BfS-classifier

30.9% 32.4% 16.6%

123,278 112,945 144,231

1689 1663 2028

84,973 79,845 103,744

804 846 1088

122,843 112,727 143,997

435 218 234

885 817 940

0.35 0.19 0.16

52.4 49.1 46.4

0.283 0.173 0.148

Please cite this article in press as: H. Romm, et al., Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res.: Genet. Toxicol. Environ. Mutagen. (2013), http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

Scored cells

Lab 1

0 0.25 0.75 1 1.5 2.5 3 5

31,469 7733 5229 4928 439 1168 2509 757

14,007 2904 3436 2962 321 848 1549 526

17,462 4829 1793 1966 118 320 960 231

55.5 62.4 34.3 39.9 26.9 27.4 38.3 30.5

597 125 247 292 44 234 422 358

0 0.25 0.75 1 1.5 2.5 3 4.5

9138 5093 3091 4761 2352 2755 2200 1947

7502 4283 2698 4199 2060 2297 1880 1752

1636 810 393 562 292 458 320 195

17.9 15.9 12.7 11.8 12.4 16.6 14.5 10.0

0 0.25 0.75 1 1.5 2.5 3 4.5

3000 3000 3000 3000 2147 1500 1500 1500

2371 1896 2266 2010 1652 1234 1382 1353

629 1104 734 990 495 266 118 147

0 0.25 0.75 1 1.5 2.5 3 4.5

7954 3467 3312 5934 4781 1967 2164 1901

6996 2978 2357 3007 3942 1706 1863 1715

958 489 955 2927 839 261 301 186

Lab 3

Lab 4

Rejected cells %

DC

% RD

dic/cell

SE

Distribution of dicentrics

0

1

3 12 86 132 25 168 335 326

99.5 90.4 65.2 54.8 43.2 28.2 20.6 8.9

0.000 0.004 0.025 0.045 0.078 0.198 0.216 0.620

0.000 0.001 0.003 0.004 0.016 0.015 0.012 0.034

14,004 2892 3350 2831 297 695 1248 289

3 12 86 130 23 140 269 169

673 472 266 520 304 571 611 1019

12 26 71 153 136 344 436 842

98.2 94.5 73.3 70.6 55.3 39.8 28.6 17.4

0.002 0.006 0.026 0.036 0.066 0.150 0.232 0.481

0.000 0.001 0.003 0.003 0.006 0.008 0.011 0.017

7490 4257 2630 4050 1934 1986 1507 1107

12 26 65 145 118 281 318 482

21.0 36.8 24.5 33.0 23.1 17.7 7.9 9.8

78 126 181 163 213 209 539 860

1 10 49 60 114 138 392 744

98.7 92.1 72.9 63.2 46.5 34.0 27.3 13.5

0.000 0.005 0.022 0.030 0.069 0.112 0.284 0.550

0.000 0.002 0.003 0.004 0.006 0.010 0.014 0.020

2370 1886 2217 1952 1544 1101 1048 815

1 10 49 56 102 128 282 374

2 6 5 47 127

12.0 14.1 28.8 49.3 17.5 13.3 13.9 9.8

307 253 183 262 604 435 646 1188

13 29 54 114 311 300 474 1049

95.8 88.5 70.5 56.5 48.5 31.0 26.6 11.7

0.002 0.010 0.023 0.038 0.079 0.176 0.254 0.612

0.001 0.002 0.003 0.004 0.004 0.010 0.012 0.019

6983 2950 2304 2896 3643 1442 1457 949

13 27 52 108 287 229 347 547

1 1 3 12 34 53 165

2

1 1 11 30 49

3 4 6 28 49 134

3

2 2 17

2 1 4 26

4 32

1 4 46

Var(x)/x

u test

2

1.00 1.00 0.98 0.97 1.01 1.01 1.00 1.07

−0.01 −0.15 −1.03 −1.12 0.07 0.11 −0.02 1.13

1 2 2

1.00 0.99 1.06 1.02 1.11 1.07 1.10 1.09

−0.09 −0.28 2.17 0.74 3.57 2.23 3.18 2.60

1 5

1.00 1.00 0.98 1.04 1.04 0.96 1.05 1.13

– −0.15 −0.72 1.19 1.06 −0.96 1.28 3.41

1 6

1.00 1.06 1.01 1.02 1.00 1.07 1.09 1.07

−0.11 2.34 0.50 0.59 −0.07 2.09 2.69 2.15

4

5

0

1 2

6

1

H. Romm et al. / Mutation Research xxx (2013) xxx–xxx

Captured images

ARTICLE IN PRESS

Dose (Gy)

Lab 2

Rejected cells

DA

Lab

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Please cite this article in press as: H. Romm, et al., Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents, Mutat. Res.: Genet. Toxicol. Environ. Mutagen. (2013), http://dx.doi.org/10.1016/j.mrgentox.2013.05.013

Table 3 Number of analyzed images and observed aberration frequency of automatically detected dicentrics when the IRSN-Classifier was applied. DA: automatically detected dicentrics; DC: confirmed dicentrics; RD: rejected dicentric candidates.

5

Lab 5

Lab 6

Scored cells

Rejected cells

Rejected cells %

DA

DC

% RD

dic/cell

SE

Distribution of dicentrics

0

1

0 0.25 0.75 1 1.5 2.5 3 5

31,469 7733 5229 4928 439 1168 2509 757

19,222 4075 4011 3590 336 952 2055 635

12,247 3658 1218 1338 103 216 454 122

38.9 47.3 23.3 27.2 23.5 18.5 18.1 16.1

757 200 281 342 48 254 602 426

6 31 128 171 29 212 523 386

99.2 84.5 54.4 50.0 39.6 16.5 13.1 9.4

0.000 0.008 0.032 0.048 0.086 0.223 0.255 0.608

0.000 0.001 0.003 0.004 0.016 0.015 0.011 0.031

19,216 4044 3885 3421 309 765 1610 348

6 31 124 167 25 165 378 206

0 0.25 0.75 1 1.5 2.5 3 4.5

3000 3000 3000 3000 2147 1500 1500 1500

2592 2193 2595 2287 1811 1327 1438 1396

408 807 405 713 336 173 62 104

13.6 26.9 13.5 23.8 15.6 11.5 4.1 6.9

71 108 204 161 192 191 500 743

1 8 46 58 102 140 424 680

98.6 92.6 77.5 64.0 46.9 26.7 15.2 8.5

0.000 0.004 0.018 0.025 0.056 0.106 0.295 0.487

0.000 0.001 0.003 0.003 0.006 0.009 0.014 0.019

2591 2185 2550 2231 1712 1196 1070 895

1 8 44 54 96 123 320 360

0 0.25 0.75 1 1.5 2.5 3 4.5

10,700 7181 5319 4687 4228 3851 4476 1604

7048 4537 4059 3135 3088 2394 2718 956

3652 2644 1260 1552 1140 1457 1758 648

34.1 36.8 23.7 33.1 27.0 37.8 39.3 40.4

600 322 333 321 342 376 766 431

6 15 97 104 181 262 699 412

99.0 95.3 70.9 67.6 47.1 30.3 8.7 4.4

0.001 0.003 0.024 0.033 0.059 0.109 0.257 0.431

0.000 0.001 0.002 0.003 0.004 0.007 0.010 0.021

7042 4522 3964 3033 2911 2146 2117 623

6 15 93 100 173 234 514 266

0 0.25 0.75 1 1.5 2.5 3 4.5

11,396 3769 3625 3520 4587 2380 2794 1896

8738 3022 3046 2974 3471 1864 1940 1568

2658 747 579 546 1116 516 854 328

23.3 19.8 16.0 15.5 24.3 21.7 30.6 17.3

852 334 351 326 519 635 813 1200

2 51 89 112 358 529 665 1070

99.8 84.7 74.6 65.6 31.0 16.7 18.2 10.8

0.000 0.017 0.029 0.038 0.103 0.284 0.343 0.682

0.000 0.002 0.003 0.004 0.005 0.012 0.013 0.021

8736 2971 2959 2864 3132 1404 1398 840

2 51 85 108 321 395 438 475

2

3

2 2 2 19 57 66

1 2 3 7 41 110

2 2 4 14 76 57

2 2 17 61 88 178

3 9 12

1 6 25

11 8

1 4 13 63

Var(x)/x

u test

1 3

1.00 0.99 1.00 0.98 1.05 1.04 1.09 1.02

−0.03 −0.34 −0.02 −1.02 0.72 0.93 2.89 0.28

1 5

1.00 1.00 1.03 1.04 1.00 1.04 1.01 1.18

– −0.11 0.95 1.50 0.09 0.99 0.33 4.64

2

1.00 1.00 1.02 1.01 0.99 1.00 1.06 1.02

−0.05 −0.15 0.80 0.22 −0.56 −0.07 2.03 0.47

3 11

1.00 0.98 1.02 1.00 1.01 0.99 1.09 1.16

−0.01 −0.65 0.63 −0.06 0.37 −0.22 2.93 4.36

4

5

6

1

0

1

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Lab 3

Captured images

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Table 4 Number of analyzed images and observed aberration frequency of automatically detected dicentrics when the BfS-Classifier was applied. DA: automatically detected dicentrics; DC: confirmed dicentrics; RD: rejected dicentric candidates.

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Table 5 Number of analyzed images and observed aberration frequency of automatically detected dicentrics when the BfS-First-Classifier was applied. DA: automatically detected dicentrics; DC: confirmed dicentrics; RD: rejected dicentric candidates. Lab

Lab 1

Dose (Gy)

0 0.25 0.75 1 1.5 2.5 3 5

Captured Scored images cells

31,469 7733 5229 4928 439 1168 2509 757

17,773 4208 4084 3714 365 973 2065 663

Rejected cells

13,696 3525 1145 1214 74 195 444 94

Rejected cells %

43.5 45.6 21.9 24.6 16.9 16.7 17.7 12.4

DA

2062 628 623 790 93 357 802 522

DC

5 34 125 172 32 192 518 413

% RD

99.8 94.6 79.9 78.2 65.6 46.2 35.4 20.9

although this difference is not significant (p = 0.854). Each laboratory captured enough cells to set up a clear dose effect relationship (10,000–30,000 analyzed images per curve). The mean number of observed objects and chromosomes in the labs were in the range of 42–46 (lab 5: 33.3) and 31–32 (lab 5: 23.3) per cell. In all four labs the distribution of dicentrics followed the Poisson distribution. In Table 5 the results of the automatic scoring with the BfSFirst-Class are given in order to illustrate the progress with the new classifiers. For a direct comparison, the dataset of lab 1 was used again. The main difference was in the number of detected dicentric candidates which increased by a factor of two (5877 vs. 2910, p < 0.05). The number of confirmed dicentrics did not change (1486 vs 1491). In consequence in this dataset many more FP had to be rejected using the BfS-First-Class (74.6%) than using the newer one (48.9%). In total, about 34,000 images were analyzed, showing a clear dose effect relationship and the dicentrics again followed a Poisson distribution. In Fig. 3 and Table 6, the resulting dose effect curves and their parameters are shown. In general, the dose effect relationship of dicentric chromosomes can be described by a linear quadratic curve Y = C + alpha * D and beta * D2 , where Y= yield of dicentrics, D = dose (Gy), C = spontaneous frequency, alpha = linear coefficient and beta = quadratic coefficient. For the pooled data, dose was shown to be the only significant experimental factor which contributed to the shape of the curve (p < 0.001). There was no statistically significant effect of classifier (p > 0.134) and no significant differences between labs (p > 0.105). The only exception to this is the ANOVA p value of 0.015 for the beta coefficient, which is suggestive of a significant difference between the labs’ fitted quadratic coefficients. However, overall, there were no significant differences between the curves, which indicate that it is statistically valid to pool the data collected in each laboratory to fit a joint calibration curve. Pooling the data of all laboratories and fitting a joint curve, there were no significant differences between the coefficients of the individual calibration curves and those of

dic/cell

0.000 0.008 0.031 0.046 0.088 0.197 0.251 0.623

SE

Distribution of dicentrics

0.000 0.001 0.003 0.004 0.015 0.014 0.011 0.031

0

1

17,768 4174 3962 3545 335 800 1614 357

5 34 119 166 28 157 389 215

2

3 3 2 13 57 77

3

3 5 12

4

2

5

Var(x)/x

u test

1.00 0.99 1.02 0.99 1.04 1.03 1.03 0.98

−0.02 −0.37 0.80 −0.48 0.55 0.73 0.89 −0.29

6

Table 7 With automatic scoring the analysis of 1 sample in triage mode (150 cells) can be performed in 20 min and requires only minimum human interaction. Metaphase finding: Auto capturing of 150 cells: Automatic scoring: Evaluation of dic candidates:

5 min 12 min 1 min 2 min

Total:

20 min

the combined curve. Furthermore, there is very good agreement between data points when the yields and doses are compared for 0.5, 2 and 4 Gy for the individual curves of the participants compared to the pooled curve. Table 7 4. Discussion The development of software for the automatic detection of dicentric chromosomes started in the 1980s and the basic structure of the algorithms emerged over the next few years [28]. The aim of the first scoring systems was to increase the throughput of samples in the frame of biological dosimetry and the systems were extensively tested for practical use [29]. At this time the first dose effect curve of dicentric chromosomes was established using the software by MetaSystems [27]. However the scoring criteria were very restrictive at this time, for instance, generally only complete cells with 46 centromeres were accepted for analysis. The software was not able to fulfill these requirements because there are naturally many variations in the size and position of the chromosomes in metaphase cells. Chromosomes which are very close or overlapping were not included in further analyses and consequently, the number of detected chromosomes per image depended heavily on the quality of metaphase spreads. Human scorers are much better equipped to deal with such situations and this is one reason

Table 6 The dose effect curves after semi-automatic scoring using different classifiers. Laboratory

Classifier

C

± SE

alpha

±SE

beta

±SE

Chi2

DF

p

Lab 1 Lab 2 Lab 3 Lab 4

I-55 I-100 I-55 I-100

0.0002 0.0016 0.0006 0.0022

0.0002 0.0004 0.0014 0.0008

0.0189 0.0141 0.0066 0.0104

0.0044 0.0027 0.0086 0.0046

0.0203 0.0202 0.0249 0.0263

0.0021 0.0011 0.0036 0.0019

10.71 3.84 38.55 12.05

5 5 5 5

0.057 0.572 0.000 0.034

Lab 1 Lab 3 Lab 5 Lab 6

B-55 B-55 B-55 B-55

0.0003 0.0005 0.0008 0.0003

0.0001 0.0014 0.0011 0.0006

0.0279 0.0030 0.0104 0.0251

0.0031 0.0089 0.0083 0.0107

0.0195 0.0243 0.0200 0.0292

0.0014 0.0040 0.0038 0.0045

5.46 54.04 59.80 57.69

5 5 5 5

0.363 0.000 0.000 0.000

Lab 1

OLD

0.0003

0.0001

0.0263

0.0029

0.0196

0.0013

0.65

5

0.986

0.0006

0.0002

0.0166

0.0023

0.0227

0.0010

36.45

6

0.490

Pooled

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why humans find more dicentrics than automated systems. Another difference between human and automated scoring is in the recognition of which chromosomes belong to which cell. If chromosomes from two or more adjacent cells are captured in an image, then the software will assume they belong to a single cell and they will be analyzed as such. It is for this reason that the parameter for the maximal number of 55 (or 100) objects per image is necessary, as this limits the number of images with chromosomes from more than one cell. 55 objects were choosen to detect all complete cells with 46 chromosomes and some cell nuclei in the neighborhood of the image. Furthermore the software is not able to distinguish between first and second mitosis, therefore images with second mitoses after Fluorescence plus Giemsa staining have to be deleted manually. Development of the automated scoring methods has continued over time, as the slide scanning systems have been applied for several different clinical and research purposes [30]. As a result of collaborative efforts to prepare the system for dicentric scoring following a large scale accident, the scoring criteria have been revised. With a reduced number of 20 to 50 scored cells recommended to be scored per subject in triage mode, very high throughput of samples became possible. However the inevitable consequence of increased speed of analysis is a loss of accuracy. Here the question again arose, as to whether the automatic dicentric scoring module can also be used for individual triage analyses in a large population. The DCScore software analysis was first time successfully used for biological dosimetry in triage mode during the Dakar accident in 2006 [24]. In total 63 victims were investigated and in parallel a respective dose effect curve for gamma rays was established. It was demonstrated that the dose estimations using the automatic scoring were more accurate (because the number of cells scored was higher) and were available faster than those based on manual scoring. An overview of the main characteristics of the currently existing classifiers of the DCScore software is given in Table 2. The data obtained in this paper demonstrate that the two new classifiers show a better performance with regard to image analysis than the original classifier: The number of correctly identified dicentrics has increased and the number of false positive dicentrics has declined by a factor of two, which clearly reduces the workload and allows results to be obtained faster. Here the technological developments related to image processing, analysis and storage have clearly made a difference. Nevertheless, it is not possible to run the dicentric assay in a fully automated manner, because there is a higher number of automatically detected dicentric candidates (DA) at lower doses, which are in fact FP (not true dicentric chromosomes) and which therefore need to be manually rejected. TP are correctly detected dicentric chromosomes, which are confirmed as dicentrics (DC) by human scorers. The yield of DC/cell increases with increasing dose and in consequence the ratio of DC/DA also increases. In other words, there was a higher ratio of FP/TP at lower doses. At doses above 2 Gy the proportion becomes less than 20% FP/TP. Between the two new classifiers there was no difference in this respect (Fig. 4, p = 0.85). Pooling all the data, the number of rejected FP showed some variation between the laboratory preparations but the total number was between 40 and 90 FP in 1000 cells for both classifiers. In both cases, the rate is much lower than for the BfS-First-Classifier, for which there were 130 FP in 1000 cells. New calibration curves have been established with the new classifiers. It is well known that the formation and (manual) detection of dicentric chromosomes is governed by the Poisson distribution. Therefore, initial statistical analysis was carried out in order to test whether this is also true for semi automated scoring, using the various different classifier settings. This was found to be the case, and furthermore, the method can be used for the satisfactory detection

Fig. 3. Nine established gamma ray dose effect curves of semi-automatic dicentric analysis using three different classifiers for dicentric detection in 6 laboratories. The curves are not significantly different and therefore a combined curve of the pooled data is also given.

Fig. 4. Independent of the classifier (BfS-Class.or IRSN-Class), the ratio of false positives (FP) to confirmed dicentrics (DC) decreases with increasing dose.

of partial body exposures [31]. In addition to the above, no statistically significant differences were observed between the classifiers or laboratories. The results demonstrate that a common curve can be used for dicentrics scored using either classifier. Comparing the manual [23] and semi-automatic dose effect relationships of the yield of dicentrics for pooled data from all the laboratories participating in MULTIBIODOSE (Fig. 5), human scorers alone detect approximately double the number of dicentrics than are detected by semi-automatic scoring. This is in agreement with earlier data [24,27]. The need to improve the automatic detection of dicentric chromosomes has been driven by the need for high sample throughput in the case of a large scale radiation incident. In triage mode a human scorer can analyze 50 cells in approximately 60 min [11,24,32], depending on the dose and metaphase quality. With a dicentric detection efficiency of about 50% and a cell rejection rate of about 33%, 150 cells scored in automatic mode should give a similar amount of information. A much smaller amount of human interaction is required for semi-automated – an experienced human scorer has only to analyze the dicentric candidates. The results can therefore be produced in a much shorter time–approximately 20 min per sample in total. Around 70 samples can be analyzed with one metaphase scoring system in one day. Using several scoring systems, one scorer can increase his output

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Fig. 5. Relationship between pooled gamma dose effect curves after manual and automatic scoring of dicentric chromosomes.

by a factor of 30 (2 min scoring of dicentric candidates instead of analysing 50 cells in 1 h). Furthermore, operators can be trained to carry out semi-automated scoring in a few days. The results obtained so far in the framework of the MULTIBIODOSE project, for simulated radiation accidents [24] and in recently performed intercomparisons [14,23] are very encouraging, even when different radiation scenarios such as acute whole body or partial body exposure [31] have been tested. Further investigation and validation of semi-automated scoring is necessary to establish the method as a routine tool for biological dosimetry. 5. Conclusions Many years of inter-laboratory comparisons using the conventional (manual) dicentric chromosome assay have demonstrated that differences in culturing, slide preparation and scoring of cells are sources of variation between labs. Therefore it has been recommended that each laboratory should establish its own dose effect curve [13]. In this study, whole blood samples were sent to different laboratories and each lab followed their own standard protocols and successfully established a calibration curve for semi-automated scoring. Despite all the experimental variables, including the two different colcemid treatments and three different classifiers used, the dose effect curves obtained in this work were not significantly different from each other. Thus a common dose effect curve could be established which will further promote networking between labs and which potentially increases the emergency response capacity of the joint European laboratories. Overall, the results of this work show that the semi-automated method of dicentric scoring can be a reliable tool in the case of a large scale radiation accident, enabling high throughput screening of samples and fast dose assessment. References [1] E.A. Ainsbury, E. Bakhanova, J.F. Barquinero, M. Brai, V. Chumak, V. Correcher, F. Darroudi, P. Fattibene, G. Gruel, I. Guclu, S. Horn, A. Jaworska, U. Kulka, C. Lindholm, D. Lloyd, A. Longo, M. Marrale, O. Monteiro Gil, U. Oestreicher, J. Pajic, B. Rakic, H. Romm, F. Trompier, I. Veronese, P. Voisin, A. Vral, C.A. Whitehouse, A. Wieser, C. Woda, A. Wojcik, K. Rothkamm, Review of retrospective dosimetry techniques for external ionising radiation exposures, Radiat. Prot. Dosim. 147 (2011) 573–592. [2] M.A. Yoshida, I. Hayata, H. Tateno, K. Tanaka, S. Sonta, S. Kodama, Y. Kodama, M.S. Sasaki, The chromosome network for biodosimetry in Japan, Radiat Meas. 42 (2007) 1125–1127.

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