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Purpose: This interlaboratory comparison was conducted to evaluate the performance of the Latin-American Biodosimetry Network (LBDNet) in analyzing digitized images for scoring dicentric chromosomes from in vitro irradiated blood samples. The exercise also assessed the use of weighted robust algorithms to compensate the uneven expertise among the participating laboratories., Methods: Three sets of coded images obtained through the dicentric chromosome assay from blood samples irradiated at 1.5 Gy (sample A) and 4 Gy (sample B), as well as a non-irradiated whole blood sample (sample C), were shared among LBDNet laboratories. The images were captured using the Metafer4 platform coupled with the AutoCapt module. The laboratories were requested to perform triage scoring, conventional scoring, and dose estimation. The dose estimation was carried out using either their laboratory calibration curve or a common calibration curve. A comparative statistical analysis was conducted using a weighted robust Hampel algorithm and z score to compensate for uneven expertise in dicentric analysis and dose assessment among all laboratories., Results: Out of twelve laboratories, one had unsatisfactory estimated doses at 0 Gy, and two had unsatisfactory estimated doses at 1.5 Gy when using their own calibration curve and triage scoring mode. However, all doses were satisfactory at 4 Gy. Six laboratories had estimated doses within 95% uncertainty limits at 0 Gy, seven at 1.5 Gy, and four at 4 Gy. While the mean dose for sample C was significantly biased using robust algorithms, applying weights to compensate for the laboratory's analysis expertise reduced the bias by half. The bias from delivered doses was only notable for sample C. Using the common calibration curve for dose estimation reduced the standard deviation ( s *) estimated by robust methods for all three samples., Conclusions: The results underscore the significance of performing interlaboratory comparison exercises that involve digitized and electronically transmitted images, even when analyzing non-irradiated samples. In situations where the participating laboratories possess different levels of proficiency, it may prove essential to employ weighted robust algorithms to achieve precise outcomes.

Purpose: To test the performance of different algorithms that can be used in inter-laboratory comparisons based on dicentric chromosome analysis, and to evaluate the impact of considering a priori values different to calculate individual laboratory performance based on the ionizing radiation dose estimation., Methods: Mean and standard deviation estimations in inter-laboratory comparisons are tested on simulated data and data from previously published inter-laboratory comparisons using three robust algorithms, Algorithm A, Algorithm B and Q/Hampel, all programmed in R-project language and implemented in a Shiny application. The simulated data were generated assuming three different probabilities to contaminate inter-laboratory comparisons samples with atypical dose values. Comparison between different algorithms was also done using published exercises where blood samples were irradiated at 0 and 0.7 Gy that represent a challenge for the assessment of an inter-laboratory comparison., Results: The best performance was obtained with the Q/Hampel algorithm for the estimation of the dose mean and with the Algorithm B for the estimation of the dose standard deviation under the conditions tested in the simulations. The Q/Hampel algorithm showed the best performance when non-irradiated samples were evaluated and there was a high proportion of identical values. The presence identical values cause the Algorithm B to fail. Real examples illustrating the need to consider standard deviation priors, and the need to use algorithms resistant to a high proportion of identical values are presented., Conclusions: Q/Hampel algorithm is a serious candidate to estimate the dose mean in the inter-laboratory comparisons, and to estimate both parameters when the proportion of identical values equals or higher than the half of the results. When the proportion of identical values is less than the half of the results, the Algorithm B should be considered as a candidate to estimate the standard deviation in the inter-laboratory comparisons with small number of laboratories. We remark that special attention is needed to establish prior definitions of standard deviation in the assessment of inter-laboratory dicentric assay comparisons.

Purpose: To present the impact in coverage of different methods for Poisson confidence intervals and the impact in dose coverage of different uncertainty factors. A detailed explanation of the uncertainty sources in the Bayesian method is also presented. Materials and methods: The exact coverage of uncertainty Poisson confidence intervals and the dose uncertainty interval coverage were performed by simulations using R-based scripts. Results: The Poisson exact calibration interval via the Modified Crow and Gardner method resulted in coverage quite close to the nominal level of confidence; additionally, the method retains the shortest property of Crow and Gardner, and gains the property of a lower limit strictly increasing in the mean of dicentrics. The unlimited simultaneous calibration interval seems to be the method of choice to preserve the coverage at 95% under parametric and nonparametric conditions but is a conservative method. When samples came from a Poisson distribution, the ISO propagation of errors and Bayesian approaches seem to be the closest to the 95% coverage. Conclusions: The Modified Crow and Gardner method should be preferred over the Garwood method for Poisson exact confidence intervals. The unlimited simultaneous calibration interval did not lose its property to preserve the coverage at 95% applying a regression coverage factor of value 2.02 at the point of doses studied in the simulation.

Purpose: To present Poisson exact goodness-of-fit tests as alternatives and complements to the asymptotic u-test, which is the most widely used in cytogenetic biodosimetry, to decide whether a sample of chromosomal aberrations in blood cells comes from an homogeneous or inhomogeneous exposure., Materials and Methods: Three Poisson exact goodness-of-fit test from the literature are introduced and implemented in the R environment. A Shiny R Studio application, named GOF Poisson, has been updated for the purpose of giving support to this work. The three exact tests and the u-test are applied in chromosomal aberration data from clinical and accidental radiation exposure patients., Results: It is observed how the u-test is not an appropriate approximation in small samples with small yield of chromosomal aberrations. Tools are provided to compute the three exact tests, which is not as trivial as the implementation of the u-test., Conclusions: Poisson exact goodness-of-fit tests should be considered jointly to the u-test for detecting inhomogeneous exposures in the cytogenetic biodosimetry practice.

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.