Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay

References

• Ainsbury EA, Higueras M, Puig P, Einbeck J, Samaga D, Barquinero JF, Barrios L, Brzozowska B, Fattibene P, Gregoire E, et al. 2017. Uncertainty of fast biological radiation dose assessment for emergency response scenarios. Int J Radiat Biol. 93(1):127–135. doi:10.1080/09553002.2016.1227106
   PubMed Web of Science ®Google Scholar
• Boice JD.Jr. 2015. Radiation epidemiology and recent paediatric computed tomography studies. Ann ICRP. 44(1 Suppl):236–248. doi:10.1177/0146645315575877
   PubMedGoogle Scholar
• Brenner DJ, Hall EJ. 2012. Cancer risks from CT scans: now we have data, what next? Radiology. 265(2):330–331. doi:10.1148/radiol.12121248
   PubMed Web of Science ®Google Scholar
• Crow EL, Gardner RS. 1959. Confidence intervals for the expectation of a Poisson variable. Biometrika. 46(3–4):441–453. doi:10.2307/2333541
   Web of Science ®Google Scholar
• Endesfelder D, Kulka U, Einbeck J, Oestreicher U. 2020. Improving the accuracy of dose estimates from automatically scored dicentric chromosomes by accounting for chromosome number. Int J Radiat Biol. 96(12):1571–1584. doi:10.1080/09553002.2020.1829152
   Google Scholar
• Guilherme RS, Ayres Meloni VF, Kim CA, Pellegrino R, Takeno SS, Spinner NB, Conlin LK, Christofolini DM, Kulikowski LD, Melaragno MI. 2011. Mechanisms of ring chromosome formation, ring instability and clinical consequences. BMC Med Genet. 12:171. doi:10.1186/1471-2350-12-171
   Google Scholar
• Huber D, Voithenberg LV, Kaigala GV. 2018. Fluorescence in situ hybridization (FISH): History, limitations and what to expect from microscale FISH? Micro Nano Eng. 1:15–24. doi:10.1016/j.mne.2018.10.006
   Google Scholar
• IAEA. 2011. Cytogenetic dosimetry: applications in preparedness for and response to radiation emergencies. Vienna, Austria: EPR-Biodosimetry, Internaional Atomic Energy Agency.
   Google Scholar
• Jeong SK, Oh SJ, Kim SH, Jang S, Kang YR, Kim HJ, Kye YU, Lee SH, Lee CG, Park MT, et al. 2023. Dicentric chromosome assay using a deep learning based automated system. Sci Rep. 12(1):22097. doi:10.1038/s41598-022-25856-1
   Google Scholar
• Kye YU, Kim HJ, Lee JE, Seo YJ, Kim JK, Jo WS, Lee DY, Kang YR. 2021. Measurement of absorbed dose rate in water phantom maintained at body temperature by 60Co irradiator – comparison of experimental results and Monte Carlo simulation. Nucl Technol Radiat Prot. 36(3):289–293. doi:10.2298/NTRP2103289K
   Web of Science ®Google Scholar
• Li Y, Knoll JH, Wilkins RC, Flegal FN, Rogan PK. 2016. Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing. Microsc Res Tech. 79(5):393–402. doi:10.1002/jemt.22642
   PubMed Web of Science ®Google Scholar
• Li Y, Shirley BC, Wilkins R, Norton F, Knoll JH, Rogan PK. 2019. Radiation dose estimation by completely automated interpretation of the dicentric chromosome assay. Radiat Prot Dosimetry. 186(1):42–47. doi:10.1093/rpd/ncy282
   PubMed Web of Science ®Google Scholar
• Liu J, Li Y, Wilkins R, Flegal F, Knoll JM, Rogan PK. 2017. Accurate cytogenetic biodosimetry through automated dicentric chromosome curation and metaphase cell. F1000Res. 6:1396. doi:10.12688/f1000research.12226.1
   PubMedGoogle Scholar
• Merkle W. 1983. Statistical methods in regression and calibration analysis of chromosome aberration data. Radiat Environ Biophys. 21(3):217–233. doi:10.1007/BF01323412
   PubMed Web of Science ®Google Scholar
• Oestreicher U, Endesfelder D, Gomolka M, Kesminiene A, Lang P, Lindholm C, Rößler U, Samaga D, Kulka U. 2018. Automated scoring of dicentric chromosomes differentiates increased radiation sensitivity of young children after low dose CT exposure in vitro. Int J Radiat Biol. 94(11):1017–1026. doi:10.1080/09553002.2018.1503429
   Google Scholar
• Preston DL, Ron E, Tokuoka S, Funamoto S, Nishi N, Soda M, Mabuchi K, Kodama K. 2007. Solid cancer incidence in atomic bomb survivors: 1958–1998. Radiat Res. 168(1):1–64. doi:10.1667/RR0763.1
   PubMed Web of Science ®Google Scholar
• Rogan PK, Mucaki EJ, Shirley BC, Li Y, Wilkins RC, Norton F, Sevriukova O, Pham ND, Waller E, Knoll JHM, et al. 2021. Automated cytogenetic biodosimetry at population-scale. Radiation. 1(2):79–94. doi:10.3390/radiation1020008
   Google Scholar
• Romm H, Ainsbury E, Barnard S, Barrios L, Barquinero JF, Beinke C, Deperas M, Gregoire E, Koivistoinen A, Lindholm C, et al. 2013. Automatic scoring of dicentric chromosomes as a tool in large scale radiation accidents. Mutat Res. 756(1–2):174–183. doi:10.1016/j.mrgentox.2013.05.013
   PubMed Web of Science ®Google Scholar
• Ryan TL, Escalona MB, Smith TL, Albanese J, Iddins CJ, Balajee AS. 2019. Optimization and validation of automated dicentric chromosome analysis for radiological/nuclear triage applications. Mutat Res Genet Toxicol Environ Mutagen. 847:503087. doi:10.1016/j.mrgentox.2019.503087
   PubMed Web of Science ®Google Scholar
• Shen X, Ma T, Li C, Wen C, Wen Z, Zheng J, Zhou Z. 2023. High-precision automatic identification method for dicentric ­chromosome images using two-stage convolutional neural network. Sci Rep. 13(1):2124. doi:10.1038/s41598-023-28456-9
   PubMed Web of Science ®Google Scholar
• Shen X, Qi Y, Ma T, Zhou Z. 2019. A dicentric chromosome identification method based on clustering and watershed algorithm. Sci Rep. 9(1):2285. doi:10.1038/s41598-019-38614-7
   PubMedGoogle Scholar
• Shirley BC, Li Y, Knoll JH, Rogan PK. 2017. Expedited radiation biodosimetry by automated dicentric chromosome identification (ADCI) and dose estimation. J Vis Exp. 127:56245. doi:10.3791/56245
   Google Scholar
• Tan M, Le QV. 2019. EfficientNet: rethinking model scaling for convolutional neural networks. arXiv 1905.11946v5. doi:10.48550/arXiv.1905.11946
   Google Scholar
• Trompier F, Burbidge C, Bassinet C, Baumann M, Bortolin E, Angelis CD, Eakins J, Monaca SD, Fattibene P, Quattrini MC, et al. 2017. Overview of physical dosimetry methods for triage application integrated in the new European network RENEB. Int J Radiat Biol. 93(1):65–74. doi:10.1080/09553002.2016.1221545
   PubMed Web of Science ®Google Scholar
• Wadhwa AS, Tyagi N, Chowdhury PR. 2022. Deep learning based ­automatic detection of dicentric chromosome. arXiv 2204.08029. doi:10.48550/arXiv.2204.08029
   Google Scholar
• Wang CY, Bochkovskiy A, Liao HM. 2022. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada. p. 7464–7475. doi:10.1109/CVPR52729.2023.00721
   Google Scholar