EPJ Nuclear Sci. Technol.
Volume 9, 2023
Euratom Research and Training in 2022: the Awards collection
Article Number 22
Number of page(s) 12
Section Part 1: Safety research and training of reactor systems
Published online 24 May 2023
  1. Committee on the Biological Effects of Ionizing Radiation, Health Risks from Exposure to Low Levels of Ionizing Radiation (US National Research Council, Washington, DC, 2006) [Google Scholar]
  2. S. Akiba, S. Mizuno, The third analysis of cancer mortality among Japanese nuclear workers, 1991–2002: estimation of excess relative risk per radiation dose, J. Radiol. Prot. 32, 73 (2012) [CrossRef] [Google Scholar]
  3. A.V. Brenner et al., Comparison of all solid cancer mortality and incidence dose-response in the life span study of atomic bomb survivors, 1958–2009, Radiat. Res. 197, 491 (2022) [CrossRef] [Google Scholar]
  4. M.S. Pearce et al., Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study, Lancet 380, 499 (2012) [CrossRef] [PubMed] [Google Scholar]
  5. J.D. Mathews et al., Cancer risk in 680 000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians, BMJ 346, f2360 (2013) [CrossRef] [PubMed] [Google Scholar]
  6. W.-Y. Huang et al., Paediatric head CT scan and subsequent risk of malignancy and benign brain tumour: a nation-wide population-based cohort study, Br. J. Cancer. 110, 2354 (2014) [CrossRef] [Google Scholar]
  7. N. Journy et al., Childhood CT scans and cancer risk: impact of predisposing factors for cancer on the risk estimates, J. Radiol. Prot. 36, N1-7 (2016) [CrossRef] [Google Scholar]
  8. R. Pokora et al., Computed tomography in Germany, Dtsch Arztebl Int. 113, 721 (2016) [Google Scholar]
  9. M.-O. Bernier et al., Cohort profile: the EPI-CT study: a European pooled epidemiological study to quantify the risk of radiation-induced cancer from paediatric CT, Int. J. Epidemiol. 48, 379 (2019) [CrossRef] [PubMed] [Google Scholar]
  10. J.M. Meulepas et al., Radiation exposure from pediatric CT scans and subsequent cancer risk in the Netherlands, J. Natl. Cancer Inst. 111, 256 (2019) [CrossRef] [Google Scholar]
  11. A. Foucault et al., Childhood cancer risks estimates following CT scans: an update of the French CT cohort study, Eur. Radiol. 32, 5491 (2022) [CrossRef] [Google Scholar]
  12. M. Hauptmann et al., Brain cancer after radiation exposure from CT examinations of children and young adults: results from the EPI-CT cohort study, Lancet Oncol. S1470–2045, 00655 (2022). [Google Scholar]
  13. L.S. Constine et al., Pediatric normal tissue effects in the clinic (PENTEC): an international collaboration to analyse normal tissue radiation dose-volume response relationships for paediatric cancer patients, Clin. Oncol. (R. Coll. Radiol.) 31, 199 (2019) [CrossRef] [Google Scholar]
  14. Y. Wang et al., Cohort profile: risk and risk factors for female breast cancer after treatment for childhood and adolescent cancer: an internationally pooled cohort, BMJ Open. 12, e065910 (2022) [CrossRef] [Google Scholar]
  15. H. Dolk, M. Loane, E. Garne, European Surveillance of Congenital Anomalies (EUROCAT) Working Group, Congenital heart defects in Europe: prevalence and perinatal mortality, 2000 to 2005, Circulation 123, 841 (2011) [CrossRef] [Google Scholar]
  16. J.F. Winther et al., Childhood cancer survivor cohorts in Europe, Acta Oncol. 54, 655 (2015) [CrossRef] [Google Scholar]
  17. R. Miralbell, A. Lomax, L. Cella, U. Schneider, Potential reduction of the incidence of radiation-induced second cancers by using proton beams in the treatment of pediatric tumors, Int. J. Radiat. Oncol. Biol. Phys. 54, 824, (2002) [CrossRef] [Google Scholar]
  18. J.D. Fontenot, A.K. Lee, W.D. Newhauser, Risk of secondary malignant neoplasms from proton therapy and intensity-modulated X-ray therapy for early-stage prostate cancer, Int. J. Radiat. Oncol. Biol. Phys. 74, 616 (2009) [CrossRef] [Google Scholar]
  19. M. Steneker, A. Lomax, U. Schneider, Intensity modulated photon and proton therapy for the treatment of head and neck tumors, Radiother. Oncol. 80, 263 (2006) [CrossRef] [Google Scholar]
  20. R.W. Harbron et al., Patient radiation doses in paediatric interventional cardiology procedures: a review, J. Radiol. Prot. 36, R131 (2016) [CrossRef] [Google Scholar]
  21. A. Bottomley, The cancer patient and quality of life, Oncologist 7, 120 (2002) [CrossRef] [Google Scholar]
  22. H. Baysson et al., Risk of cancer associated with cardiac catheterization procedures during childhood: a cohort study in France, BMC Public Health 13, 266 (2013) [CrossRef] [Google Scholar]
  23. R.W. Harbron et al., Cancer incidence among children and young adults who have undergone X-ray guided cardiac catheterization procedures, Eur. J. Epidemiol. 33, 393 (2018) [CrossRef] [Google Scholar]
  24. R.W. Harbron et al., The HARMONIC project: study design for assessment of cancer risks following cardiac fluoroscopy in childhood, J. Radiol. Prot. 40, 1074 (2020) [Google Scholar]
  25. E. Yakoumakis, H. Kostopoulou, T. Makri, A. Dimitriadis, E. Georgiou, I. Tsalafoutas, Estimation of radiation dose and risk to children undergoing cardiac catheterization for the treatment of a congenital heart disease using Monte Carlo simulations, Pediatr. Radiol. 43, 339 (2013) [CrossRef] [Google Scholar]
  26. R.W. Harbron et al., Radiation doses from fluoroscopically guided cardiac catheterization procedures in children and young adults in the United Kingdom: a multicentre study, Br. J. Radiol. 88, 20140852 (2015) [CrossRef] [Google Scholar]
  27. T.P. Jones, P.C. Brennan, E. Ryan, Cumulative effective and individual organ dose levels in paediatric patients undergoing multiple catheterisations for congenital heart disease, Radiat. Prot. Dosimetry 176, 252 (2017) [Google Scholar]
  28. F. Bray et al., Cancer incidence in five continents: inclusion criteria, highlights from volume X and the global status of cancer registration, Int. J. Cancer 137, 2060 (2015) [CrossRef] [Google Scholar]
  29. S. Cohen et al., Exposure to low-dose ionizing radiation from cardiac procedures and malignancy risk in adults with congenital heart disease, Circulation 137, 1334 (2018) [CrossRef] [Google Scholar]
  30. T. Bjørge, S. Cnattingius, R.T. Lie, S. Tretli, A. Engeland, Cancer risk in children with birth defects and in their families: a population-based cohort study of 5.2 million children from Norway and Sweden, Cancer Epidemiol Biomark, Prev. 17, 500 (2008) [Google Scholar]
  31. Y.-S. Lee et al., The risk of cancer in patients with congenital heart disease: a nationwide population-based cohort study in Taiwan, PLoS One 10, e0116844 (2015) [CrossRef] [Google Scholar]
  32. Z. Mandalenakis et al., Risk of cancer among children and young adults with congenital heart disease compared with healthy controls, JAMA Netw. Open. 2, e196762 (2019) [CrossRef] [Google Scholar]
  33. R.T. Collins et al., Congenital heart disease complexity and childhood cancer risk, Birth Defects Res. 110, 1314 (2018) [CrossRef] [Google Scholar]
  34. H. Hasle, I.H. Clemmensen, M. Mikkelsen, Risks of leukaemia and solid tumours in individuals with Down’s syndrome, Lancet 355, 165 (2000) [CrossRef] [Google Scholar]
  35. A.E. Grulich, M.T. van Leeuwen, M.O. Falster, C.M. Vajdic, Incidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis, Lancet 370, 59 (2007) [CrossRef] [Google Scholar]
  36. R.M. Howell, S.B. Scarboro, P.J. Taddei, S. Krishnan, S.F. Kry, W.D. Newhauser, Methodology for determining doses to in-field, out-of-field and partially in-field organs for late effects studies in photon radiotherapy, Phys. Med. Biol. 55, 7009 (2010) [CrossRef] [Google Scholar]
  37. D. Borrego et al., Organ-specific dose coefficients derived from Monte Carlo simulations for historical (1930s to 1960s) fluoroscopic and radiographic examinations of tuberculosis patients, J. Radiol. Prot. 39, 950 (2019) [CrossRef] [Google Scholar]
  38. C. Lee, D. Lodwick, J. Hurtado, D. Pafundi, J.L. Williams, W.E. Bolch, The UF family of reference hybrid phantoms for computational radiation dosimetry, Phys. Med. Biol. 55, 339 (2010) [CrossRef] [Google Scholar]
  39. D.B. Pelowitz, MCNPX Users Manual Version 2.7.0, Report LA-CP-11-00438 (Los Alamos National Laboratory, Los Alamos, NM, 2011) [Google Scholar]
  40. E. Hofer, How to account for uncertainty due to measurement errors in an uncertainty analysis using Monte Carlo simulation, Health Phys. 95, 277 (2008) [CrossRef] [Google Scholar]
  41. S.L. Simon, F.O. Hoffman, E. Hofer, The two-dimensional Monte Carlo: a new methodologic paradigm for dose reconstruction for epidemiological studies, Radiat. Res. 183, 27 [Google Scholar]
  42. I. Thierry-Chef et al., Dose estimation for the European epidemiological study on pediatric computed tomography (EPI-CT), Radiat. Res. 196, 74 (2021) [CrossRef] [Google Scholar]
  43. L. Toussaint et al., Delineation atlas of the Circle of Willis and the large intracranial arteries for evaluation of doses to neurovascular structures in pediatric brain tumor patients treated with radiation therapy, Acta Oncol. 60, 1392 (2021) [CrossRef] [Google Scholar]
  44. H. Baysson et al., Follow-up of children exposed to ionising radiation from cardiac catheterisation: the Coccinelle study, Radiat. Prot. Dosim. 165, 13 (2015) [CrossRef] [Google Scholar]
  45. K.D. Abalo et al., Exposure to low-dose ionising radiation from cardiac catheterisation and risk of cancer: the COCCINELLE study cohort profile, BMJ Open. 11, e048576 (2021) [CrossRef] [Google Scholar]
  46. M. Rodriguez, J. Sempau, L. Brualla, PRIMO: a graphical environment for the Monte Carlo simulation of Varian and Elekta linacs, Strahlenther Onkol. 189, 881 (2013) [CrossRef] [Google Scholar]
  47. M. De Saint-Hubert et al., Experimental validation of an analytical program and a Monte Carlo simulation for the computation of the far out-of-field dose in external beam photon therapy applied to pediatric patients, Front. Oncol. 12, 1 (2022) [Google Scholar]
  48. P. Hauri, R.A. Hälg, J. Besserer, U. Schneider, A general model for stray dose calculation of static and intensity-modulated photon radiation, Med. Phys. 43, 1955 (2016) [CrossRef] [Google Scholar]
  49. I. Diallo, A. Lamon, A. Shamsaldin, E. Grimaud, F. De Vathaire, J. Chavaudra, Estimation of the radiation dose delivered to any point outside the target volume per patient treated with external beam radiotherapy, Radiother. Oncol. 38, 269 (1996) [CrossRef] [Google Scholar]
  50. P. Francois, C. Beurtheret, A. Dutreix, Calculation of the dose delivered to organs outside the radiation beams, Med. Phys. 15, 879 (1988) [CrossRef] [Google Scholar]
  51. P.H. Van Der Giessen, Peridose, a software program to calculate the dose outside the primary beam in radiation therapy, Radiother. Oncol. 58, 209 (2001) [CrossRef] [Google Scholar]
  52. B. Sánchez-Nieto et al., Analytical model for photon peripheral dose estimation in radiotherapy treatments, Biomed. Phys. Eng. Exp. 1, 045205 (2015) [CrossRef] [Google Scholar]
  53. J. Baró, J. Sempau, J.M. Fernández-Varea, F. Salvat, PENELOPE: an algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter, Nucl. Instrum. Meth. Phys. Res. Sect. B: Beam Interact. Mater. At. 100, 31 (1995) [CrossRef] [Google Scholar]
  54. F. Salvat, A generic algorithm for Monte Carlo simulation of proton transport, Nucl. Instrum. Meth. Phys. Res. Sect. B: Beam Interact. Mater. At. 316, 144 (2013) [CrossRef] [Google Scholar]
  55. N. Verbeek et al., Experiments and Monte Carlo simulations on multiple Coulomb scattering of protons, Med. Phys. 48, 3186 (2021) [CrossRef] [Google Scholar]
  56. J. Perl, J. Shin, J. Schumann, B. Faddegon, H. Paganetti, TOPAS: an innovative proton Monte Carlo platform for research and clinical applications, Med. Phys. 39, 6818 (2012) [CrossRef] [Google Scholar]
  57. S. Agostinelli et al., GEANT4 – A simulation toolkit, Nucl. Instrum. Meth. Phys. Res. Sect. A: Accel. Spectrom. Detect. Assoc. Equip. 506, 250 (2003) [CrossRef] [Google Scholar]
  58. M. De Saint-Hubert et al., Validation of a Monte Carlo framework for out-of-field dose calculations in proton therapy, Front. Oncol. 12, 1 (2022) [Google Scholar]
  59. P. Hauri et al., Development of whole-body representation and dose calculation in a commercial treatment planning system, Z. Med. Phys. 32, 159 (2022) [CrossRef] [Google Scholar]

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