Vol 29, No 5 (2024)
Review paper
Published online: 2024-11-15

open access

Page views 55
Article views/downloads 3
Get Citation

Connect on Social Media

Connect on Social Media

Monte Carlo methods to assess biological response to radiation in peripheral organs and in critical organs near the target

Natalia Matuszak1, Igor Piotrowski21, Marta Kruszyna-Mochalska13, Agnieszka Skrobała31, Mirosława Mocydlarz-Adamcewicz1, Julian Malicki13
DOI: 10.5603/rpor.103525
Rep Pract Oncol Radiother 2024;29(5):638-648.

Abstract

Background: The biological effects and clinical consequences of out-of-field radiation in peripheral organs can be difficult to determine, especially for low doses (0.1 Gy–1 Gy). In recent years, Monte Carlo (MC) methods have been proposed to more accurately predict nontarget doses. The aim of the present study was to assess the feasibility of using Monte Carlo methods to predict the biological response of tissues and critical organs to low dose radiation (0.1 to 1 Gy) based on results published in the literature.

Materials and methods. Literature review, including studies published by our group.

Results and Conclusions. It has long been assumed that radiation doses to peripheral organs located far from the target volume are too low to have any clinical impact. In recent years, however, concerns about the risk of treatment-induced secondary cancers, even in peripheral organs, have continued to grow in line with increasing life expectancy. At present, it is difficult in routine calculations to accurately determine radiation doses to the whole body and peripheral organs. Moreover, the potential clinical impact of these doses remains uncertain and the biological response to low dose radiation depends on the organ. In this context, MC methods can predict biological response in those organs. Monte Carlo methods have become a powerful tool to better predict the consequences of interactions between ionising radiation and biological matter. MC modelling can also help to characterise microscopic system dynamics and to provide a better understanding of processes occurring at the cellular, molecular, and nanoscales.

Article available in PDF format

View PDF Download PDF file

References

  1. Rodemann HP, Blaese MA, Cordes N, et al. Fibronectin and laminin increase resistance to ionizing radiation and the cytotoxic drug Ukrain in human tumour and normal cells in vitro. Int J Radiat Biol. 2003; 79(9): 709–720.
  2. Nikjoo H, Emfietzoglou D, Liamsuwan T, et al. Radiation track, DNA damage and response-a review. Rep Prog Phys. 2016; 79(11): 116601.
  3. Wang Z, Lv MY, Huang YX. Effects of Low-Dose X-Ray on Cell Growth, Membrane Permeability, DNA Damage and Gene Transfer Efficiency. Dose Response. 2020; 18(4): 1559325820962615.
  4. Khan MdG, Wang Yi. Advances in the Current Understanding of How Low-Dose Radiation Affects the Cell Cycle. Cells. 2022; 11(3).
  5. Shimura N, Kojima S. The Lowest Radiation Dose Having Molecular Changes in the Living Body. Dose Response. 2018; 16(2): 1559325818777326.
  6. Lonati L, Barbieri S, Guardamagna I, et al. Radiation-induced cell cycle perturbations: a computational tool validated with flow-cytometry data. Sci Rep. 2021; 11(1): 925.
  7. Pham QT, Anne A, Bony M, et al. Coupling of Geant4-DNA physics models into the GATE Monte Carlo platform: Evaluation of radiation-induced damage for clinical and preclinical radiation therapy beams. Nucl Instr Meth Phys Res Sect B. 2015; 353: 46–55.
  8. Shin E, Lee S, Kang H, et al. Organ-Specific Effects of Low Dose Radiation Exposure: A Comprehensive Review. Front Genet. 2020; 11: 566244.
  9. Polgár S, Schofield PN, Madas BG. Datasets of in vitro clonogenic assays showing low dose hyper-radiosensitivity and induced radioresistance. Sci Data. 2022; 9(1): 555.
  10. Wang Y, Gao J, Tang Bo, et al. A comparative study on the dose-effect of low-dose radiation based on microdosimetric analysis and single-cell sequencing technology. Sci Rep. 2024; 14(1): 11524.
  11. Kry SF, Bednarz B, Howell RM, et al. AAPM TG 158: Measurement and calculation of doses outside the treated volume from external-beam radiation therapy. Med Phys. 2017; 44(10): e391–e429.
  12. National Council on Radiation Protection and Measurements. Neutron contamination from medical electron accelerators. NCRP Report 79, Bethesda 1984.
  13. Al-Ghamdi H, Al-Jarallah MI, Maalej N. Photoneutron intensity variation with field size around radiotherapy linear accelerator 18-MeV X-ray beam. Radiat Measurements. 2008; 43: S495–S499.
  14. Xingcai G, Masanobu M, Isao M, et al. Design of an epi-thermal neutron flux intensity monitor with GaN wafer for boron neutron capture therapy. J Nucl Sci Technol. 2015; 52: 503–508.
  15. Kry SF, Salehpour M, Followill DS, et al. Out-of-field photon and neutron dose equivalents from step-and-shoot intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys. 2005; 62(4): 1204–1216.
  16. Vedelago J, Geser FA, Muñoz ID, et al. Assessment of secondary neutrons in particle therapy by Monte Carlo simulations. Phys Med Biol. 2022; 67(1).
  17. Thwaites D. Accuracy required and achievable in radiotherapy dosimetry: have modern technology and techniques changed our views? J Phys: Conference Series. 2013; 444: 012006.
  18. Kruszyna-Mochalska M, Skrobala A, Romanski P, et al. Influence of Specific Treatment Parameters on Nontarget and Out-of-Field Doses in a Phantom Model of Prostate SBRT with CyberKnife and TrueBeam. Life (Basel). 2022; 12(5).
  19. IAEA. Absorbed Dose Determination in External Beam Radiotherapy, Technical Reports Series No. 398. IAEA, Vienna 2000.
  20. Krieger T, Sauer OA. Monte Carlo- versus pencil-beam-/collapsed-cone-dose calculation in a heterogeneous multi-layer phantom. Phys Med Biol. 2005; 50(5): 859–868.
  21. Elcim Y, Dirican B, Yavas O. Dosimetric comparison of pencil beam and Monte Carlo algorithms in conformal lung radiotherapy. J Appl Clin Med Phys. 2018; 19(5): 616–624.
  22. Khan FM. The Physics of Radiation Therapy. Chapter 19, 4th Ed. Lippincott Williams & Wilkins, Philadelphia 2010.
  23. Oelkfe U, Scholz C. Dose Calculation Algorithms. In: Schlegel W, Bortfeld T, Grosu AL. ed. New Technologies in Radiation Oncology. Medical Radiology. Springer, Berlin, Heidelberg 2006.
  24. Paganetti H, Jiang H, Parodi K, et al. Clinical implementation of full Monte Carlo dose calculation in proton beam therapy. Phys Med Biol. 2008; 53(17): 4825–4853.
  25. Sakata D, Belov O, Bordage MC, et al. Fully integrated Monte Carlo simulation for evaluating radiation induced DNA damage and subsequent repair using Geant4-DNA. Sci Rep. 2020; 10(1): 20788.
  26. Matuszak N, Kruszyna-Mochalska M, Skrobala A, et al. Nontarget and Out-of-Field Doses from Electron Beam Radiotherapy. Life (Basel). 2022; 12(6).
  27. Matuszak N, Kruszyna-Mochalska M, Skrobała A, et al. Monte Carlo computation of photon energy spectra in central axis of flattened and unflattened beams and doses in critical organs in a water phantom model of prostate radiotherapy. Radiat Phys Chem. 2022; 198: 110211.
  28. El Naqa I, Pater P, Seuntjens J. Monte Carlo role in radiobiological modelling of radiotherapy outcomes. Phys Med Biol. 2012; 57(11): R75–R97.
  29. Bozkurt A, Toker GD, Erkilic M. A personalized Monte Carlo study of tumor and critical organ doses for trans-arterial radioembolization patients. Phys Med Biol. 2023; 68(19).
  30. Barnett GC, West CML, Dunning AM, et al. Normal tissue reactions to radiotherapy: towards tailoring treatment dose by genotype. Nat Rev Cancer. 2009; 9(2): 134–142.
  31. Piotrowski I, Kulcenty K, Suchorska WM, et al. Carcinogenesis Induced by Low-dose Radiation. Radiol Oncol. 2017; 51(4): 369–377.
  32. Dang AT, Levin-Epstein RG, Shabsovich D, et al. Gantry-Mounted Linear Accelerator-Based Stereotactic Body Radiation Therapy for Low- and Intermediate-Risk Prostate Cancer. Adv Radiat Oncol. 2020; 5(3): 404–411.
  33. Butterworth KT, McGarry CK, Trainor C, et al. Dose, dose-rate and field size effects on cell survival following exposure to non-uniform radiation fields. Phys Med Biol. 2012; 57(10): 3197–3206.
  34. Jiao Y, Cao F, Liu Hu. Radiation-induced Cell Death and Its Mechanisms. Health Phys. 2022; 123(5): 376–386.
  35. Ullrich RL, Davis CM. Radiation-induced cytogenetic instability in vivo. Radiat Res. 1999; 152(2): 170–173.
  36. Matsuda Y, Uchimura A, Satoh Y, et al. Spectra and characteristics of somatic mutations induced by ionizing radiation in hematopoietic stem cells. Proc Natl Acad Sci U S A. 2023; 120(15): e2216550120.
  37. Schneider U, Sumila M, Robotka J, et al. Dose-response relationship for breast cancer induction at radiotherapy dose. Radiat Oncol. 2011; 6: 67.
  38. Fleet A. Radiobiology for the Radiologist: 6th edition, Eric J. Hall, Amato J. Giaccia, Lppincott Williams and Wilkins Publishing; ISBN 0-7817-4151-3; 656 pages; 2006; Hardback; £53CrossRef
  • Green DR, Llambi F. Cell Death Signaling. Cold Spring Harb Perspect Biol. 2015; 7(12).
  • McMahon SJ, Prise KM. Mechanistic Modelling of Radiation Responses. Cancers (Basel). 2019; 11(2).
  • Bernal MA, Bordage MC, Brown JMC, et al. Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit. Phys Med. 2015; 31(8): 861–874.
  • Friedland W, Dingfelder M, Kundrát P, et al. Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC. Mutat Res. 2011; 711(1-2): 28–40.
  • McMahon SJ, McNamara AL, Schuemann J, et al. A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation. Sci Rep. 2017; 7(1): 10790.
  • Dos Santos M, Clairand I, Gruel G, et al. Influence of chromatin condensation on the number of direct DSB damages induced by ions studied using a Monte Carlo code. Radiat Prot Dosimetry. 2014; 161(1-4): 469–473.
  • Rydzewski NR, Helzer KT, Bootsma M, et al. Machine Learning & Molecular Radiation Tumor Biomarkers. Semin Radiat Oncol. 2023; 33(3): 243–251.
  • Røe K, Kakar M, Seierstad T, et al. Early prediction of response to radiotherapy and androgen-deprivation therapy in prostate cancer by repeated functional MRI: a preclinical study. Radiat Oncol. 2011; 6: 65.
  • Francis Z, Incerti S, Ivanchenko V, et al. Monte Carlo simulation of energy-deposit clustering for ions of the same LET in liquid water. Phys Med Biol. 2012; 57(1): 209–224.
  • Bernal M, et al. The invariance of the total direct DNA strand break yield. Med Phys. 2011; 38(7): 4147–4153.
  • Bernal MA, Sikansi D, Cavalcante F, et al. Performance of a new atomistic geometrical model of the B-DNA configuration for DNA-radiation interaction simulations. J Phys: Conference Series. 2014; 490: 012150.
  • Santos MD, Villagrasa C, Clairand I, et al. Influence of the DNA density on the number of clustered damages created by protons of different energies. Nucl Instrum Methods Phys Res B. 2013; 298: 47–54.
  • Meylan S, Vimont U, Incerti S, et al. Geant4-DNA simulations using complex DNA geometries generated by the DnaFabric tool. Comput Phys Commun. 2016; 204: 159–169.
  • Meylan S, Incerti S, Karamitros M, et al. Simulation of early DNA damage after the irradiation of a fibroblast cell nucleus using Geant4-DNA. Sci Rep. 2017; 7(1): 11923.
  • Tang H, Cai L, He X, et al. Radiation-induced bystander effect and its clinical implications. Front Oncol. 2023; 13: 1124412.
  • McMahon S, Butterworth K, McGarry C, et al. A Computational Model of Cellular Response to Modulated Radiation Fields. Int J Radiat Oncol Biol Phys. 2012; 84(1): 250–256.
  • Butterworth K, McGarry C, Trainor C, et al. Out-of-Field Cell Survival Following Exposure to Intensity-Modulated Radiation Fields. Int J Radiat Oncol Biol Phys. 2011; 79(5): 1516–1522.
  • Crowther JA. Some Considerations elative to the Action of X-ays on Tissue Cells. Proc R Soc B Biol Sci. 1924; 96: 207–211.
  • Nikjoo H, O'Neill P, Goodhead DT, et al. Computational modelling of low-energy electron-induced DNA damage by early physical and chemical events. Int J Radiat Biol. 1997; 71(5): 467–483.
  • Nikjoo H, Uehara S, Emfietzoglou D, et al. Track-structure codes in radiation research. Rad Meas. 2006; 41(9-10): 1052–1074.
  • Semenenko VA, Stewart RD. Fast Monte Carlo simulation of DNA damage formed by electrons and light ions. Phys Med Biol. 2006; 51(7): 1693–1706.
  • Stewart RD, Streitmatter SW, Argento DC, et al. Rapid MCNP simulation of DNA double strand break (DSB) relative biological effectiveness (RBE) for photons, neutrons, and light ions. Phys Med Biol. 2015; 60(21): 8249–8274.
  • Uehara S, Nikjoo H, Goodhead DT. Cross-sections for water vapour for the Monte Carlo electron track structure code from 10 eV to the MeV region. Phys Med Biol. 1999; 38(12): 1841–1858.
  • Wälzlein C, Scifoni E, Krämer M, et al. Simulations of dose enhancement for heavy atom nanoparticles irradiated by protons. Phys Med Biol. 2014; 59(6): 1441–1458.
  • Plante I, Cucinotta FA. Monte-Carlo simulation of ionizing radiation tracks. Application of Monte Carlo methods. In: Mode CJ, Cucinotta FA. ed. Biology, medicine and other fields of science. InTech, Rijeka, Croatia 2011: 315–56.
  • Tajik M, Rozatian A, Semsarha F. Calculation of direct effects of 60Co gamma rays on the different DNA structural levels: A simulation study using the Geant4-DNA toolkit. Nucl Instrum Methods Phys Res B. 2015; 346: 53–60.
  • Douglass M, Bezak E, Penfold S. Development of a radiation track structure clustering algorithm for the prediction of DNA DSB yields and radiation induced cell death in Eukaryotic cells. Phys Med Biol. 2015; 60(8): 3217–3236.
  • Zhang Y, Feng Y, Wang W, et al. An Expanded Multi-scale Monte Carlo Simulation Method for Personalized Radiobiological Effect Estimation in Radiotherapy: a feasibility study. Sci Rep. 2017; 7: 45019.
  • Zygmanski P, Sajo E. Nanoscale radiation transport and clinical beam modeling for gold nanoparticle dose enhanced radiotherapy (GNPT) using X-rays. Br J Radiol. 2016; 89(1059): 20150200.
  • Plante I, Cucinotta FA. Monte-Carlo simulation of ionizing radiation tracks. In: Mode CB. ed. Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science. InTech, Rijeka 2011.
  • Plante I, West DW, Weeks J, et al. Simulation of Radiation-Induced DNA Damage and Protection by Histones Using the Code RITRACKS. BioTech (Basel). 2024; 13(2).
  • Friedland W, Kundrát P, Schmitt E, et al. Modeling DNA damage by photons and light ions over energy ranges used in medical applications. Radiat Prot Dosimetry. 2019; 183(1-2): 84–88.
  • Friedland W, Dingfelder M, Kundrát P, et al. Track structures, DNA targets and radiation effects in the biophysical Monte Carlo simulation code PARTRAC. Mutat Res. 2011; 711(1-2): 28–40.
  • Sarrut D, Etxebeste A, Muñoz E, et al. Artificial Intelligence for Monte Carlo Simulation in Medical Physics. Frontiers Phys. 2021; 9.
  • Siddique S, Chow JCL. Artificial intelligence in radiotherapy. Rep Pract Oncol Radiother. 2020; 25(4): 656–666.
  • Nguyen D, Lin MH, Sher D, et al. Advances in Automated Treatment Planning. Semin Radiat Oncol. 2022; 32(4): 343–350.