Skip to main content

Advertisement

Log in

Cellular S values in spindle-shaped cells: a dosimetry study on more realistic cell geometries using Geant4-DNA Monte Carlo simulation toolkit

  • Original Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

Cellular dosimetry plays a crucial role in radiobiology and evaluation of the relative merits of radiopharmaceuticals used for targeted radionuclide therapy. The present study aims to investigate the effects of various cell geometries on dosimetric characteristics of several Auger emitters distributed in different subcellular compartments using Monte Carlo simulation.

Methods

The Geant4-DNA extension of the Geant4 Monte Carlo simulation toolkit was employed to calculate the mean absorbed dose per unit cumulated activity (S value) for different subcellular distributions of several Auger electron-emitting theranostic radionuclides including 99mTc, 111In, 123I, 125I, and 201Tl. The simulations were carried out in various single-cell models of liquid water including spherical, ellipsoidal, spherical spindle, and ellipsoidal spindle cell models. The latter two models which are generalized from the first two models were inspired by the morphologies of spindle-shaped (fusiform) cells, and were developed to provide more realistic modeling of this common geometry observed in many healthy and cancerous cells.

Results

Evaluation of the S values calculated for the examined cell models reveals that the differences are small (less than 9%) for the cell ← cell, cell ← cell surface, and nucleus ← nucleus source–target combinations. However, moderate discrepancies are seen (up to 28%) when the nucleus is considered as the target, as well as the radioactivity is either internalized into the cytoplasm or bound to the cell membrane.

Conclusions

The findings of the present work suggest that the assumption of spherical cell geometry may provide reasonably accurate estimates of the cellular/nuclear dose for the considered Auger emitters, even for spindle-shaped cells. Of course, this approximation should be used with caution for the nucleus ← cytoplasm and nucleus ← cell surface configurations, since the S-value sensitivity to the cell geometry is somewhat significant in these cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Emfietzoglou D, Kostarelos K, Hadjidoukas P, Bousis C, Fotopoulos A, Pathak A, et al. Subcellular S-factors for low-energy electrons: a comparison of Monte Carlo simulations and continuous-slowing-down calculations. Int J Radiat Biol. 2008;84:1034–44. https://doi.org/10.1080/09553000802460180.

    Article  CAS  PubMed  Google Scholar 

  2. Santos-Cuevas CL, Ferro-Flores G, Rojas-Calderón EL, García-Becerra R, Ordaz-Rosado D, Arteaga de Murphy C, et al. 99mTc-N2S2-Tat (49–57)-bombesin internalized in nuclei of prostate and breast cancer cells: kinetics, dosimetry and effect on cellular proliferation. Nucl Med Commun. 2011;32:303–13. https://doi.org/10.1097/MNM.0b013e328341b27f.

    Article  CAS  PubMed  Google Scholar 

  3. André T, Morini F, Karamitros M, Delorme R, Le Loirec C, Campos L, et al. Comparison of Geant4-DNA simulation of S-values with other Monte Carlo codes. Nucl Instrum Methods Phys Res B. 2014;319:87–94. https://doi.org/10.1016/j.nimb.2013.11.005.

    Article  CAS  Google Scholar 

  4. Emfietzoglou D, Bousis C, Hindorf C, Fotopoulos A, Pathak A, Kostarelos K. A Monte Carlo study of energy deposition at the sub-cellular level for application to targeted radionuclide therapy with low-energy electron emitters. Nucl Instrum Methods Phys Res B. 2007;256:547–53. https://doi.org/10.1016/j.nimb.2006.12.055.

    Article  CAS  Google Scholar 

  5. Bousis C, Emfietzoglou D, Hadjidoukas P, Nikjoo H. A Monte Carlo study of cellular S-factors for 1 keV to 1 MeV electrons. Phys Med Biol. 2009;54:5023–38. https://doi.org/10.1088/0031-9155/54/16/012.

    Article  CAS  PubMed  Google Scholar 

  6. Cornelissen B, Vallis KA. Targeting the nucleus: an overview of Auger-electron radionuclide therapy. Curr Drug Discov Technol. 2010;7:263–79. https://doi.org/10.2174/157016310793360657.

    Article  CAS  PubMed  Google Scholar 

  7. Rojas-Calderón EL, Torres-García E, Ávila O. Dose per unit cumulated activity (S-values) for e and beta emitting radionuclides in cancer cell models calculated by Monte Carlo simulation. Appl Radiat Isot. 2014;90:229–33. https://doi.org/10.1016/j.apradiso.2014.04.012.

    Article  CAS  PubMed  Google Scholar 

  8. Gudkov SV, Shilyagina NY, Vodeneev VA, Zvyagin AV. Targeted radionuclide therapy of human tumors. Int J Mol Sci. 2016;17:33. https://doi.org/10.3390/ijms17010033.

    Article  CAS  Google Scholar 

  9. Bardiès M, Pihet P. Dosimetry and microdosimetry of targeted radiotherapy. Curr Pharm Des. 2000;6:1469–502. https://doi.org/10.2174/1381612003399176.

    Article  PubMed  Google Scholar 

  10. Sgouros G. Dosimetry of internal emitters. J Nucl Med. 2005;46:18S–27S.

    PubMed  Google Scholar 

  11. Champion C, Zanotti-Fregonara P, Hindié E. CELLDOSE: a Monte Carlo code to assess electron dose distribution—S values for 131I in spheres of various sizes. J Nucl Med. 2008;49:151–7. https://doi.org/10.2967/jnumed.107.045179.

    Article  PubMed  Google Scholar 

  12. Siragusa M, Baiocco G, Fredericia PM, Friedland W, Groesser T, Ottolenghi A, et al. The COOLER code: a novel analytical approach to calculate subcellular energy deposition by internal electron emitters. Radiat Res. 2017;182:204–20. https://doi.org/10.1667/RR14683.1.

    Article  Google Scholar 

  13. Fourie H, Newman R, Slabbert J. Microdosimetry of the Auger electron emitting 123I radionuclide using Geant4-DNA simulations. Phys Med Biol. 2015;60:3333–46. https://doi.org/10.1088/0031-9155/60/8/3333.

    Article  CAS  PubMed  Google Scholar 

  14. Šefl M, Incerti S, Papamichael G, Emfietzoglou D. Calculation of cellular S-values using Geant4-DNA: the effect of cell geometry. Appl Radiat Isot. 2015;104:113–23. https://doi.org/10.1016/j.apradiso.2015.06.027.

    Article  CAS  PubMed  Google Scholar 

  15. Nikjoo H, Emfietzoglou D, Liamsuwan T, Taleei R, Liljequist R, Uehara S. Radiation track, DNA damage and response—a review. Rep Prog Phys. 2016;79:116601. https://doi.org/10.1088/0034-4885/79/11/116601.

    Article  CAS  PubMed  Google Scholar 

  16. Humm JL, Howell RW, Rao DV. Dosimetry of Auger-electron-emitting radionuclides: report no. 3 of AAPM nuclear medicine task group no. 6. Med Phys. 1994;21:1901–15. https://doi.org/10.1118/1.597227.

    Article  CAS  PubMed  Google Scholar 

  17. Ftáčniková S, Böhm R. Monte Carlo calculations of energy deposition on cellular, multicellular and organ level for Auger emitters. Radiat Prot Dosim. 2000;92:279–88. https://doi.org/10.1093/oxfordjournals.rpd.a033293.

    Article  Google Scholar 

  18. Goddu SM, Howell RW, Bouchet L, Bolch W, Rao DV. MIRD cellular S values. Reston: Society of Nuclear Medicine; 1997.

    Google Scholar 

  19. Howell RW, Wessels BW, Leovinger R, In Collaboration with the MIRD Committee, Society of Nuclear Medicine. The MIRD perspective 1999. J Nucl Med. 1999;40:3S–10S.

    CAS  PubMed  Google Scholar 

  20. Incerti S, Kyriakou I, Bernal MA, Bordage MC, Francis Z, Guatelli S, et al. Geant4-DNA example applications for track structure simulations in liquid water: a report from the Geant4-DNA project. Med Phys. 2018;45:e722. https://doi.org/10.1002/mp.13048.

    Article  CAS  Google Scholar 

  21. Howell RW, Rao DV, Sastry KSR. Macroscopic dosimetry for radioimmunotherapy: nonuniform activity distributions in solid tumors. Med Phys. 1989;16:66–74. https://doi.org/10.1118/1.596404.

    Article  CAS  PubMed  Google Scholar 

  22. Bousis C, Emfietzoglou D, Nikjoo H. Monte Carlo single-cell dosimetry of I-131, I-125 and I-123 for targeted radioimmunotherapy of B-cell lymphoma. Int J Radiat Biol. 2012;88:908–15. https://doi.org/10.3109/09553002.2012.666004.

    Article  CAS  PubMed  Google Scholar 

  23. Arnaud FX, Paillas S, Pouget JP, Incerti S, Bardiès M, Bordage MC. Complex cell geometry and sources distribution model for Monte Carlo single cell dosimetry with iodine 125 radioimmunotherapy. Nucl Instrum Methods Phys Res B. 2016;366:227–33. https://doi.org/10.1016/j.nimb.2015.11.008.

    Article  CAS  Google Scholar 

  24. Nikjoo H, Uehara S, Emfietzoglou D, Cucinotta FA. Track-structure codes in radiation research. Radiat Meas. 2006;41:1052–74. https://doi.org/10.1016/j.radmeas.2006.02.001.

    Article  CAS  Google Scholar 

  25. Bousis C, Emfietzoglou D, Hadjidoukas P, Nikjoo H. Monte Carlo single-cell dosimetry of Auger-electron emitting radionuclides. Phys Med Biol. 2010;55:2555–72. https://doi.org/10.1088/0031-9155/55/9/009.

    Article  CAS  PubMed  Google Scholar 

  26. Falzone N, Fernández-Varea JM, Flux G, Vallis KA. Monte Carlo evaluation of Auger electron-emitting theranostic radionuclides. J Nucl Med. 2015;56:1441–6. https://doi.org/10.2967/jnumed.114.153502.

    Article  CAS  PubMed  Google Scholar 

  27. Taborda A, Benabdallah N, Desbrée A. Dosimetry at the sub-cellular scale of Auger-electron emitter Tc-99 m in a mouse single thyroid follicle. Appl Radiat Isot. 2016;108:58–63. https://doi.org/10.1016/j.apradiso.2015.12.010.

    Article  CAS  PubMed  Google Scholar 

  28. Nettleton JS, Lawson RS. Cellular dosimetry of diagnostic radionuclides for spherical and ellipsoidal geometry. Phys Med Biol. 1996;41:1845–54. https://doi.org/10.1088/0031-9155/41/9/018.

    Article  CAS  PubMed  Google Scholar 

  29. Amato E, Lizio D, Baldari S. Absorbed fractions in ellipsoidal volumes for β radionuclides employed in internal radiotherapy. Phys Med Biol. 2009;54:4171–80. https://doi.org/10.1088/0031-9155/54/13/013.

    Article  CAS  PubMed  Google Scholar 

  30. Amato E, Lizio D, Baldari S. Absorbed fractions for electrons in ellipsoidal volumes. Phys Med Biol. 2011;56:357–65. https://doi.org/10.1088/0031-9155/56/2/005.

    Article  CAS  PubMed  Google Scholar 

  31. Salim R, Taherparvar P. Monte Carlo single-cell dosimetry using Geant4-DNA: the effects of cell nucleus displacement and rotation on cellular S values. Radiat Environ Biophys. 2019;58:353–71. https://doi.org/10.1007/s00411-019-00788-z.

    Article  CAS  PubMed  Google Scholar 

  32. Ikenaga N, Ohuchida K, Mizumoto K, Akagawa S, Fujiwara K, Eguchi D, et al. Pancreatic cancer cells enhance the ability of collagen internalization during epithelial–mesenchymal transition. PLoS ONE. 2012;7:e40434. https://doi.org/10.1371/journal.pone.0040434.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Duclos G, Erlenkämper C, Joanny JF, Silberzan P. Topological defects in confined populations of spindle-shaped cells. Nat Phys. 2017;13:58–62. https://doi.org/10.1038/nphys3876.

    Article  CAS  Google Scholar 

  34. Franchi M, Masola V, Bellin G, Onisto M, Karamanos KA, Piperigkou Z. Collagen fiber array of peritumoral stroma influences epithelial-to-mesenchymal transition and invasive potential of mammary cancer cells. J Clin Med. 2019;8:213. https://doi.org/10.3390/jcm8020213.

    Article  CAS  PubMed Central  Google Scholar 

  35. Human Protein Atlas. The Human Protein Atlas. 2019. https://www.proteinatlas.org/ENSG00000068903-SIRT2/cell#img and https://www.proteinatlas.org/ENSG00000161800-RACGAP1/cell#img. Accessed 14 Apr 2019.

  36. Sechopoulos I, Rogers DWO, Bazalova-Carter M, Bolch WE, Heath EC, McNitt-Gray MF, et al. RECORDS: improved Reporting of montE CarlO RaDiation transport Studies: report of the AAPM Research Committee Task Group 268. Med Phys. 2018;45:e1–5. https://doi.org/10.1002/mp.12702.

    Article  PubMed  Google Scholar 

  37. Agostinelli S, Allison J, Amako K, Apostolakis J, Araujo H, Arce P, et al. Geant4—a simulation toolkit. Nucl Instrum Methods Phys Res A. 2003;506:250–303. https://doi.org/10.1016/S0168-9002(03)01368-8.

    Article  CAS  Google Scholar 

  38. Allison J, Amako K, Apostolakis J, Araujo H, Arce Dubois P, Asai M, et al. Geant4 developments and applications. IEEE Trans Nucl Sci. 2006;53:270–8. https://doi.org/10.1109/TNS.2006.869826.

    Article  Google Scholar 

  39. Allison J, Amako K, Apostolakis J, Arce P, Asai M, Aso T, et al. Recent developments in Geant4. Nucl Instrum Methods Phys Res A. 2016;835:186–225. https://doi.org/10.1016/j.nima.2016.06.125.

    Article  CAS  Google Scholar 

  40. Incerti S, Baldacchino G, Bernal MA, Capra R, Champion C, Francis Z, et al. The Geant4-DNA project. Int J Model Simul Sci Comput. 2010;1:157–78. https://doi.org/10.1142/S1793962310000122.

    Article  Google Scholar 

  41. Bernal MA, Bordage MC, Brown JMC, Davídková M, Delage E, El Bitar Z, 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;31:861–74. https://doi.org/10.1016/j.ejmp.2015.10.087.

    Article  CAS  PubMed  Google Scholar 

  42. Geant4 collaboration. Geant4 user’s guide for application developers, release 10.5. 2019. https://geant4.web.cern.ch/support/user_documentation. Accessed 1 Aug 2019.

  43. Incerti S, Ivanchenko A, Karamitros M, Mantero A, Moretto P, Tran HN, et al. Comparison of GEANT4 very low energy cross section models with experimental data in water. Med Phys. 2010;37:4692–708. https://doi.org/10.1118/1.3476457.

    Article  CAS  PubMed  Google Scholar 

  44. Howell RW. Radiation spectra for Auger-electron emitting radionuclides: report no. 2 of AAPM nuclear medicine task group no. 6. Med Phys. 1992;19:1371–83. https://doi.org/10.1118/1.596927.

    Article  CAS  PubMed  Google Scholar 

  45. Vaziri B, Wu H, Dhawan AP, Du P, Howell RW. MIRD pamphlet no. 25: MIRDcell v2.0 software tool for dosimetric analysis of biologic response of multicellular populations. J Nucl Med. 2014;55:1557–64. https://doi.org/10.2967/jnumed.113.131037.

    Article  PubMed  Google Scholar 

  46. Incerti S, Douglass M, Penfold S, Guatelli S, Bezak E. Review of Geant4-DNA applications for micro and nanoscale simulations. Phys Med. 2016;32:1187–200. https://doi.org/10.1016/j.ejmp.2016.09.007.

    Article  CAS  PubMed  Google Scholar 

  47. Incerti S, Kyriakou I, Bordage MC, Guatelli S, Ivanchenko V, Emfietzoglou D. Track structure simulations of proximity functions in liquid water using the Geant4-DNA toolkit. J Appl Phys. 2019;125:104301. https://doi.org/10.1063/1.5083208.

    Article  CAS  Google Scholar 

  48. Kyriakou I, Emfietzoglou D, Ivanchenko V, Bordage MC, Guatelli S, Lazarakis P, et al. Microdosimetry of electrons in liquid water using the low energy models of Geant4. J Appl Phys. 2017;122:024303. https://doi.org/10.1063/1.4992076.

    Article  CAS  Google Scholar 

  49. Lampe N, Karamitros M, Breton V, Brown JMC, Kyriakou I, Sakata D, et al. Mechanistic DNA damage simulations in Geant4-DNA part 1: a parameter study in a simplified geometry. Phys Med. 2018;48:135–45. https://doi.org/10.1016/j.ejmp.2018.02.011.

    Article  PubMed  Google Scholar 

  50. Lampe N, Karamitros M, Breton V, Brown JMC, Sakata D, Sarramia D, et al. Mechanistic DNA damage simulations in Geant4-DNA part 2: electron and proton damage in a bacterial cell. Phys Med. 2018;48:146–55. https://doi.org/10.1016/j.ejmp.2017.12.008.

    Article  PubMed  Google Scholar 

  51. Michel RB, Castillo ME, Andrews PM, Mattes MJ. In vitro toxicity of A-431 carcinoma cells with antibodies to epidermal growth factor receptor and epithelial glycoprotein-1 conjugated to radionuclides emitting low-energy electrons. Clin Cancer Res. 2004;10:5957–66. https://doi.org/10.1158/1078-0432.CCR-03-0465.

    Article  CAS  PubMed  Google Scholar 

  52. Ocampo-García BE, Santos-Cuevas CL, León-Rodríguez LM, García-Becerra R, Ordaz-Rosado D, Luna-Gutiérrez MA, et al. Design and biological evaluation of 99mTc-N2S2-Tat(49–57)-c(RGDyK): a hybrid radiopharmaceutical for tumors expressing α(v)β(3) integrins. Nucl Med Biol. 2013;40:481–7. https://doi.org/10.1016/j.nucmedbio.2013.01.003.

    Article  CAS  PubMed  Google Scholar 

  53. Pouget JP, Santoro L, Raymond L, Chouin N, Bardiès M, Bascoul-Mollevi C, et al. Cell membrane is a more sensitive target than cytoplasm to dense ionization produced by auger electrons. Radiat Res. 2008;170:192–200. https://doi.org/10.1667/RR1359.1.

    Article  CAS  PubMed  Google Scholar 

  54. Bousis C. Dosimetry on sub-cellular level for intracellular incorporated Auger-electron-emitting radionuclides: a comparison of Monte Carlo simulation and analytic calculations. Radiat Prot Dosim. 2011;143:33–41. https://doi.org/10.1093/rpd/ncq293.

    Article  CAS  Google Scholar 

  55. Eckerman KF, Westfall RJ, Ryman JC, Cristy M. Nuclear decay data files of the Dosimetry Research Group. Tennessee: Oak Ridge National Laboratory (ORNL); 1993. https://doi.org/10.2172/10116928.

    Book  Google Scholar 

Download references

Funding

The authors received no specific funding for this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Payvand Taherparvar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salim, R., Taherparvar, P. Cellular S values in spindle-shaped cells: a dosimetry study on more realistic cell geometries using Geant4-DNA Monte Carlo simulation toolkit. Ann Nucl Med 34, 742–756 (2020). https://doi.org/10.1007/s12149-020-01498-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-020-01498-z

Keywords

Navigation