Advertisement

Biological Models in Treatment Planning

  • Christian P. Karger
Chapter
Part of the Medical Radiology book series (MEDRAD)

18.8 Conclusion

Several biological models have been developed. Although these models give a correct description of the main characteristics of the radiation response, great caution has to be taken if these models are to be applied to patients.

While the linear-quadratic model provides a good description of experimental settings, a larger uncertainty is involved in the prediction of iso-effects for clinical applications. The more advanced NTCP and TCP models should only be applied for relative, rather than absolute, predictions of effect probabilities. When using relative values, the uncertainty of the predictions should be considered to decide whether a detected difference is really significant. As TCP/NTCP models are currently not completely validated, integration of these models into the cost function of the dose optimisation algorithm is not warranted. Whether it is possible to arrive at fully biologically optimised treatment plans for photon therapy has to be investigated by further research.

In this context, the clinical application of heavy charged particles plays an exceptional role as biological optimisation is routinely performed and an adequate RBE model is an essential prerequisite. The applied RBE model may still contain some degree of uncertainty which has to be considered carefully at treatment plan assessment and dose prescription.

Keywords

Radiat Oncol Biol Phys Biological Model Linear Energy Transfer Boron Neutron Capture Therapy Normal Tissue Complication Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amols HI, Zaider M, Mayes MK et al. (1997) Physician/patient-driven risk assignment in radiation oncology: reality or fancy? In J Radiat Oncol Biol Phys 38:455–461Google Scholar
  2. Barendsen GW (1982) Dose fractionation, dose rate and isoeffect relationships for normal tissue responses. Int J Radiat Oncol Biol Phys 8:1981–1997PubMedGoogle Scholar
  3. Borkenstein K, Levegrün S, Peschke P (2004) Modeling and computer simulations of tumor growth and tumor response to radiotherapy. Radiat Res 162:71–83PubMedGoogle Scholar
  4. Brahme A (2001) Individualizing cancer treatment: biological optimization models in treatment planning and delivery. Int J Radiat Oncol Biol Phys 49:327–337PubMedCrossRefGoogle Scholar
  5. Brenner DJ, Hall JH (1991) Conditions for the equivalence of continuous to pulsed low dose rate brachytherapy. Int J Radiat Oncol Biol Phys 20:181–190PubMedGoogle Scholar
  6. Brenner DJ, Hlatky LR, Hahnfeldt PJ et al. (1995) A convenient extension of the linear-quadratic model to include redistribution and reoxygenation. Int J Radiat Oncol Biol Phys 32:379–390PubMedGoogle Scholar
  7. Burman C (2002) Fitting of tissue tolerance data to analytic function: improving the therapeutic ratio. Front Radiat Ther Oncol 37:151–162PubMedGoogle Scholar
  8. Burman C, Kutcher GJ, Emami B et al. (1991) Fitting normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 21:123–135PubMedGoogle Scholar
  9. Cohen L (1982) The tissue volume factor in radiation oncology. Int J Radiat Oncol Biol Phys 8:1771–1774PubMedGoogle Scholar
  10. Dale RG (1986) The application of the linear-quadratic model to fractionated radiotherapy when there is incomplete normal tissue recovery between fractions, and possible implication for treatments involving multiple fractions per day. Br J Radiol 59:919–927PubMedGoogle Scholar
  11. Dale RG, Huczkowski J, Trott KR (1988) Possible dose rate dependence of recovery kinetics as deduced from a preliminary analysis of the effects of fractionated irradiations at varying dose rates. Br J Radiol 61:153–157PubMedGoogle Scholar
  12. Emami B, Lyman J, Brouwn A et al. (1991) Tolerance of normal tissue to therapeutic irradiation. Int J Radiat Oncol Biol Phys 21:109–122PubMedGoogle Scholar
  13. Flickinger JC (1989) An integrated logistic formula for prediction of complication from radiosurgery. Int J Radiat Oncol Biol Phys 17:879–885PubMedGoogle Scholar
  14. Flickinger JC, Schell MC, Larson D (1990) Estimation of complications for linear accelerator radiosurgery with the integrated logistic formula. Int J Radiat Oncol Biol Phys 19:143–148PubMedGoogle Scholar
  15. Fowler JF (1984) What next in fractionated radiotherapy? Br J Cancer 49(Suppl VI):285–300Google Scholar
  16. Fowler JF (1989) The linear-quadratic formula and progress in fractionated radiotherapy. Br J Radiol 62:679–694PubMedGoogle Scholar
  17. Fowler JF (1992) Brief summary of radiobiological principles in fractionated radiotherapy. Semin Radiat Oncol 2:16–21Google Scholar
  18. Gilbert CW, Hendry JH, Major D (1980) The approximation in the formulation for survival S=exp-(αD+βD2). Int J Radiat Biol 37:469–471Google Scholar
  19. Haberer T, Becher W, Schardt D at al. (1993) Magnetic scanning system for heavy ion therapy. Nucl Instrum Meth A330:296–305Google Scholar
  20. Jackson A, Kutscher GJ, Yorke ED (1993) Probability of radiation induced complications for normal tissues with parallel architecture subject to non-uniform irradiation. Med Phys 20:613–625PubMedGoogle Scholar
  21. Källman P, Agren A, Brahme A (1992) Tumour and normal tissue responses to fractionated non-uniform dose delivery. Int J Radiat Biol 62:249–262PubMedGoogle Scholar
  22. Kanai T, Furusawa Y, Fukutsu K et al. (1997) Irradiation of mixed beam and design of spread-out Bragg peak for heavy-ion radiotherapy. Radiat Res 147:78–85PubMedGoogle Scholar
  23. Kanai T, Endo M, Minohara S et al. (1999) Biophysical characteristics of HIMAC clinical irradiation system for heavy-ion radiation therapy. Int J Radiat Oncol Biol Phys 44:201–210PubMedCrossRefGoogle Scholar
  24. Karger CP, Hartmann GH (2001) Determination of tolerance dose uncertainties and optimal design of dose response experiments with small animal numbers. Strahlenther Onkol 177:37–42PubMedGoogle Scholar
  25. Kraft G (2000) Tumortherapy with heavy charged particles. Prog Part Nucl Phys 45:S473–S544CrossRefGoogle Scholar
  26. Kraft G, Scholz M, Bechthold U (1999) Tumor therapy and track structure. Radiat Environ Biophys 38:229–237PubMedCrossRefGoogle Scholar
  27. Krämer M, Scholz M (2000) Treatment planning for heavy-ion radiotherapy: calculation and optimization of biologically effective dose. Phys Med Biol 45:3319–3330PubMedGoogle Scholar
  28. Krämer M, Weyrather WK, Scholz M (2003) The increased relative biological efficiency of heavy charged particles: from radiobiology to treatment planning. Technol Cancer Res Treat 2:427–436PubMedGoogle Scholar
  29. Kutcher GJ (1996) Quantitative plan evaluation: TCP/NTCP models. Front Radiat Ther Oncol 29:67–80PubMedGoogle Scholar
  30. Kutcher GJ, Burman C (1989) Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume model. Int J Radiat Oncol Biol Phys 16:1623–1630PubMedGoogle Scholar
  31. Kutcher GJ, Burman C, Brewster L (1991) Histogram reduction method for calculating complication probabilities for three-dimensional treatment planning evaluations. Int J Radiat Oncol Biol Phys 21:137–146PubMedGoogle Scholar
  32. Larson DA, Flickinger JC, Loeffler JS (1993) The radiobiology of radiosurgery. Int J Radiat Oncol Biol Phys 25:557–561PubMedGoogle Scholar
  33. Lax I, Karlsson B (1996) Prediction of complications in gamma knife radiosurgery of ateriovenous malformations. Acta Oncol 35:49–55PubMedCrossRefGoogle Scholar
  34. Ling CC, Chui CS (1993) Stereotactic treatment of brain tumors with radioactive implants or external photon beams: radiobiophysical aspects. Radiother Oncol 26:11–18PubMedGoogle Scholar
  35. Lyman JT (1985) Complication probability as assessed from dose-volume-histograms. Radiat Res 104:S13–S19Google Scholar
  36. Lyman JT, Wolbarst AB (1987) Optimization of radiation therapy III: a method of assessing complication probabilities from dose volume histograms. Int J Radiat Oncol Biol Phys 13:103–109PubMedGoogle Scholar
  37. Lyman JT, Wolbarst AB (1989) Optimization of radiation therapy IV: a dose volume histogram reduction algorithm. Int J Radiat Oncol Biol Phys 17:433–436PubMedGoogle Scholar
  38. Niemierko A (1998) Radiobiological models of tissue response to radiation in treatment planning systems. Tumori 84:140–143PubMedGoogle Scholar
  39. Niemierko A, Goitein M (1991) Calculation of normal tissue complication probability and dose-volume histogram reduction schemes for tissue with critical element architecture. Radiother Oncol 20:166–176PubMedCrossRefGoogle Scholar
  40. Niemierko A, Goitein M (1993a) Modelling of normal tissue response to radiation: the critical volume model. Int J Radiat Oncol Biol Phys 25:135–145PubMedGoogle Scholar
  41. Niemierko A, Goitein M (1993b) Implementation of a model for estimating tumor control probability for an inhomogeneously irradiated tumor. Radiother Oncol 29:140–147PubMedCrossRefGoogle Scholar
  42. Nilsson P, Thames HD, Joiner MC (1990) A generalized formulation of the incomplete-repair model for cell survival and tissue response to fractionated low dose-rate irradiation. Int J Radiat Biol 57:127–142PubMedGoogle Scholar
  43. Paganetti H (2003) Significance and implementation of RBE variations in proton beam therapy. Technol Cancer Res Treat 2:413–426PubMedGoogle Scholar
  44. Pop LAM, van den Broek JFCM, Visser AG, van der Kogel AJ (1996) Constraints in the use of repair half time and mathematical modelling for the clinical application of HDR and PDR treatment schedules as an alternative for LDR brachytherapy. Radiother Oncol 38:153–162PubMedCrossRefGoogle Scholar
  45. Prasad SC (1992) Linear quadratic model and biologically equivalent dose for single fraction treatments. Med Dosim 17:101–102PubMedGoogle Scholar
  46. Roberts SA, Hendry JH (1998) A realistic closed-form radiobiological model of clonical tumor-control data incorporating intertumor heterogeneity. Int J Radiat Oncol Biol Phys 41:689–699PubMedGoogle Scholar
  47. Sanchez-Nieto B, Nahum AE (1999) The delta-TCP concept: a clinically useful measure of tumor control probability. Phys Med Biol 44:369–380Google Scholar
  48. Scholz M, Kraft G (1994) Calculation of heavy ion inactivation probabilities based on track structure, X-ray sensitivity and target size. Radiat Proton Dosim 52:29–33Google Scholar
  49. Scholz M, Kellerer AM, Kraft-Weyrather G et al. (1997) Computation of cell survival in heavy ion beams for therapy. The model and its approximation. Radiat Environ Biophys 36:59–66PubMedCrossRefGoogle Scholar
  50. Schultheiss TE, Orton CG, Peck RA (1983) Models in radiotherapy: volume effects. Med Phys 10:410–415PubMedCrossRefGoogle Scholar
  51. Schultheiss TE, Zagars GK, Peters LJ (1987) An explanatory hypothesis for early-and late-effect parameter values in the LQ model. Radiother Oncol 9:241–248PubMedCrossRefGoogle Scholar
  52. Thames HD (1985) An “incomplete-repair” model for survival after fractionated and continuous irradiations. Int J Radiat Biol 47:319–339Google Scholar
  53. Thames HD, Bentzen SM, Turesson I et al. (1989) Fractionation parameters for human tissues and tumors. Int J Radiat Biol 56:701–710PubMedGoogle Scholar
  54. Thames HD, Withers HR, Peters LJ et al. (1982) Changes in early and late responses with altered dose fractionation: implications for dose survival relationships. Int J Radiat Oncol Biol Phys 8:219–226PubMedGoogle Scholar
  55. Tsujii H, Morita S, Miyamoto T et al. (2002) Experiences of carbon ion radiotherapy at NIRS. In: Kogelnik HD, Lukas P, Sedlmayer F (eds) Progress in radio-oncology, vol 7. Monduzzi Editore, Bologna, pp 393–405Google Scholar
  56. Ulmer W (1986) Some aspects of the chronological dose distribution in the radiobiology and radiotherapy. Strahlenther Onkol 162:374–385PubMedGoogle Scholar
  57. Van Vliet-Vroegindeweij C, Wheeler F, Stecher-Rasmussen F et al. (2001) Microdosimetry model for Boron neutron capture therapy. Part II. Theoretical estimation of the effectiveness function and surviving fractions. Radiat Res 155:498–502PubMedGoogle Scholar
  58. Wambersie A, Menzel HG (1993) RBE in fast neutron therapy and boron neutron capture therapy. A useful concept or a misuse. Strahlenther Oncol 169:57–64Google Scholar
  59. Webb S, Nahum AE (1993) A model for calculating tumor control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cells. Phys Med Biol 38:653–666PubMedGoogle Scholar
  60. Wilkens JJ, Oelfke U (2003) Analytical linear energy transfer calculations for proton therapy. Med Phys 30:806–815PubMedCrossRefGoogle Scholar
  61. Withers HR (1986) Predicting late normal tissue responses. Int J Radiat Oncol Biol Phys 12:693–698PubMedGoogle Scholar
  62. Withers HR (1992) Biologic basis of radiation therapy. In: Perez CA, Brady LW, (eds) Principles and practice of radiation oncology, 2nd edn. Lippincott, Philadelphia, pp 64–96Google Scholar
  63. Withers HR, Taylor JMG, Maciejewski B (1988) Treatment volume and tissue tolerance. Int J Radiat Oncol Biol Phys 14:751–759PubMedGoogle Scholar
  64. Wolbarst AB (1984) Optimization of radiation therapy. Part II. The critical-voxel model. Int J Radiat Oncol Biol Phys 10:741–745PubMedGoogle Scholar
  65. Wolbarst AB, Chin LM, Svenson GK (1982) Optimization of radiation therapy: integral-response of a model biological system. Int J Radiat Oncol Biol Phys 8:1761–1769PubMedGoogle Scholar
  66. Yashkin PN, Silin DI, Zolotov VA et al. (1995) Relative biological effectiveness of proton medical beam at Moscow synchrotron determined by the Chinese hamster cells assay. Int J Radiat Oncol Biol Phys 31:535–540PubMedCrossRefGoogle Scholar
  67. Yorke ED, Kutscher GJ, Jackson A et al. (1993) Probability of radiation induced complications in normal tissues with parallel architecture under conditions of uniform whole or partial organ irradiation. Radiother Oncol 26:226–237PubMedCrossRefGoogle Scholar
  68. Zamenhof R, Redmond E, Solares G et al. (1996) Monte-Carlo based treatment planning for Boron neutron capture therapy using custom designed models automatically generated from CT data. Int J Radiat Oncol Biol Phys 35:383–397PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian P. Karger
    • 1
  1. 1.Abteilung Medizinische Physik in der StrahlentherapieDeutsches KrebsforschungszentrumHeidelbergGermany

Personalised recommendations