Skip to main content
SpringerLink
Log in

Optimization of Radiotherapy Using Biological Parameters

  • Chapter
Radiation Oncology Advances

Part of the book series: Cancer Treatment and Research ((CTAR,volume 139))

  • 1152 Accesses

Most early stage cancers can be cured with either radiotherapy or surgery; however, tumor control for many locally advanced tumors has been unsatisfactory in spite of combined-modality treatments. It has been observed that local recurrence of tumors happens in high-risk tumor subvolumes such as hypoxic tumor subvolumes [1, 2] or regions that contain a large number of highly proliferation-capable clonogens [2, 3]. In an effort to overcome local recurrence, radiation dose boosting techniques have been widely studied. Clinical evidence exists that tumor control can be enhanced using boosting techniques for the following disease sites: head-and-neck (H & N) cancer [4–6], prostate cancer [7, 8], lung cancer [9–11], and liver cancer [12, 13].

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Thomlinson RH, Gray LH. The histological structure of some human lung cancers and the possible implications for radiotherapy. Br J Cancer 1955; 9:539–549.

    CAS  PubMed  Google Scholar 

  2. Weichselbaum RR, Beckett MA, Schwartz JL, Dritschilo A. Radioresistant tumor cells are present in head and neck carcinoma that recur after radiotherapy. Int J Radiat Oncol Biol Phys 1988; 15:575–579.

    CAS  PubMed  Google Scholar 

  3. Bentzen SM. Repopulation in radiation oncology: perspectives of clinical research. Int J Radiat Biol 2003; 79:581–585.

    Article  CAS  PubMed  Google Scholar 

  4. Marks JE, Bedwinek JM, Lee F, et al. Dose–response analysis for nasopharyngeal carcinoma: an historical perspective. Cancer 1982; 50:1042–1050.

    Article  CAS  PubMed  Google Scholar 

  5. Vikram B, Mishra UB, Strong EW, et al. Patterns of failure in carcinoma of the nasopharynx: I. Failure at the primary site. Int J Radiat Oncol Biol Phys 1985; 11:1455–1459.

    CAS  PubMed  Google Scholar 

  6. Bentzen SM. Radiobiological considerations in the design of clinical trials. Radiother Oncol 1994; 32:1–11.

    Article  CAS  PubMed  Google Scholar 

  7. Hanks GE, Hanlon AL, Schultheiss TE, et al. Dose escalation with 3D conformal treatment: five year outcomes, treatment optimization, and future directions. Int J Radiat Oncol Biol Phys 1998; 41:501–510.

    Article  CAS  PubMed  Google Scholar 

  8. Zelefsky MJ, Leibel SA, Gaudin PB, et al. Dose escalation with three-dimensional conformal radiation therapy affects the outcome in prostate cancer. Int J Radiat Oncol Biol Phys 1998; 41:491–500.

    Article  CAS  PubMed  Google Scholar 

  9. Kong FM, Ten Haken RK, Schipper MA, et al. High dose radiation improved local tumor control and overall survival in inoperable/unresectable non-small cell lung cancer: Longterm results from radiation dose escalation study. Int J Radiat Oncol Biol Phys 2005; 63:324–333.

    PubMed  Google Scholar 

  10. Willner J, Baier K, Caragiani E, et al. Dose, volume, and tumor control prediction in primary radiotherapy of non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2002; 52:382–389.

    PubMed  Google Scholar 

  11. Rengan R, Rosenzweig KE, Venkatraman E, et al. Improved local control with higher doses of radiation in large-volume stage III non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004; 60:741–747.

    PubMed  Google Scholar 

  12. Dawson LA, McGinn CJ, Grazia A, et al. Escalated focal liver radiation and concurrent hepatic artery fluorodeoxyuridine for unresectable intrahepatic malignancies. J Clin Oncol 2000; 18:2210–2218.

    CAS  PubMed  Google Scholar 

  13. Park HC, Seong J, Han KH, Dose–response relationship in local radiotherapy for hepatocellular carcinoma. Int J Radiat Oncol Biol Phys 2002; 54:150–155.

    Article  PubMed  Google Scholar 

  14. Wadsley JC, Bentzen SM. Investigation of relationship between change in locoregional control and change in overall survival in randomized controlled trials of modified radiotherapy in head-and-neck cancer. Int J Radiat Oncol Biol Phys 2004; 60:1405–1409.

    PubMed  Google Scholar 

  15. Thames HD, Schultheiss TE, Hendry JH, et al. Can modest escalations of dose be detected as increased tumor control? Int J Radiat Oncol Biol Phys 1992; 22:241–246.

    CAS  PubMed  Google Scholar 

  16. Bartelink H, Horiot JC, Poortmans P, et al. Recurrence rates after treatment of breast cancer with standard radiotherapy with or without additional radiation. N Eng J Med 2001; S345:1378–1387.

    Article  Google Scholar 

  17. Tomé WA, Fowler JF. Selective boosting of tumor subvolumes. Int J Radiat Oncol Biol Phys 2000; 48:593–599.

    PubMed  Google Scholar 

  18. Goitein M, Niemierko A, Okunieff P. The probability of controlling an inhomogeneously irradiated tumor. In: Kaulner K, Carey B, Crellin A, et al., editors. Quantitative imaging in oncology Proceedings of the 19th LH Gray Conference: London: British Institute of Radiology; 1995. p. 25–32.

    Google Scholar 

  19. Deasy J. Tumor control probability models for nonuniform dose distributions. In: Paliwal BR, Fowler JF, Herbert DE, et al., editors. Volume & Kinetics in Tumor Control & Normal Tissue Complication. Madison, WI: Medical Physics Publishing; 1997. p. 65–85.

    Google Scholar 

  20. Popple RA, Ove R, Shen S. Tumor control probability for selective boosting of hypoxic subvolumes, including the effect of reoxygenation. Int J Radiat Oncol Biol Phys 2002; 54:921–927.

    PubMed  Google Scholar 

  21. Hong TS, Tomé WA, Chappell RJ, et al. The impact of daily setup variations on head-and-neck intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2005; 61:779–788.

    PubMed  Google Scholar 

  22. Vanderstraeten B, Duthoy W, De Gersem W, et al. [18F]fluoro-deoxy-glucose positron emission tomography ([18F]FDG-PET) voxel intensity-based intensity-modulated radiation therapy (IMRT) for head and neck cancer. Radiother Oncol 2006; 79:249–258.

    Article  CAS  PubMed  Google Scholar 

  23. De Meerleer G, Villeirs G, Bral S, et al. The manetic resonance detected intraprostatic lesion in prostate cancer: planning and delivery of intensity-modulated radiotherapy. Radiother Oncol 2005; 75:325–333.

    Article  PubMed  Google Scholar 

  24. Chao KSC, Bosch WR, Mutic S, Lewis JS, et al. A novel approach to overcome hypoxic tumour resistance: Cu-ATSM-guided intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 2001; 49:1171–1182.

    Article  CAS  PubMed  Google Scholar 

  25. van Lin EN, Fütterer JJ, Heijmink SW, et al. IMRT boost dose planning on dominant intraprostatic lesions: gold marker-based three-dimensional fusion of CT with dynamic contrast-enhanced and 1H-spectroscopic MRI. Int J Radiat Oncol Biol Phys 2006; 65:291–303.

    PubMed  Google Scholar 

  26. De Ruysscher D, Wanders S, Minken A, et al. Effects of radiotherapy planning with a dedicated combined PET-CT-simulator of patients with non-small cell lung cancer on dose limiting normal tissues and radiation dose-escalation: a planning study. Radiother Oncol 2005; 77:5–10.

    Article  PubMed  Google Scholar 

  27. Xing L, Cotrutz C, Hunjan S, et al. Inverse planning for functional image-guided intensity-modulated radiation therapy. Phys Med Biol 2002; 47:3567–3578.

    Article  PubMed  Google Scholar 

  28. Bentzen SM. Theragnostic imaging for radiation oncology: dose-painting by numbers. Lancet Oncol 2005; 6:112–117.

    Article  PubMed  Google Scholar 

  29. Källman P, Lind BK, Brahme A. An algorithm for maximizing the probability of complication free tumor control in radiation therapy. Phys Med Biol 1992; 37:871–890.

    Article  PubMed  Google Scholar 

  30. Söderström S, Gustafsson A, Brahme A. Optimization of the dose delivery in few field techniques using radiobiological objective functions. Med Phys 1993; 20:1201–1210.

    Article  Google Scholar 

  31. Kim Y, Tomé WA. Risk-adaptive optimization: selective boosting of high-risk tumor subvolumes. Int J Radiat Oncol Biol Phys 2006; 66:1528–1542.

    PubMed  Google Scholar 

  32. Munro TR, Gilbert CW. The relation between tumour lethal doses and the radiosensitivity of tumour cells. Br J Radiol 1961; 34:246–251.

    Article  CAS  PubMed  Google Scholar 

  33. Holthusen H. Erfahrungen über die Verträglichkeitsgrenze für Röntgenstrahlen und deren Nutzanwendung zur Verhütung von Schäden. Strahlentherapie 1936; 57:254–269.

    Google Scholar 

  34. Tanderup K, Olsen DR, Grau C. Dose painting: art or science? Radiother Oncol 2006; 79: 245–248.

    Article  PubMed  Google Scholar 

  35. Thorwarth D, Eschmann SM, Scheiderbauer J, et al. Kinetic analysis of dynamic 18F-fluoromisonidazole PET correlates with radiation treatment outcome in head-and-neck cancer. BMC Cancer 2005; 5:152.

    Article  PubMed  Google Scholar 

  36. Bentzen SM, Tucker SL. Quantifying the position and steepness of radiation dose–response curves. Int J Radiat Biol 1997; 71:531–542.

    Article  CAS  PubMed  Google Scholar 

  37. Kutcher GJ, Burman C. Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. Int J Radiation Oncology Biol Phys 1989; 16:1623–1630.

    CAS  Google Scholar 

  38. Niemierko A. A generalized concept of equivalent uniform dose (EUD). Med Phys 1999; 26:1100.

    Google Scholar 

  39. Niemierko A. Reporting, and analyzing dose distributions: a concept of equivalent uniform dose. Med Phys 1997; 24:103–110.

    Article  CAS  PubMed  Google Scholar 

  40. Kreyszig E. Advanced Engineering Mathematics. Sixth edition, John Wiley & Sons, New York, USA. 1985.

    Google Scholar 

  41. Suit HD, Shalek RJ, Wette R. Radiation response of C3H mouse mammary carcinoma evaluated in terms of cellular radiation sensitivity. In Cellular Radiation Biology. Baltimore. Williams & Wilkins 1965. pp 514–530.

    Google Scholar 

  42. Rancati T, Fiorino C, Gagliardi G, et al. Fitting late rectal bleeding data using different NTCP models: results from an Italian multi-centric study (AIROPROS0101). Radiother Oncol 2004; 73: 21–32.

    Article  CAS  PubMed  Google Scholar 

  43. Lyman JT. Complication probability as assessed from dose–volume histogram. Radiat Res Suppl 1985; 8:S13–S19.

    Article  CAS  PubMed  Google Scholar 

  44. Fowler J. Normal tissue complication probabilities: how well do the models work? Phys Med 2001; XVII:24–34.

    Google Scholar 

  45. Ågren AK, Brahme A, Turesson I. Optimization of uncomplicated control for head and neck tumors. Int J Radiat Oncol Biol Phys 1990; 19:1077–1085.

    PubMed  Google Scholar 

  46. Romeijn HE, Dempsey JF, Li JG. A unifying framework for multi-criteria fluence map optimization models. Phys Med Biol 2004; 49:1991–2013.

    Article  PubMed  Google Scholar 

  47. American Cancer Society. Cancer Facts and Figures 2005. Atlanta: American Cancer Society; 2005.

    Google Scholar 

  48. Cellini N, Morganti AG, Mattiucci GC, et al. Analysis of intraprostatic failures in patients treated with hormonal therapy and radiotherapy: implications for conformal therapy planning. Int J Radiat Oncol Biol Phys 2002; 53:595–599.

    PubMed  Google Scholar 

  49. Kuban DA, El-Mahdi AM, Schellhammer PF. Potential benefit of improved local tumor control in patient with prostate carcinoma. Cancer 1995; 75:2373–2382.

    Article  CAS  PubMed  Google Scholar 

  50. Zagars GK, von Eschenbach AC, Ayala AG, et al. The influence of local control on metastatic dissemination of prostate cancer treated by external beam megavoltage radiation therapy. Cancer 1991; 68:2370–2377.

    Article  CAS  PubMed  Google Scholar 

  51. Fuks Z, Leibel SA, Wallner KE, et al. The effect of local control on metastatic dissemination in carcinoma of the prostate: long-term results in patients treated with 125-I implantation. Int J Radiat Oncol Biol Phys 1991; 21:537–547.

    CAS  PubMed  Google Scholar 

  52. Fowler J, Chappell R, Ritter MA. Is alpha/beta for prostate tumors really low? Int J Radiation Oncology Biol Phys 2001; 50:1021–1031.

    Article  CAS  Google Scholar 

  53. Bentzen SM, Ritter MA. The alpha/beta ratio for prostate cancer: What is it, really? Radiother Oncol 2005; 76:1–3.

    Article  PubMed  Google Scholar 

  54. Zagars GK, Schultheiss TE, Peters LP. Inter-tumor heterogeneity and radiation dose control curves. Radiother Oncol 1987; 8:353–362.

    Article  CAS  PubMed  Google Scholar 

  55. Levegrün SA, Jackson MJ, Zelefsky ES, et al. Risk group dependence of dose–response for biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer. Radiother. Oncol. 2002; 63:11–26.

    Article  PubMed  Google Scholar 

  56. Gregoire V. Is there any future in radiotherapy planning without the use of PET: unraveling the myth. Radiother Oncol 2004; 73:261–263.

    Article  PubMed  Google Scholar 

  57. Antoch G, Saoudi N, Kuehl H, et al. Accuracy of whole-body dual-modality fluorine-18–2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (FDG-PET/CT) for tumor staging in solid tumors: comparison with CT and PET. J Clin Oncol 2004; 22:4357–4368.

    Article  PubMed  Google Scholar 

  58. Apisarnthanarax S, Chao KSC. Current imaging paradigms in radiation oncology. Radiat Res 2005; 163:1–25.

    Article  CAS  PubMed  Google Scholar 

  59. Jerusalem G, Hustinx R, Beguin Y, et al. PET scan imaging in oncology. Eur J Cancer 2003; 39:1525–1534.

    Article  CAS  PubMed  Google Scholar 

  60. Gambhir SS, Czernin J, Schwimmer DH, et al. A tabulated summary of the FDG PET literature. J Nucl Med 2001; 42:1S–93S.

    CAS  PubMed  Google Scholar 

  61. Rose PG, Adler LP, Rodriguez M, et al. Positron emission tomography for evaluating paraaortic nodal metastasis in locally advanced cervical cancer before staging: a surgicopathologic study. J Clin Oncol 1999; 17:41–45.

    CAS  PubMed  Google Scholar 

  62. Pieterman RM, van Putten JW, Meuzelaar JJ, et al. Preoperative staging of non-small-cell lung cancer with positron-emission tomography. N Engl J Med 2000; 343:254–261.

    Article  CAS  PubMed  Google Scholar 

  63. Staib L, Schirrmeister H, Reske SN, Beger HG. Is 18F-fluorodeoxyglucose positron emission tomography in recurrent colorectal cancer a contribution to surgical decision making? Am J Surg 2000; 180:1–5.

    Article  CAS  PubMed  Google Scholar 

  64. Van de Wiele C, Lahorte C, Oyen W, et al. Nuclear medicine imaging to predict response to radiotherapy: a review. Int J Radiation Oncology Biol Phys 2003; 55:5–15.

    Article  Google Scholar 

  65. Brizel DM, Scully SP, Harrelson JM, et al. Tumor oxygenation predicts for the likelihood of distant metastases in human soft tissue sarcoma. Cancer Res 1996; 56:941–943.

    CAS  PubMed  Google Scholar 

  66. Shields AF, Grierson JR, Dohmen BM, et al. Imaging proliferation in vivo with 18F FLT and positron emission tomography. Nat Med 1998; 4:1334–1336.

    Article  CAS  PubMed  Google Scholar 

  67. Alber M, Paulsen F, Eschmann SM, Machulla HJ. On biologically conformal boost dose optimization. Phys Med Biol 2003; 48:N31–N35.

    Article  CAS  PubMed  Google Scholar 

  68. Das SK, Miften MM, Zhou S, et al. Feasibility of optimizing the dose distribution in lung tumours using fluorine-18-fluorodeoxyglucose positron emission tomography and single photon emission computed tomography guided dose prescriptions. Med Phys 2004; 31:1452–1461.

    Article  CAS  PubMed  Google Scholar 

  69. Šámal M, Statistical decision theory. In regional training workshop, advanced image processing of SPECT studies, Tygerberg Hospital. 2004.

    Google Scholar 

  70. Burman CM, Zelefsky MJ, Leibel SA. Treatment planning, dose delivery, and outcome of IMRT For localized prostate cancer. In: Samuel Hellman et al., A Practical Guide To Intensity-Modulated Radiation Therapy. Madison, WI: Medical Physics Publishing; 2003. p. 169–190.

    Google Scholar 

  71. Tomé WA, Fowler JF. On cold spots in tumor subvolumes. Med Phys 2002; 29:1590–1598.

    Article  PubMed  Google Scholar 

  72. Burman C, Kutcher GJ, Emami B, et al. Fitting of normal tissue tolerance data to an analytic function. Int J Radiat Oncol Biol Phys 1991; 21:123–135.

    CAS  PubMed  Google Scholar 

  73. Dawson LA, Normolle D, Balter JM, et al. Analysis of radiation-induced liver disease using the Lyman NTCP model. Int J Radiat Oncol Biol Phys 2002; 53:810–821.

    PubMed  Google Scholar 

  74. Dawson LA, Ten Haken RK. Partial volume tolerance of the liver to radiation. Semin Radiat Oncol 2005; 15:279–283.

    Article  PubMed  Google Scholar 

  75. Rodrigues G, Lock M, D’Souza D, et al. Prediction of radiation pneumonitis by dose–volume histogram parameters in lung cancer-a systematic review. Radiother Oncol 2004; 71:127–138.

    Article  PubMed  Google Scholar 

  76. Hope AJ, Lindsay PE, Naqa IE, et al. Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters. Int J Radiat Oncol Biol Phys 2006; 65:112–124.

    PubMed  Google Scholar 

  77. Chapet O, Kong F, Lee JS, et al. Normal tissue complication probability modeling for acute esophagitis in patients treated with conformal radiation therapy for non-small cell lung cancer. Radiother Oncol 2005; 77:176–181.

    Article  PubMed  Google Scholar 

  78. Okunieff P, Morgan D, Niemierko A, Suit HD, Radiation dose–response of human tumors. Int J Radiat Oncol Biol Phys 1995; 32:1227–1237.

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, K4/310, 53792, Madison, WI, USA

    Yusung Kim Ph.D & Associate Professor Wolfgang A. Tomé M.D., Ph.D

Authors
  1. Yusung Kim Ph.D
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Associate Professor Wolfgang A. Tomé M.D., Ph.D
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, K4/310, 53792, Madison, WI, USA

    Søren M. Bentzen Ph.D., D.Sc , Paul M. Harari M.D , Wolfgang A. Tomé M.D., Ph.D  & Minesh P. Mehta M.D , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Kim, Y., Tomé, W.A. (2008). Optimization of Radiotherapy Using Biological Parameters. In: Bentzen, S.M., Harari, P.M., Tomé, W.A., Mehta, M.P. (eds) Radiation Oncology Advances. Cancer Treatment and Research, vol 139. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36744-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-36744-6_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-36743-9

  • Online ISBN: 978-0-387-36744-6

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation