Radiotherapy & Oncology
Volume 86, Issue 1 , Pages 48-54 , January 2008

Clinical implications of the implementation of advanced treatment planning algorithms for thoracic treatments

  • Andrew M. Morgan

      Affiliations

    • Medical Physics and Engineering, Leeds Teaching Hospitals, University of Leeds, UK
    • ,
  • Tommy Knöös

      Affiliations

    • Radiation Physics, Lund University Hospital, Sweden
    • Corresponding Author InformationCorresponding author. Tommy Knöös, Radiation Physics, Lund University Hospital, Klinikgatan 7, S-221 85 Lund, Sweden.
    • ,
  • Stuart G. McNee

      Affiliations

    • Radiotherapy Physics, The Beatson West of Scotland Cancer Centre, Glasgow, UK
    • ,
  • Chris J. Evans

      Affiliations

    • Medical Physics and Engineering, Leeds Teaching Hospitals, University of Leeds, UK
    • ,
  • David I. Thwaites

      Affiliations

    • Medical Physics and Engineering, Leeds Teaching Hospitals, University of Leeds, UK

Received 21 September 2007 ,Revised 28 November 2007 ,Accepted 28 November 2007.

References 

  1. Knöös T, Wieslander E, Cozzi L, et al. Comparison of dose calculation algorithms for treatment planning in external photon beam therapy for clinical situations. Phys Med Biol. 2006;51:5785–5807
  2. American Association of Medical Physics, Report 85, Tissue inhomogeneity corrections for megavoltage photon beams, 2004, ISBN 1-888340-47-9.
  3. Nisbet A, Beange I, Vollmar HS, Irvine C, Morgan A, Thwaites DI. Dosimetric verification of a commercial collapsed cone algorithm in simulated clinical situations. Radiother Oncol. 2004;73:79–88
  4. Irvine C, Morgan A, Crellin A, Nisbet A, Beange I. The clinical implications of the collapsed cone planning algorithm. Clin Oncol (R Coll Radiol). 2004;16:148–154
  5. Koelbl O, Krieger T, Haedinger U, Sauer O, Flentje M. Influence of calculation algorithm on dose distribution in irradiation of non-small cell lung cancer (NSCLC) collapsed cone versus pencil beam. Strahlenther Onkol. 2004;180:783–788
  6. Haedinger U, Krieger T, Flentje M, Wulf J. Influence of calculation model on dose distribution in stereotactic radiotherapy for pulmonary targets. Int J Radiat Oncol Biol Phys. 2005;61:239–249
  7. Fogliata A, Vanetti E, Albers D, et al. On the dosimetric behaviour of photon dose calculation algorithms in the presence of simple geometric heterogeneities: comparison with Monte Carlo calculations. Phys Med Biol. 2007;52:1363–1385
  8. Miften M, Wiesmeyer M, Monthofer S, Krippner K. Implementation of FFT convolution and multigrid superposition models in the FOCUS RTP system. Phys Med Biol. 2000;45:817–833
  9. Rogers DW, Faddegon BA, Ding GX, Ma CM, We J, Mackie TR. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Med Phys. 1995;22:503–524
  10. Kawrakow I. Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. Med Phys. 2000;27:485–498
  11. Evans C, Thwaites DI, Morgan A, Weston S. Monte Carlo modelling with BEAMnrc of the Elekta Beam Modulator for small field use. Radiother Oncol. 2007;84:S228
  12. Thwaites DI, Ambolt P, Bowden J, Cooper M, Evans C, Mcormack S, et al. Commissioning a superposition photon dose calculation mdel against measurement and Monte Carlo and some consequences for lung planning of changing TPS algorithms. Radiother Oncol. 2007;84(Suppl 1):S48
  13. De Jaeger K, Hoogeman MS, Engelsman M, Seppenwoolde Y, Damen EMF, Mijnheer BJ, et al. Incorporating an improved dose-calculation algorithm in conformal radiotherapy of lung cancer: re-evaluation of dose in normal lung tissue. Radiother Oncol. 2003;69:1–10
  14. Miften MM, Beavis AW, Marks LB. Influence of dose calculation model on treatment plan evaluation in conformal radiotherapy: a three-case study. Med Dosim. 2002;27:51–57
  15. Emami B, Lyman J, Brown A, et al. Tolerance of normal tissue to therapeutic radiation. Int J Radiat Oncol Biol Phys. 1991;21:109–122
  16. Hope AJ, Lindsay PE, El Naqa I, Alaly JR, Vicic M, Bradley JD, et al. Modelling radiation pneumonitis risk with clinical, dosimetric and special parameters. Int J Radiat Oncol Biol Phys. 2006;65:112–124
  17. Tsujino K, Hirota S, Kotani Y, Kado T, Yoden E, Fujii O, et al. Radiation pneumonitis following concurrent accelerated hyperfractionated radiotherapy and chemotherapy for limited-stage small-cell lung cancer: dose-volume histogram analysis and comparison with conventional chemoradiation. Int J Radiat Oncol Biol Phys. 2006;64:1100–1105
  18. Kong FM, Pan C, Eisbruch A, Ten Haken RK. Physical models and simpler dosimetric descriptors of radiation late toxicity. Semin Radiat Oncol. 2007;17:108–120
  19. Seppenwoolde Y, Lebesque JV. Partial irradiation of the lung. Semin Radiat Oncol. 2001;11:247–258
  20. Madani I, De Ruyck K, Geominne H, De Neve W, Thierens H. Van Meerbeeck, J. Predicting risk of radiation-induced lung injury. J Thorac Oncol. 2007;2:864–874
  21. Yorke ED, Jackson A, Rozenzweig KE, Brabab L, Liebel SA, Ling CC. Correlation of dosimetric factors and radiation pneumonitis for non-small-cell lung cancer patients in a recently completed dose escalation study. Int J Radiat Oncol Biol Phys. 2005;63:672–682
  22. Graham MV, Purdy JA, Emami B, Harms W, Bosch W, Lockett MA, et al. Clinical dose-volume histogram analysis after 3D treatment for non-small cell lung cancer (NSCLC). Int J Radiat Oncol Biol Phys. 1999;45:323–329
  23. Rodrigues G, Lock M, D’Souza D, Van Dyk J. Prediction of radiation pneumonitis by dose-volume histogram parameters in lung cancer – a systematic review. Radiother Oncol. 2004;71:127–138
  24. Senan S, De Ruyscher D, Giraud P, Mirimanoff R, Budach V. Literature-based recommendations for treatment planning and execution in high-dose radiotherapy for lung cancer. Radiother Oncol. 2004;71:139–146
  25. Chang D, Lui C, Dempsey JF, Palta JR, Louis D, Morris C, et al. Predicting changes in dose distribution to tumour and normal tissue when correcting for heterogeneity in radiotherapy for lung cancer. Am J Clin Oncol. 2007;30:57–62
  26. Schallenkamp JM, Miller RC, Brinkman DH, Foote T, Garces YI. Incidence of radiation pneumonitis after thoracic irradiation: dose volume correlates. Int J Radiat Oncol Biol Phys. 2007;67:410–416
  27. McNee SG, Morgan A, Maguire J, Thwaites D, Snee M, McMenemin R. Effect of calculation algorithms on dose prescription and planning for lung radiotherapy. Lung Cancer. 2007;57:S8–S9
  28. Madani M, Vanderstraeten B, Bral S, Coghe M, De Gersem W, De Wagter C, et al. Comparison of 6MV and 18MV photons for IMRT treatment of lung cancer. Radiother Oncol. 2007;82:63–69
  29. Seppenwoolde Y, De Jaeger K, Boersma LJ, Belderbos JS, Lebesque JV. Regional differences in lung radiosensitivity after radiotherapy for non-small-cell lung Cancer. Int J Radiat Oncol Biol Phys. 2004;60:748–758
  30. Jackson A, Yorke ED, Rosenzweig KE. The atlas of complication incidence: a proposal for a new standard for reporting the results of radiotherapy protocols. Semin Radiat Oncol. 2006;16:260–268Review
  31. Siebers VJ, Keall JP, Nahum EA, Mohan R. Converting absorbed dose to medium to absorbed dose to water for Monte Carlo based photon beam dose calculations. Phys Med Biol. 2000;45:983–995
  32. Dogan N, Siebers JV, Keall PJ. Clinical comparison of head and neck and prostate IMRT plans using absorbed dose to medium and absorbed dose to water. Phys Med Biol. 2006;51:4967–4980

PII: S0167-8140(07)00633-0

doi: 10.1016/j.radonc.2007.11.033

Radiotherapy & Oncology
Volume 86, Issue 1 , Pages 48-54 , January 2008

Article Tools

* Image Usage & Resolution