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The use and QA of biologically related models for treatment planning: Short report of the TG-166 of the therapy physics committee of the AAPMa)
1. C. C. Ling and X. A. Li, “Over the next decade the success of radiation treatment planning will be judged by the immediate biological response of tumor cells rather than by surrogate measures such as dose maximization and uniformity,” Med. Phys. 32, 2189–2192 (2005).
2. M. V. Graham, J. A. Purdy, B. Emami, W. Harms, W. Bosch, M. A. Lockett, and C. A. Perez, “Clinical dose-volume histogram analysis for pneumonitis after 3D treatment for non-small lung cancer (NSCLC),” Int. J. Radiat. Oncol., Biol., Phys. 45, 323–329 (1999).
3. L. B. Marks, S. M. Bentzen, J. O. Deasy, F. M. Kong, J. D. Bradley, I. S. Vogelius, I. El Naqa, J. L. Hubbs, J. V. Lebesque, R. D. Timmerman, M. K. Martel, and A. Jackson, “Radiation dose-volume effects in the lung,” Int. J. Radiat. Oncol., Biol., Phys. 76(3 Suppl), S70–S76 (2010).
4. J. O. Deasy, “Multiple local minima in radiotherapy optimization problems with dose-volume constraints,” Med. Phys. 24, 1157–1161 (1997).
6. R. D. Stewart and X. A. Li, “BGRT: Biologically guided radiation therapy—The future is fast approaching!,” Med. Phys. 34, 3739–3751 (2007).
8. C. C. Ling, J. Humm, S. Larson, H. Amols, Z. Fuks, S. Leibel, and J. A. Koutcher, “Towards multidimensional radiotherapy (MD-CRT): Biological imaging and biological conformality,” Int. J. Radiat. Oncol., Biol., Phys. 47, 551–560 (2000).
9. Y. Yang and L. Xing, “Towards biologically conformal radiation therapy (BCRT): Selective IMRT dose escalation under the guidance of spatial biology distribution,” Med. Phys. 32, 1473–1484 (2005).
12. A. Søvik, E. Malinen, H. K. Skogmo, S. M. Bentzen, O. S. Bruland, and D. R. Olsen, “Radiotherapy adapted to spatial and temporal variability in tumor hypoxia,” Int. J. Radiat. Oncol., Biol., Phys. 68, 1496–1504 (2007).
14. A. Niemierko, “Reporting and analyzing dose distributions: A concept of equivalent uniform dose,” Med. Phys. 24, 103–110 (1997).
15. A. Niemierko, “A generalized concept of equivalent uniform dose (EUD),” Med. Phys. 26, 1101 (1999).
18. M. Alber and R. Reemtsen, “Intensity modulated radiation therapy planning by use of a barrier-penalty multiplier method,” Optimization, Methods and Software (OMS) (Taylor & Francis, London, 2007), Vol. 22, pp. 391–411.
19. Q. Wu, D. Djajaputra, Y. Wu, J. Zhou, H. H. Liu, and R. Mohan, “Intensity-modulated radiotherapy optimization with gEUD-guided dose-volume objectives,” Phys. Med. Biol. 48, 279–291 (2003).
20. D. N. Mihailidis, B. Plants, L. Farinash, M. Harmon, L. Whaley, P. Paja, and P. Tomara, “Superiority of equivalent uniform dose (EUD)-based optimization for breast and chest wall,” Med. Dosim. 35, 67–76 (2010)
21. P. Källman, B. K. Lind, and A. Brahme, “An algorithm for maximizing the probability of complication-free tumour control in radiation therapy,” Phys. Med. Biol. 37, 871–890 (1992).
22. Q. Wu, R. Mohan, A. Niemierko, and R. Schmidt-Ullrich, “Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose,” Int. J. Radiat. Oncol., Biol., Phys. 52, 224–235 (2002).
23. W. R. De Gersem, S. Derycke, C. O. Colle, C. De Wagter, and W. J. De Neve, “Inhomogeneous target-dose distributions: A dimension more for optimization?,” Int. J. Radiat. Oncol., Biol., Phys. 44, 461–468 (1999).
24. QUANTEC, Int. J. Radiat. Oncol. Biol. Phys. 76(3 Suppl) (2010).
25. S. H. Benedict, K. M. Yenice, D. Followill, J. M. Galvin, W. Hinson, B. Kavanagh, P. Keall, M. Lovelock, S. Meeks, L. Papiez, T. Purdie, R. Sadagopan, M. C. Schell, B. Salter, D. J. Schlesinger, A. S. Shiu, T. Solberg, D. Y. Song, V. Stieber, R. Timmerman, W. A. Tomé, D. Verellen, L. Wang, and F. F. Yin, “Stereotactic body radiation therapy: The report of AAPM Task Group 101,” Med Phys. 37, 4078–4101 (2010).
26. I. Kawrakow, “The effect of Monte Carlo statistical uncertainties on the evaluation of dose distributions in radiation treatment planning,” Phys. Med. Biol. 49, 1549–1556 (2004).
27. R. K. Ten Haken, T. S. Lawrence, and L. A. Dawson, “Prediction of radiation-induced liver disease by Lyman normal-tissue complication probability model in three-dimensional conformal radiation therapy for primary liver carcinoma: In regards to Xu et al. (Int J Radiat Oncol Biol Phys 2006;65:189-195),” Int. J. Radiat. Oncol., Biol., Phys. 66, 1272 (2006).
28. E. S. Koh, A. Sun, T. H. Tran, R. Tsang, M. Pintilie, D. C. Hodgson, W. Wells, R. Heaton, and M. K. Gospodarowicz, “Clinical dose-volume histogram analysis in predicting radiation pneumonitis in Hodgkin’s lymphoma,” Int. J. Radiat. Oncol., Biol., Phys. 66, 223–228 (2006).
30. G. J. Kutcher and C. Burman, “Calculation of complication probability factors for non-uniform normal tissue irradiation: The effective volume method,” Int. J. Radiat. Oncol., Biol., Phys. 16, 1623–1630 (1989).
31. L. A. Dawson, D. Normolle, J. M. Balter, C. J. McGinn, T. S. Lawrence, and R. K. Ten Haken, “Analysis of radiation-induced liver disease using the Lyman NTCP model,” Int. J. Radiat. Oncol., Biol., Phys. 53, 810–821 (2002). Erratum in:
32. Y. Seppenwoolde, J. V. Lebesque, K. de Jaeger, J. S. Belderbos, L. J. Boersma, C. Schilstra, G. T. Henning, J. A. Hayman, M. K. Martel, and R. K. Ten Haken, “Comparing different NTCP models that predict the incidence of radiation pneumonitis. Normal tissue complication probability,” Int. J. Radiat. Oncol., Biol., Phys. 55, 724–735 (2003).
33. Z. Y. Xu, S. X. Liang, J. Zhu, X. D. Zhu, J. D. Zhao, H. J. Lu, Y. L. Yang, L. Chen, A. Y. Wang, X. L. Fu, and G. L. Jiang, “Prediction of radiation-induced liver disease by Lyman normal-tissue complication probability model in three-dimensional conformal radiation therapy for primary liver carcinoma,” Int. J. Radiat. Oncol., Biol., Phys. 65, 189–195 (2006).
35. V. Moiseenko, J. Battista, and J. Van Dyk, “Normal tissue complication probabilities: Dependence on choice of biological model and dose-volume histogram reduction scheme,” Int. J. Radiat. Oncol., Biol., Phys. 46, 983–993 (2000).
36. L. P. Muren, N. Jebsen, A. Gustafsson, and O. Dahl, “Can dose-response models predict reliable normal tissue complication probabilities in radical radiotherapy of urinary bladder cancer? The impact of alternative radiation tolerance models and parameters,” Int. J. Radiat. Oncol., Biol., Phys. 50, 627–637 (2001).
41. B. Emami, J. Lyman, A. Brown, L. Coia, M. Goitein, J. E. Munzenrider, B. Shank, L. J. Solin, and M. Wesson, “Tolerance of normal tissue to therapeutic irradiation,” Int. J. Radiat. Oncol., Biol., Phys. 21, 109–122 (1991).
42. V. A. Semenenko, B. Reitz, E. Day, X. S. Qi, M. Miften, and X. A. Li, “Evaluation of a commercial biologically based IMRT treatment planning system,” Med. Phys. 35, 5851–5860 (2008).
43. X. S. Qi, V. A. Semenenko, X. A. Li, “Improved critical structure sparing with biologically-based IMRT optimization,” Med. Phys. 36, 1790–1799 (2009).
44. G. J. Kutcher, L. Coia, M. Gillin, W. F. Hanson, S. Leibel, R. J. Morton, J. R. Palta, J. A. Purdy, L. E. Reinstein, G. K. Svensson, M. Weller, and L. Wingfield, “Comprehensive QA for radiation oncology: Report of AAPM Radiation Therapy Committee Task Group 40,” Med. Phys. 21, 581–618 (1994).
45. B. Fraass, K. Doppke, M. Hunt, G. Kutcher, G. Starkschall, R. Stern, and J. Van Dyke, “American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: Quality assurance for clinical radiotherapy treatment planning,” Med. Phys. 25, 1773–1829 (1998).
46. G. A. Ezzell, J. W. Burmeister, N. Dogan, T. J. Losasso, J. G. Mechalakos, D. Mihailidis, A. Molineu, J. R. Palta, C. R. Ramsey, B. J. Salter, J. Shi, P. Xia, N. J. Yue, and Y. Xiao, “IMRT commissioning: Multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119,” Med. Phys. 36, 5359–5373 (2009).
47. E. E. Klein, J. Hanley, J. Bayouth, F. F. Yin, W. Simon, S. Dresser, C. Serago, F. Aguirre, L. Ma, B. Arjomandy, C. Liu, C. Sandin, and T. Holmes, “Task Group 142 report: Quality assurance of medical accelerators,” Med Phys. 36, 4197–212 (2009).
48. J. O. Deasy, A. I. Blanco, and V. H. Clark, “CERR: A computational environment for radiotherapy research,” Med. Phys. 30, 979–985 (2003).
51. J. Uzan and A. E. Nahum, “BioSuite, new software for radiobiological customisation of dose and fraction size in external-beam radiothearapy,” 10th Biennial ESTRO meeting (2009).
56.ICRU, “Prescribing, recording and reporting photon beam therapy,” ICRU Report No. 50 (International Commission on Radiation Units and Measurements, Washington, DC, 1993).
57. J. H. Killoran, H. M. Kooy, D. J. Gladstone, F. J. Welte, and C. J. Beard, “A numerical simulation of organ motion and daily setup uncertainties: Implications for radiation therapy,” Int. J. Radiat. Oncol., Biol., Phys. 37, 213–221 (1997).
61. L. B. Marks, G. W. Sherouse, M. T. Munley, G. C. Bentel, and D. P. Spencer, “Incorporation of functional status into dose-volume analysis,” Med. Phys. 26, 196–9 (1999).
63. J. D. Cox, J. Stetz, and T. F. Pajak, “Toxicity criteria of the radiation therapy oncology group (RTOG) and the European organization for research and treatment of cancer (EORTC),” Int. J. Radiat. Oncol., Biol., Phys. 31, 1341–1346 (1995).
66. M. Alber, “A concept for the optimization of radiotherapy,” Ph.D. dissertation, University of Tübingen, Tübingen, Germany, 2000.
70. C. Burman, G. J. Kutcher, B. Emami, and M. Goitein, “Fitting of normal tissue tolerance data to an analytic function,” Int. J. Radiat. Oncol., Biol., Phys. 21, 123–135 (1991).
71. B. K. Lind, P. Mavroidis, S. Hyödynmaa, and C. Kappas, “Optimization of the dose level for a given treatment plan to maximize the complication-free tumor cure,” Acta Oncol. 38, 787–798 (1999).
72. G. Kåver, B. K. Lind, J. Löf, A. Liander, and A. Brahme, “Stochastic optimization of intensity modulated radiotherapy to account for uncertainties in patient sensitivity,” Phys. Med. Biol. 44, 2955–2969 (1999).
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Treatment planning tools that use biologically related models for plan optimization and/or evaluation are being introduced for clinical use. A variety of dose-responsemodels and quantities along with a series of organ-specific model parameters are included in these tools. However, due to various limitations, such as the limitations of models and available model parameters, the incomplete understanding of dose responses, and the inadequate clinical data, the use of biologically based treatment planningsystem (BBTPS) represents a paradigm shift and can be potentially dangerous. There will be a steep learning curve for most planners. The purpose of this task group is to address some of these relevant issues before the use of BBTPS becomes widely spread. In this report, the authors (1) discuss strategies, limitations, conditions, and cautions for using biologically based models and parameters in clinical treatment planning; (2) demonstrate the practical use of the three most commonly used commercially available BBTPS and potential dosimetric differences between biologically model based and dose-volume based treatment plan optimization and evaluation; (3) identify the desirable features and future directions in developing BBTPS; and (4) provide general guidelines and methodology for the acceptance testing, commissioning, and routine quality assurance (QA) of BBTPS.
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