Optimization of Treatment Plans, Inverse Planning

  • Thomas Bortfeld
  • Christian Thieke
Part of the Medical Radiology book series (MEDRAD)

17.5 Conclusion

Optimized inverse planning can yield superior treatment plans, especially in complex situations with convex-concave target volumes and nearby critical structures; however,the optimization criteria must be carefully chosen. Determining appropriate optimization criteria is not straightforward and requires some trial and error in a “human iteration loop.” Using current commercial inverse planning systems this process can be quite time-consuming. Experienced treatment planners know how to steer an IMRT plan in the desired direction by appropriately changing the optimization criteria. Also, class solutions can help to avoid or reduce the “human iteration loop” in cases that do not vary too much between individuals, such as prostate treatments, because optimization criteria can be re-used. Nevertheless, plan optimization leaves something to be desired. The main problem is that it may not be possible to come up with a quantitative, complete optimization formulation for radiotherapy planning in the near future; however, an achievable alternative is to design optimization systems that let the physicians exercise their experienced clinical judgment or intuition in the most direct interactive way. Therefore, some future developments aim at a more interactive approach towards inverse planning. Multicriteria optimization and navigating a treatment plan database have been described as promising approaches in this context.


Dose Distribution Radiat Oncol Biol Phys Normal Tissue Complication Probability IMRT Plan Multicriteria Optimization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Alber M, Nüsslin F (2000) Intensity modulated photon beams subject to a minimal surface smoothing constraint. Phys Med Biol 45:N49–N52PubMedCrossRefGoogle Scholar
  2. Alber M, Nüsslin F (2001) Optimization of intensity modulated radiotherapy under constraints for static and dynamic MLC delivery. Phys Med Biol 46:3229–3339PubMedGoogle Scholar
  3. Bahr GK, Kereiakes JG, Horwitz H, Finney R, Galvin J, Goode K (1968) The method of linear programming applied to radiation treatment planning. Radiology 91:686–693PubMedGoogle Scholar
  4. Birkhoff G (1940) On drawings composed of uniform straight lines. J Math 19:3Google Scholar
  5. Bortfeld T (1999) Optimized planning using physical objectives and constraints. Semin Radiat Oncol 9:20–34PubMedGoogle Scholar
  6. Bortfeld T (2003) Physical optimization. In: Palta JR, Mackie TR (eds) Intensity-modulated radiation therapy: the state of the art. Medical Physics Publishing, Madison, Wisconsin, pp 51–76Google Scholar
  7. Bortfeld T, Schlegel W (1993) Optimization of beam orientations in radiation therapy: some theoretical considerations. Phys Med Biol 38:291–304PubMedCrossRefGoogle Scholar
  8. Bortfeld T, Bürkelbach J, Boesecke R, Schlegel W (1990) Methods of image reconstruction from projections applied to conformation radiotherapy. Phys Med Biol 35:1423–1434PubMedCrossRefGoogle Scholar
  9. Bortfeld T, Boyer AL, Schlegel W, Kahler DL, Waldron TJ (1994) Realization and verification of three-dimensional conformal radiotherapy with modulated fields. Int J Radiat Oncol Biol Phys 30:899–908PubMedGoogle Scholar
  10. Bortfeld T, Stein J, Preiser K (1997) Clinically relevant intensity modulation optimization using physical criteria. Twelfth International Conference on the Use of Computers in Radiation Therapy, Salt Lake City, UtahGoogle Scholar
  11. Bortfeld T, Thieke C, Küfer K.-H., Trinkaus H (2002) New approaches in intensity-modulated radiotherapy: a new optimization paradigm. In: Kogelnik HD, Lukas P, Sedlmayer F (eds) Progress in radio-oncology, vol 7. Monduzzi Editore, Bologna, pp 251–258Google Scholar
  12. Brahme A (1984) Dosimetric precision requirements in radiation therapy. Acta Radiol Oncol 23:379–391PubMedGoogle Scholar
  13. Brahme A (1993) Optimization of radiation therapy and the development of multileaf collimation. Int J Radiat Oncol Biol Phys 25:373–375PubMedGoogle Scholar
  14. Brahme A (1994) Optimization of radiation therapy. Int J Radiat Oncol Biol Phys 28:785–787Google Scholar
  15. Brahme A, Roos JE, Lax I (1982) Solution of an integral equation in rotation therapy. Phys Med Biol 27:1221–1229PubMedCrossRefGoogle Scholar
  16. Carol MP, Nash RV, Campbell RC, Huber R, Sternick E (1997) The development of a clinically intuitive approach to inverse treatment planning: partial volume prescription and area cost function. Twelfth International Conference on the Use of Computers in Radiation Therapy. Salt Lake City, UtahGoogle Scholar
  17. Censor Y (2003) Mathematical optimization for the inverse problem of intensity-modulated radiation therapy. Intensity-modulated radiation therapy: the state of the art. In: Palta JR, Mackie TR (eds) Medical Physics Publishing, pp 25–49Google Scholar
  18. Censor Y, Altschuler MD, Powlis WD (1988) A computational solution of the inverse problem in radiation-therapy treatment planning. Appl Math Comput 25:57–87CrossRefGoogle Scholar
  19. Cho PS, Marks RJ II (2000) Hardware-sensitive optimization for intensity modulated radiotherapy. Phys Med Biol 45:429–440PubMedCrossRefGoogle Scholar
  20. Cho PS, Lee S, Marks RJ II, Oh S, Sutlief SG, Phillips MH (1998) Optimization of intensity modulated beams with volume constraints using two methods: cost function minimization and projections onto convex sets. Med Phys 25:435–443PubMedCrossRefGoogle Scholar
  21. Chui CS, Chan MF, Yorke E, Spirou S, Ling CC (2001) Delivery of intensity-modulated radiation therapy with a conventional multileaf collimator: comparison of dynamic and segmental methods. Med Phys 28:2441–2449PubMedCrossRefGoogle Scholar
  22. Cormack AM, Cormack RA (1987) A problem in rotation therapy with X-rays: dose distributions with an axisof symmetry. Int J Radiat Oncol Biol Phys 13:1921–1925PubMedGoogle Scholar
  23. Deasy JO (1997) Multiple local minima in radiotherapy optimization problems with dose-volume constraints. Med Phys 24:1157–1161PubMedCrossRefGoogle Scholar
  24. DeNeve W, DeWagter C, DeJaeger K, Thienpont M, Colle C, Derycke S, Schelfhout J (1996) Planning and delivering high doses to targets surrounding the spinal cord atthe lower neck and upper mediastinal levels: static beam-segmentation technique executed with a multileaf collimator. Radiother Oncol 40:271–279Google Scholar
  25. Fraass B (2002) Differences between plan evaluation and the optimization problem statement, and the difference it makes. talks.php.Google Scholar
  26. Gustafsson A, Langer M (2000) Dose-volume constrained radiotherapy optimization for a clinical treatment planning system. Thirteenth International Conference on the use of Computers in Radiation therapy. Springer, Berlin Heidelberg New YorkGoogle Scholar
  27. Gustafsson A, Lind BK, Brahme A (1994) A generalized pencil beam algorithm for optimization of radiation therapy. Med Phys 21:343–356PubMedGoogle Scholar
  28. Holmes T, Mackie TR, Simpkin D, Reckwerdt P (1991) A unified approach to the optimization of brachytherapy and external beam dosimetry. Int J Radiat Oncol Biol Phys 20:859–873PubMedGoogle Scholar
  29. Hope CS, Laurie J, Orr JS, Halnan KE (1967) Optimization of X-ray treatment planning by computer judgement. Phys Med Biol 12:531–542PubMedCrossRefGoogle Scholar
  30. Hunt MA, Hsiung CY, Spirou SV, Chui CS, Amols HI, Ling CC (2002) Evaluation of concave dose distributions created using an inverse planning system. Int J Radiat Oncol Biol Phys 54:953–962PubMedCrossRefGoogle Scholar
  31. ICRU (1993) Prescribing, recording, and reporting photon beam therapy. International Commission on Radiation Units and Measurements, report 50. Bethesda, MarylandGoogle Scholar
  32. IMRT (2001) Intensity-modulated radiotherapy: current status and issues of interest. Int J Radiat Oncol Biol Phys 51:880–914Google Scholar
  33. Keller-Reichenbecher M-A, Bortfeld T, Levegrün S, Stein J, Preiser K, Schlegel W (1998) Intensity modulation with the step and shoot technique using a commercial MLC: a planning study. Int J Radiat Oncol Biol Phys 45:1315–1324Google Scholar
  34. Kessen A, Grosser K-H, Bortfeld T (2000) Simplification of IMRT intensity maps by means of 1-D and 2-D median-filtering during the iterative calculation. Thirteenth International Conference on the Use of Computers in Radiation therapy. Springer, Berlin Heidelberg New YorkGoogle Scholar
  35. Küfer K-H, Hamacher HW, Bortfeld TR (2000) A multicriteria optimization approach for inverse radiotherapy planning. Proc XIIIth ICCR, Heidelberg 2000. Springer, Berlin Heidelberg New York, pp 26–29Google Scholar
  36. Küfer K-H, Scherrer A, Monz M, Alonso F, Trinkaus H, Bortfeld T, Thieke C (2003) Intensity modulated radiotherapy: a large scale multi-criteria programming problem. OR Spectrum 25:223–249Google Scholar
  37. Kwa SL, Lebesque JV, Theuws JC, Marks LB, Munley MT, Bentel G, Oetzel D, Spahn U, Graham MV, Drzymala RE, Purdy JA, Lichter AS, Martel MK, Ten Haken RK (1998) Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients. Int J Radiat Oncol Biol Phys 42:1–9PubMedCrossRefGoogle Scholar
  38. Langer M, Leong J (1989) Optimization of beam weights under dose-volume restrictions. Int J Radiat Oncol Biol Phys 13:1255–1259Google Scholar
  39. Langer M, Brown R, Urie M, Leong J, Stracher M, Shapiro J (1990) Large scale optimization of beam weights under dose-volume restrictions. Int J Radiat Oncol Biol Phys 18:887–893PubMedGoogle Scholar
  40. Langer M, Morrill S, Brown R, Lee O, Lane R (1996) A comparison of mixed integer programming and fast simulated annealing for optimizing beam weights in radiation therapy. Med Phys 23:957–964PubMedGoogle Scholar
  41. Langer M, Lee EK, Deasy JO, Rardin RL, Deye JA (2003) Operations research applied to radiotherapy, an NCI-NSF-sponsored workshop 7–9 February, 2002. Int J Radiat Oncol Biol Phys 57:762–768PubMedCrossRefGoogle Scholar
  42. Lauve A, Morris M, Schmidt-Ullrich R, Wu Q, Mohan R, Abayomi O, Buck D, Holdford D, Dawson K, Dinardo L, Reiter E (2004) Simultaneous integrated boost intensity-modulated radiotherapy for locally advanced head-and-neck squamous cell carcinomas. Part II: clinical results. Int J Radiat Oncol Biol Phys 60:374–387PubMedCrossRefGoogle Scholar
  43. Levegrun S, Jackson A, Zelefsky MJ, Skwarchuk MW, Venkatraman ES, Schlegel W, Fuks Z, Leibel SA, Ling CC (2001) Fitting tumor control probability models to biopsy outcome after three-dimensional conformal radiation therapy of prostate cancer: pitfalls in deducing radiobiologic parameters for tumors from clinical data. Int J Radiat Oncol Biol Phys 51:1064–1080PubMedGoogle Scholar
  44. Llacer J, Deasy JO, Portfeld TR, Solberg TD, Promberger C (2003) Absence of multiple local minima effects in intensity modulated optimization with dose-volume constraints. Phys Med Biol 48:183–210PubMedCrossRefGoogle Scholar
  45. Mackie R, Deasy J, Holmes T, Fowler J (1994) Optimization of radiation therapy and the development of multileaf collimation. Int J Radiat Oncol Biol Phys 28:784–785PubMedGoogle Scholar
  46. McDonald SC, Rubin P (1977) Optimization of external beam radiation therapy. Int J Radiat Oncol Biol Phys 2:307–317PubMedGoogle Scholar
  47. Mohan R, Ling CC (1995) When becometh less more? Int J Radiat Oncol Biol Phys 33:235–237PubMedGoogle Scholar
  48. Mohan R, Wang XH (1996) Physical vs biological objectives for treatment plan optimization. Radiother Oncol 40:186–187CrossRefGoogle Scholar
  49. Mohan R, Wu Q, Manning M, Schmidt-Ullrich R (2000) Radiobiological considerations in the design of fractionation strategies for intensity-modulated radiation therapy of head and neck cancers. Int J Radiat Oncol Biol Phys 46:619–630PubMedGoogle Scholar
  50. Natterer F (1986) The mathematics of computerized tomography. Teubner, StuttgartGoogle Scholar
  51. Niemierko A (1992) Random search algorithm (RONSC) for the optimization of radiation therapy with both physical and biological endpoints and constraints. Int J Radiat Oncol Biol Phys 23:89–98PubMedGoogle Scholar
  52. Niemierko A (1997) Reporting and analyzing dose distributions: a concept of equivalent uniform dose. Med Phys 24:103–110PubMedGoogle Scholar
  53. Niemierko A (1999) A generalized concept of equivalent uniform dose (EUD). Med Phys 26:1100Google Scholar
  54. Pugachev A, Li JG, Boyer AL, Hancock SL, Le QT, Donaldson SS, Xing L (2001) Role of beam orientation optimization in intensity-modulated radiation therapy. Int J Radiat Oncol Biol Phys 50:551–560PubMedCrossRefGoogle Scholar
  55. Redpath AT, Vickery BL, Wright DH (1975) A set of fortran subroutines for optimizing radiotherapy plans. Comput Programs Biomed 5:158–164PubMedGoogle Scholar
  56. Romeijn HE, Ahuja RK, Dempsey JF, Kumar A, Li JG (2003) A novel linear programming approach to fluence map optimization for intensity modulated radiation therapy treatment planning. Phys Med Biol 48:3521–3542PubMedCrossRefGoogle Scholar
  57. Shepard DM, Earl MA, Li XA, Naqvi S, C Yu (2002) Direct aperture optimization: a turn key solution for step-and-shoot IMRT. Med Phys 29:1007–1018PubMedCrossRefGoogle Scholar
  58. Shepard DM, Ferris MC, Olivera GH, Mackie TR (1999) Optimizing the delivery of radiation therapy to cancer patients. SIAM Rev 41:721–733CrossRefGoogle Scholar
  59. Söderström S, Brahme A (1995) Which is the most suitable number of photon beam portals in coplanar radiation therapy. Int J Radiat Oncol Biol Phys 33:151–159PubMedGoogle Scholar
  60. Söderström S, Brahme A (1996) Small is beautiful: and often enough. Int J Radiat Oncol Biol Phys 34:757–758PubMedGoogle Scholar
  61. Spirou SV, Chui C-S (1998) A gradient inverse planning algorithm with dose-volume constraints. Med Phys 25:321–333PubMedCrossRefGoogle Scholar
  62. Starkschall G, Pollack A, Stevens CW (2000) Using a dosevolume feasibility search algorithm for radiation treatment planning. Thirteenth International Conference on the Use of Computers in Radiation therapy. Springer, Berlin Heidelberg New YorkGoogle Scholar
  63. Stein J, Mohan R, Wang XH, Bortfeld T, Wu Q, Preiser K, Ling CC, Schlegel W (1997) Number and orientation of beams in intensity-modulated radiation treatments. Med Phys 24:149–160PubMedCrossRefGoogle Scholar
  64. Thieke C (2003) Multicriteria optimization in inverse radiotherapy planning. Doctor of Natural Sciences dissertation. Combined Faculties for the Natural Sciences and for Mathematics, Ruperto-Carola University of HeidelbergGoogle Scholar
  65. Thieke C, Bortfeld T, Niemierko A (2002) Direct Consideration of EUD Constraints in IMRT Optimization. Med Phys 29:1283CrossRefGoogle Scholar
  66. Webb S (1989) Optimisation of conformal radiotherapy dose distributions by simulated annealing. Phys Med Biol 34:1349–1370PubMedGoogle Scholar
  67. Webb S (1992) Optimization by simulated annealing of three-dimensional, conformaltreatment planning for radiation fields defined by a multileaf collimator:II. Inclusion of the two-dimensional modulation of the X-ray intensity. Phys Med Biol 37:1689–1704PubMedGoogle Scholar
  68. Webb S, Convery DJ, Evans PM (1998) Inverse planning with constraints to generate smoothed intensity-modulated beams. Phys Med Biol 43:2785–2794PubMedGoogle Scholar
  69. Wu Q, Mohan R (2002) Multiple local minima in IMRT optimization based on dose-volume criteria. Med Phys 29:1514–1527PubMedGoogle Scholar
  70. Wu Q, Mohan R, Niemierko A, Schmidt-Ullrich R (2002) Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose. Int J Radiat Oncol Biol Phys 52:224–235PubMedGoogle Scholar
  71. Yajnik S, Rosenzweig KE, Mychalczak B, Krug L, Flores R, Hong L, Rusch VW (2003) Hemithoracic radiation after extrapleural pneumonectomy for malignant pleural mesothelioma. Int J Radiat Oncol Biol Phys 56:1319–1326PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Thomas Bortfeld
    • 1
  • Christian Thieke
    • 2
  1. 1.Department of Radiation OncologyMassachusetts General HospitalBostonUSA
  2. 2.Department of Radiation OncologyGerman Cancer Research Center (DKFZ)HeidelbergGermany

Personalised recommendations