Annals of Operations Research

, Volume 236, Issue 2, pp 319–339 | Cite as

Heuristics for integrated optimization of catheter positioning and dwell time distribution in prostate HDR brachytherapy

  • Åsa Holm
  • Åsa Carlsson Tedgren
  • Torbjörn Larsson
Article

Abstract

High dose-rate (HDR) brachytherapy is one kind of radiotherapy used to treat different forms of cancer, such as prostate cancer. When this treatment is used for prostate cancer, a radioactive source is moved through catheters implanted into the prostate. For each patient, a treatment plan is constructed. This plan determines for example catheter positions and dwell time distribution, that is, where to the radioactive source should stop and for how long.

Mathematical optimization methods have been used to find dwell time distributions of high quality; however few optimization approaches that concern catheter positioning have been studied. In this article we present an integrated model that optimizes catheter positioning and dwell time distribution simultaneously. Our results show that integrating the catheter positioning yields a large reduction of the dwell time distribution objective value (15–94 %) and slight improvements in clinical quality measures.

Since the presented model is computationally demanding to solve, we also present three heuristics: a tabu search, a variable neighbourhood search and a genetic algorithm. Of these, variable neighbourhood search is the best, and out-performs a state-of-the-art optimization software (CPLEX) and the two other heuristics.

Keywords

Brachytherapy Dose planning Catheter positioning Mixed integer programming Metaheuristics 

References

  1. Alterovitz, R., Lessard, E., Pouliot, J., Hsu, I. C. J., O’Brien, J. F., & Goldberg, K. (2006). Optimization of HDR brachytherapy dose distributions using linear programming with penalty costs. Medical Physics, 33(11), 4012–4019. CrossRefGoogle Scholar
  2. Ayotte, G., D’Amours, M., Aubin, S., Lessard, E., Pouliot, J., & Beaulieu, L. (2009). Sci-Thurs AM: YIS-02: optimizing number and position of catheters within inverse planning simulated annealing (IPSA) for prostate and breast high dose rate brachytherapy. In AAPM (Vol. 36, p. 4315). Google Scholar
  3. Baltas, D., Katsilieri, Z., Kefala, V., Papaioannou, S., Karabis, A., Mavroidis, P., & Zamboglou, N. (2009). Influence of modulation restriction in inverse optimization with HIPO of prostate implants on plan quality: analysis using dosimetric and radiobiological indices. In R. Magjarevic, O. Dössel, & W. C. Schlegel (Eds.), IFMBE proceedings: Vol. 25. World congress on medical physics and biomedical engineering, 7–12 September 2009, Munich, Germany (pp. 283–286). Google Scholar
  4. Baltas, D., Colla, E., Karabis, A., & Kirisits, C. Optimization and inverse planning tools in oncentra gyn. http://www.nucletron.com/en/ProductsAndSolutions/Pages/OncentraGYN.aspx. Downloaded 2010.
  5. Boyle, P. & Levin, B. (Eds.) (2008). World cancer report 2008. Lyon: International Agency for Research on Cancer. Google Scholar
  6. Chajon, E., Dumas, I., Touleimat, M., Magné, N., Coulot, J., Verstraet, R., Lefkopoulos, D., & Haie-Meder, C. (2007). Inverse planning approach for 3-D MRI-based pulse-dose rate intracavitary brachytherapy in cervix cancer. International Journal of Radiation Oncology, Biology, Physics, 69(3), 955–961. CrossRefGoogle Scholar
  7. Glover, F., & Laguna, M. (1997). Tabu search. Norwell: Kluwer Academic. CrossRefGoogle Scholar
  8. Hansen, P., & Mladenović, N. (2005). Variable neighborhood search. In E. K. Burke & G. Kendall (Eds.), Search methodologies (pp. 211–238). New York: Springer. CrossRefGoogle Scholar
  9. Holm, Å., Larsson, T., & Carlsson Tedgren, Å. (2012). Impact of using linear optimization models in dose planning for HDR brachytherapy. Medical Physics, 39(2), 1021–1028. CrossRefGoogle Scholar
  10. Karabis, A., Belotti, P., & Baltas, D. (2009). Optimization of catheter position and dwell time in prostate HDR brachytherapy using HIPO and linear programming. In R. Magjarevic, O. Dössel, & W. C. Schlegel (Eds.), IFMBE proceedings: Vol. 25. World congress on medical physics and biomedical engineering, 7–12 September 2009, Munich, Germany (pp. 612–615). Berlin: Springer. Google Scholar
  11. Kovács, G., Pötter, R., Loch, T., Hammer, J., Kolkman-Deurloo, I. K., de la Rosette, J. J., & Bertermann, H. (2005). GEC/ESTRO-EAU recommendations on temporary brachytherapy using stepping sources for localised prostate cancer. Radiotherapy and Oncology, 74(2), 137–148. CrossRefGoogle Scholar
  12. Lahanas, M., Baltas, D., Giannouli, S., Milickovic, N., & Zamboglou, N. (2000). Generation of uniformly distributed dose points for anatomy-based three-dimensional dose optimization methods in brachytherapy. Medical Physics, 27(5), 1034–1046. CrossRefGoogle Scholar
  13. Lahanas, M., Baltas, D., & Zamboglou, N. (2003). A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy. Physics in Medicine and Biology, 48(3), 399–415. CrossRefGoogle Scholar
  14. Lessard, E., & Pouliot, J. (2001). Inverse planning anatomy-based dose optimization for HDR-brachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function. Medical Physics, 28(5), 773–779. CrossRefGoogle Scholar
  15. Reeves, C. R. (2005). Fitness landscapes. In E. K. Burke & G. Kendall (Eds.), Search methodologies (pp. 587–610). New York: Springer. CrossRefGoogle Scholar
  16. Rivard, M. J., Coursey, B. M., DeWerd, L. A., Hanson, W. F., Huq, M. S., Ibbott, G. S., Mitch, M. G., Nath, R., & Williamson, J. F. (2004). Update of AAPM task group no. 43 report: a revised AAPM protocol for brachytherapy dose calculations. Medical Physics, 31(3), 633–674. CrossRefGoogle Scholar
  17. Ruotsalainen, H., Miettinen, K., Palmgren, J. E., & Lahtinen, T. (2010). Interactive multiobjective optimization for anatomy-based three-dimensional HDR brachytherapy. Physics in Medicine and Biology, 55(16), 4703–4719. CrossRefGoogle Scholar
  18. Russell, K. R., Carlsson Tedgren, Å., & Ahnesjö, A. (2005). Brachytherapy source characterization for improved dose calculations using primary and scatter dose separation. Medical Physics, 32(9), 2739–2752. CrossRefGoogle Scholar
  19. Sastry, K., Goldberg, D., & Kendall, G. (2005). Genetic algorithms. In E. K. Burke & G. Kendall (Eds.), Search methodologies (pp. 97–125). Berlin: Springer. CrossRefGoogle Scholar
  20. Siauw, T., Cunha, A., Atamturk, A., Hsu, I. C., Pouliot, J., & Goldberg, K. (2011). IPIP: a new approach to inverse planning for HDR brachytherapy by directly optimizing dosimetric indices. Medical Physics, 38(7), 4045–4051. CrossRefGoogle Scholar
  21. Siauw, T., Cunha, A., Berenson, D., Atamturk, A., Hsu, I. C., Goldberg, K., & Pouliot, J. (2012). NPIP: a skew line needle configuration optimization system for HDR brachytherapy. Medical Physics, 39(7), 4339–4346. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Åsa Holm
    • 1
  • Åsa Carlsson Tedgren
    • 2
    • 3
  • Torbjörn Larsson
    • 1
  1. 1.Department of MathematicsLinköping UniversityLinköpingSweden
  2. 2.Swedish Radiation Safety AuthorityStockholmSweden
  3. 3.Department of Medical and Health SciencesLinköping UniversityLinköpingSweden

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