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Neuro-dynamic programming for fractionated radiotherapy planning

In: Optimization in Medicine

Author

Listed:
  • Geng Deng

    (University of Wisconsin at Madison)

  • Michael C. Ferris

    (University of Wisconsin at Madison)

Abstract

Summary We investigate an on-line planning strategy for the fractionated radiotherapy planning problem, which incorporates the effects of day-to-day patient motion. On-line planning demonstrates significant improvement over off-line strategies in terms of reducing registration error, but it requires extra work in the replanning procedures, such as in the CT scans and the re-computation of a deliverable dose profile. We formulate the problem in a dynamic programming framework and solve it based on the approximate policy iteration techniques of neuro-dynamic programming. In initial limited testing, the solutions we obtain outperform existing solutions and offer an improved dose profile for each fraction of the treatment.

Suggested Citation

  • Geng Deng & Michael C. Ferris, 2008. "Neuro-dynamic programming for fractionated radiotherapy planning," Springer Optimization and Its Applications, in: Carlos J. S. Alves & Panos M. Pardalos & Luis Nunes Vicente (ed.), Optimization in Medicine, pages 47-70, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-73299-2_3
    DOI: 10.1007/978-0-387-73299-2_3
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    Cited by:

    1. Thomas Bortfeld & Jagdish Ramakrishnan & John N. Tsitsiklis & Jan Unkelbach, 2015. "Optimization of Radiation Therapy Fractionation Schedules in the Presence of Tumor Repopulation," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 788-803, November.
    2. Jalalimanesh, Ammar & Shahabi Haghighi, Hamidreza & Ahmadi, Abbas & Soltani, Madjid, 2017. "Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 133(C), pages 235-248.
    3. Chan, Timothy C.Y. & Mišić, Velibor V., 2013. "Adaptive and robust radiation therapy optimization for lung cancer," European Journal of Operational Research, Elsevier, vol. 231(3), pages 745-756.

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