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A stochastic control formalism for dynamic biologically conformal radiation therapy

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  • Kim, Minsun
  • Ghate, Archis
  • Phillips, Mark H.

Abstract

State-of-the-art methods for optimizing cancer treatment over several weeks of external beam radiotherapy take a static–deterministic view of the treatment planning process, mainly focusing on spatial distribution of dose. Recent progress in quantitative functional imaging as well as mathematical models of tumor response to radiotherapy is increasingly enabling treatment planners to monitor/predict a patient’s biological response over weeks of treatment. In this paper we introduce dynamic biologically conformal radiation therapy (DBCRT), a mathematical framework intended to exploit these emerging technological and biological modeling advances to design patient-specific radiation treatment strategies that dynamically adapt to the spatiotemporal evolution of a patient’s biological response over several treatment sessions in order to achieve the best possible health outcome. More specifically, we propose a discrete-time stochastic control formalism where we use the patient’s biological condition to model the system state and the beam intensities as controls. Three approximate control schemes are then applied and compared for efficiency. Numerical simulations on test cases show that DBCRT results in a 64–98% improvement in treatment efficacy as compared to the more conventional static–deterministic approach.

Suggested Citation

  • Kim, Minsun & Ghate, Archis & Phillips, Mark H., 2012. "A stochastic control formalism for dynamic biologically conformal radiation therapy," European Journal of Operational Research, Elsevier, vol. 219(3), pages 541-556.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:3:p:541-556
    DOI: 10.1016/j.ejor.2011.10.039
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    References listed on IDEAS

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    1. H. Edwin Romeijn & Ravindra K. Ahuja & James F. Dempsey & Arvind Kumar, 2006. "A New Linear Programming Approach to Radiation Therapy Treatment Planning Problems," Operations Research, INFORMS, vol. 54(2), pages 201-216, April.
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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. Shraddha Ghatkar, 2019. "Optimization of fractionation schemes and beamlet intensities in intensity-modulated radiation therapy with changing cancer tumor properties," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 46(4), pages 385-407, December.
    3. Fatemeh Saberian & Archis Ghate & Minsun Kim, 2017. "Spatiotemporally Optimal Fractionation in Radiotherapy," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 422-437, August.
    4. Ali Ajdari & Fatemeh Saberian & Archis Ghate, 2020. "A Theoretical Framework for Learning Tumor Dose-Response Uncertainty in Individualized Spatiobiologically Integrated Radiotherapy," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 930-951, October.
    5. Sauré, Antoine & Patrick, Jonathan & Tyldesley, Scott & Puterman, Martin L., 2012. "Dynamic multi-appointment patient scheduling for radiation therapy," European Journal of Operational Research, Elsevier, vol. 223(2), pages 573-584.
    6. Yasin Gocgun, 2018. "Simulation-based approximate policy iteration for dynamic patient scheduling for radiation therapy," Health Care Management Science, Springer, vol. 21(3), pages 317-325, September.
    7. Kotas, Jakob & Ghate, Archis, 2018. "Bayesian learning of dose–response parameters from a cohort under response-guided dosing," European Journal of Operational Research, Elsevier, vol. 265(1), pages 328-343.

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