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A survey of optimization models on cancer chemotherapy treatment planning

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  • Jinghua Shi
  • Oguzhan Alagoz
  • Fatih Erenay
  • Qiang Su

Abstract

While chemotherapy is an effective method for treating cancers such as colorectal cancer, its effectiveness may be dampened by the drug resistance and it may have significant side effects due to the destruction of normal cells during the treatment. As a result, there is a need for research on choosing an optimal chemotherapy treatment plan that minimizes the number of cancerous cells while ensuring that the total toxicity is below an allowable limit. In this paper, we summarize the mathematical models applied to the optimal design of the cancer chemotherapy. We first elaborate on a typical optimization model and classify relevant literature with respect to modeling methods: Optimal control model (OCM) and others. We further classify the OCM models with respect to the solution method used. We discuss the limitations of the existing research and provide several directions for further research in optimizing chemotherapy treatment planning. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • Jinghua Shi & Oguzhan Alagoz & Fatih Erenay & Qiang Su, 2014. "A survey of optimization models on cancer chemotherapy treatment planning," Annals of Operations Research, Springer, vol. 221(1), pages 331-356, October.
    • Handle: RePEc:spr:annopr:v:221:y:2014:i:1:p:331-356:10.1007/s10479-011-0869-4
    DOI: 10.1007/s10479-011-0869-4
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    References listed on IDEAS

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    1. Zvia Agur & Refael Hassin & Sigal Levy, 2006. "Optimizing Chemotherapy Scheduling Using Local Search Heuristics," Operations Research, INFORMS, vol. 54(5), pages 829-846, October.
    2. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Choosing Among Living-Donor and Cadaveric Livers," Management Science, INFORMS, vol. 53(11), pages 1702-1715, November.
    3. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List," Operations Research, INFORMS, vol. 55(1), pages 24-36, February.
    4. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
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    Cited by:

    1. Kai He & Lisa M. Maillart & Oleg A. Prokopyev, 2019. "Optimal sequencing of heterogeneous, non-instantaneous interventions," Annals of Operations Research, Springer, vol. 276(1), pages 109-135, May.
    2. Itziar Irurzun-Arana & Alvaro Janda & Sergio Ardanza-Trevijano & Iñaki F Trocóniz, 2018. "Optimal dynamic control approach in a multi-objective therapeutic scenario: Application to drug delivery in the treatment of prostate cancer," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-16, April.
    3. Nazila Bazrafshan & M. M. Lotfi, 2020. "A finite-horizon Markov decision process model for cancer chemotherapy treatment planning: an application to sequential treatment decision making in clinical trials," Annals of Operations Research, Springer, vol. 295(1), pages 483-502, December.
    4. Najmeddine Dhieb & Ismail Abdulrashid & Hakim Ghazzai & Yehia Massoud, 2023. "Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence," Annals of Operations Research, Springer, vol. 320(2), pages 757-770, January.
    5. Poh Ling Tan & Helmut Maurer & Jeevan Kanesan & Joon Huang Chuah, 2022. "Optimal Control of Cancer Chemotherapy with Delays and State Constraints," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 749-770, September.

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