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Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence

Author

Listed:
  • Najmeddine Dhieb

    (Stevens Institute of Technology)

  • Ismail Abdulrashid

    (The University of Tulsa)

  • Hakim Ghazzai

    (Stevens Institute of Technology)

  • Yehia Massoud

    (King Abdullah University of Science and Technology (KAUST))

Abstract

This note presents a novel chemotherapy protocol for physicians to treat cancer tumors. Mathematical modeling, analysis, and simulations are used to describe the detailed dynamics of tumor, effector-immune cells, lymphocyte population, and chemotherapy drug, inside the patient body. An optimized scheduling alternating between treatment and relaxation sessions is determined to minimize the tumor size at the end of therapy period and overcome the toxicity level of patient’s organs. To this end, we propose and allot relaxation sessions between two consecutive treatment sessions so that the body can partially recover. For each treatment period, we determine an optimal control strategy to minimize the tumor size and drug consumption without negatively affecting the natural cells. Finally, a particle swarm optimization-based approach is developed in order to ascertain the duration of each therapy session. The obtained results show that the proposed solution presents significant advantages in drug dosage, tumor reduction, and chemotherapy scheduling sessions compared to mathematical-based state-of-art approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-021-04234-6
    DOI: 10.1007/s10479-021-04234-6
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Camila Ramos & Alejandro Cataldo & Juan–Carlos Ferrer, 2020. "Appointment and patient scheduling in chemotherapy: a case study in Chilean hospitals," Annals of Operations Research, Springer, vol. 286(1), pages 411-439, March.
    Full references (including those not matched with items on IDEAS)

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