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dc.contributor.authorDhieb, Najmeddine
dc.contributor.authorAbdulrashid, Ismail
dc.contributor.authorGhazzai, Hakim
dc.contributor.authorMassoud, Yehia Mahmoud
dc.date.accessioned2021-09-08T06:18:11Z
dc.date.available2021-09-08T06:18:11Z
dc.date.issued2021-09-01
dc.identifier.citationDhieb, N., Abdulrashid, I., Ghazzai, H., & Massoud, Y. (2021). Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence. Annals of Operations Research. doi:10.1007/s10479-021-04234-6
dc.identifier.issn1572-9338
dc.identifier.issn0254-5330
dc.identifier.doi10.1007/s10479-021-04234-6
dc.identifier.urihttp://hdl.handle.net/10754/671107
dc.description.abstractThis 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.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://link.springer.com/10.1007/s10479-021-04234-6
dc.rightsArchived with thanks to Annals of Operations Research
dc.titleOptimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
dc.identifier.journalAnnals of Operations Research
dc.rights.embargodate2022-09-01
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07307, USA
dc.contributor.institutionSchool of Finance, Operations Management, and International Business, Collins College of Business, The University of Tulsa, 800 South Tucker Drive, Tulsa, OK, 74104, USA
kaust.personMassoud, Yehia Mahmoud
dc.date.accepted2021-08-10
dc.identifier.eid2-s2.0-85114022828


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