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

Najmeddine Dhieb, Ismail Abdulrashid, Hakim Ghazzai, Yehia Massoud

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish (US)
JournalAnnals of Operations Research
DOIs
StatePublished - Sep 1 2021

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research

Fingerprint

Dive into the research topics of 'Optimized drug regimen and chemotherapy scheduling for cancer treatment using swarm intelligence'. Together they form a unique fingerprint.

Cite this