A measurement-based control design approach for efficient cancer chemotherapy

Khadraoui, Sofiane; Harrou, Fouzi; Nounou, Hazem; Nounou, Mohamed; Datta, Aniruddha; Bhattacharyya, S.P.

doi:10.1016/j.ins.2015.11.026

Cited by 31 publications

(4 citation statements)

References 15 publications

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“… Calculate the difference between the target population of cancer cells and the actual population of cancer cells N ( t ) at the end of the treatment period. Adjust the dosage of the treatment based on the difference between the target and actual population of cancer cells using a proportional-integral-derivative (PID) controller 44 , 45 . The PID controller can be defined as follows:…”

Section: Control Theorymentioning

confidence: 99%

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A fractional-order mathematical model for lung cancer incorporating integrated therapeutic approaches

Amilo,

Kaymakamzade,

Hincal

2023

Sci Rep

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This paper addresses the dynamics of lung cancer by employing a fractional-order mathematical model that investigates the combined therapy of surgery and immunotherapy. The significance of this study lies in its exploration of the effects of surgery and immunotherapy on tumor growth rate and the immune response to cancer cells. To optimize the treatment dosage based on tumor response, a feedback control system is designed using control theory, and Pontryagin’s Maximum Principle is utilized to derive the necessary conditions for optimality. The results reveal that the reproduction number $$(R_0)$$ ( R 0 ) is 2.6, indicating that a lung cancer cell would generate 2.6 new cancer cells during its lifetime. The reproduction coefficient $$(R_c)$$ ( R c ) is 0.22, signifying that cancer cells divide at a rate that is 0.22 times that of normal cells. The simulations demonstrate that the combined therapy approach yields significantly improved patient outcomes compared to either treatment alone. Furthermore, the analysis highlights the sensitivity of the steady-state solution to variations in $$k_5$$ k 5 (the rate of division of cancer stem cells) and $$k_{13}$$ k 13 (the rate of differentiation of cancer stem cells into progenitor cells). This research offers clinicians a valuable tool for developing personalized treatment plans for lung cancer patients, incorporating individual patient factors and tumor characteristics. The novelty of this work lies in its integration of surgery, immunotherapy, and control theory, extending beyond previous efforts in the literature.

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Section: Control Theorymentioning

confidence: 99%

“…Adjust the dosage of the treatment based on the difference between the target and actual population of cancer cells using a proportional-integral-derivative (PID) controller 44 , 45 .…”

Section: Control Theorymentioning

confidence: 99%

A fractional-order mathematical model for lung cancer incorporating integrated therapeutic approaches

Amilo,

Kaymakamzade,

Hincal

2023

Sci Rep

View full text Add to dashboard Cite

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“…However, one suitable solution for PID parameters must be carefully chosen from several optimal Pareto solution sets. Khadraoui et al [22] applied two PID controllers on a modified Martin's chemotherapy model to control both toxicity and drug concentration in patient body. But the authors in [21,22] used conventional controller that usually does not obtain satisfactory performance under parameter variations [23].…”

Section: Introductionmentioning

confidence: 99%

“…Khadraoui et al [22] applied two PID controllers on a modified Martin's chemotherapy model to control both toxicity and drug concentration in patient body. But the authors in [21,22] used conventional controller that usually does not obtain satisfactory performance under parameter variations [23]. Fuzzy logic controllers can work with imprecise inputs, handles non-linearities and is more robust than conventional controllers [24].…”

Section: Introductionmentioning

confidence: 99%

Intuitionistic Fuzzy Control of Different Strategies for Cancer Treatment

El-Garawany

El-Brawany

2021

Menoufia Journal of Electronic Engineering Research

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Chemotherapy is one of the most effective treatment methods for cancer patients. An intuitionistic fuzzy logic control (IFLC) of chemotherapy drug delivery system is presented in this paper. An Ant Colony Optimization (ACO) algorithm is utilized to optimize the intuitionistic fuzzy input-output scaling factors of the drug infusion system. The controller is designed and tested for tracking two clinically relevant chemotherapy dose scheduling protocols. Moreover, the patient safety is implied in the design of our controller by considering maximum allowed level constraints on both drug infusion dose and drug-dose toxicity. The obtained results from the optimized control system are compared with the previous studies. The application of the developed controller resulted in lowering the number of remaining cancer cells to 0.1856. with the highest performance index of 29.31.

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