Control and Optimization
Alireza Fakharzadeh Jahromi; Mahin Azizi Karachi; Hajar Alimorad
Abstract
Cancer is a class of diseases characterized by uncontrolled cell growth that affects immune cells. There are several treatment options available, including surgery, chemotherapy, hormonal therapy, radiation therapy, targeted therapy, and ...
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Cancer is a class of diseases characterized by uncontrolled cell growth that affects immune cells. There are several treatment options available, including surgery, chemotherapy, hormonal therapy, radiation therapy, targeted therapy, and palliative care. Among these, chemotherapy is one of the most widely used and recognized methods. This paper presents a novel model designed to control cancer cell growth based on a system of nonlinear fractional differential equations with delay in chemotherapy. The model focuses on the competition between tumor and immune cells to minimize the number of tumor cells and determine the optimal dosage of the administered drug. It can simulate various scenarios and predict the outcomes of different chemotherapy regimens. By employing discretization and the Grunwald-Letnikov method, we aim to gain insights into why some patients respond well to chemotherapy while others do not. The results may also help identify potential drug targets and optimize existing treatments.
