Mathematical Modeling and Analysis of Tumor Chemotherapy

Abstract

Cancer diseases lead to the second-highest death rate all over the world. For treating tumors, one of the most common schemes is chemotherapy, which can decrease the tumor size and control the progression of cancer diseases. To better understand the mechanisms of chemotherapy, we developed a mathematical model of tumor growth under chemotherapy. This model includes both immune system response and drug therapy. We characterize the symmetrical properties and dynamics of this differential equation model by finding the equilibrium points and exploring the stability and symmetry properties in a range of model parameters. Sensitivity analyses suggest that the chemotherapy drug-induced tumor mortality rate and the drug decay rate contribute significantly to the determination of treatment outcomes. Numerical simulations highlight the importance of CTL activation in tumor chemotherapy.

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and Machine Learning Approaches

“…It complements cancer research by providing valuable quantitative predictions to unravel the complexity of cancer and open new avenues for developing effective treatments [25]. Numerous mathematical models, often rooted in ordinary differential equations (ODEs), have explored various aspects of cancer research, including treatment strategies [26][27][28][29][30][31][32][33][34], immune responses [35][36][37][38][39][40][41], treatment sensitivity and resistance [42], the effects of treatment combinations [43][44][45][46][47][48][49], habitat dynamics [50], tumor heterogeneity [51,52], and treatment optimization [53]. Their collective contribution lies in furnishing predictions for optimal dosing, treatment regimens, and scheduling, all while minimizing adverse side effects and maximizing therapeutic gains.…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…It complements cancer research by providing valuable quantitative predictions to unravel the complexity of cancer and open new avenues for developing effective treatments [25]. Numerous mathematical models, often rooted in ordinary differential equations (ODEs), have explored various aspects of cancer research, including treatment strategies [26][27][28][29][30][31][32][33][34], immune responses [35][36][37][38][39][40][41], treatment sensitivity and resistance [42], the effects of treatment combinations [43][44][45][46][47][48][49], habitat dynamics [50], tumor heterogeneity [51,52], and treatment optimization [53]. Their collective contribution lies in furnishing predictions for optimal dosing, treatment regimens, and scheduling, all while minimizing adverse side effects and maximizing therapeutic gains.…”

Modeling the Nonmonotonic Immune Response in a Tumor–Immune System Interaction

“…Talkington [23] explored and compared four previous models with adoptive immunotherapy that predict successful tumor elimination. Song et al [24] developed a tumor growth model with immune system response and drug therapy to study the mechanisms of chemotherapy. Bekker et al [25] carried out a survey of mathematical models to evaluate the effects of radiotherapy on tumor-immune system interactions.…”