Mathematical Modeling Of Cancerous Tumor For Reducing Growth As Consequence Of Cancer Treatment

Abstract

The mathematical modeling of malignant tumors provides essential insights into tumor development dynamics and the optimization of cancer therapy methods. This research examines tumor proliferation through Gompertzian and logistic models, simulates chemotherapy employing the log-kill hypothesis, and assesses radiation utilizing the linear-quadratic model. The impacts of chemotherapy, radiation, and combination therapies were modeled and examined to investigate their efficacy in diminishing tumor size and postponing drug resistance. Numerical simulations indicate that combination therapies are more effective than single-treatment modalities in managing tumor size, with adaptive chemotherapy and tailored radiation schedules producing the most favorable results. Sensitivity analysis underscores the importance of patient-specific characteristics, necessitating tailored therapy. The outcomes correspond closely with clinical data, affirming the models' predictive capability. This research highlights the significance of mathematical models in enhancing cancer treatment strategies and optimizing patient outcomes.