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Cancer, a complex disease characterized by uncontrolled cell growth and metastasis, remains a formidable challenge to global health. Mathematical modeling has emerged as a critical tool to elucidate the underlying biological mechanisms driving tumor initiation, progression, and treatment responses. By integrating principles from biology, physics, and mathematics, mathematical oncology provides a quantitative framework for understanding tumor growth dynamics, microenvironmental interactions, and the evolution of cancer cells. This study explores the key applications of mathematical modeling in oncology, encompassing tumor growth kinetics, intra-tumor heterogeneity, personalized medicine, clinical trial optimization, and cancer immunology. Through the development and application of computational models, researchers aim to gain deeper insights into cancer biology, identify novel therapeutic targets, and optimize treatment strategies. Ultimately, mathematical oncology holds the promise of transforming cancer care by enabling more precise, personalized, and effective therapies.
Tahmineh Azizi (Mon,) studied this question.