Introduction to Mathematical Oncology

“Introduction to Mathematical Oncology” by Yang Kuang, published by CRC Press, Taylor & Francis Group in 2016, is a comprehensive resource that presents biologically motivated and mathematically tractable models aimed at enhancing the understanding of cancer biology and improving treatment designs. Spanning 470 pages, this edition delves into the medical and biological backgrounds of cancer, addressing modeling issues and existing methods while introducing mathematical and programming tools essential for analytical and numerical studies.
Readers will find a detailed exploration of practical mathematical models related to avascular tumor growth and treatment dynamics, utilizing ordinary and partial differential equations. The text also highlights recent multi-scale modeling efforts, particularly in prostate cancer, and examines mechanistically formulated models with real tumor applications validated by clinical data. Designed for both undergraduate and graduate courses, this self-contained book allows for flexible instructional approaches, making it suitable for a variety of academic settings focused on mathematical oncology.
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Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases, modeling issues, and existing methods and their limitations. The authors introduce mathematical and programming tools, along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models.
After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding, the text describes well-known, practical, and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure, such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models, including cell quota-based population growth models, with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical, biological, and medical background materials for advanced and specific treatment modeling efforts.
Extensively classroom-tested in undergraduate and graduate courses, this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways, including a single-semester undergraduate course, a more ambitious graduate course, or a full-year sequence on mathematical oncology.
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