LEADER 03293nam 22006135 450 001 9911049083803321 005 20260102120454.0 010 $a0-387-68564-2 024 7 $a10.1007/978-0-387-68564-9 035 $a(CKB)44770140400041 035 $a(MiAaPQ)EBC32471231 035 $a(Au-PeEL)EBL32471231 035 $a(DE-He213)978-0-387-68564-9 035 $a(EXLCZ)9944770140400041 100 $a20260102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical Oncology /$fby Mark A. J. Chaplain, Luigi Preziosi 205 $a1st ed. 2025. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2025. 215 $a1 online resource (1181 pages) 225 1 $aInterdisciplinary Applied Mathematics,$x2196-9973 ;$v62 311 08$a0-387-40324-8 327 $aPreface -- Biology/Pathology of Cancer -- Modeling the Immune Response to Cancer -- Simple Models of Tumor Growth -- Modeling Avascular, Multicellular Spheroid Growth -- Pre-pattern Models of Tumor Growth -- Modeling Tumor-induced Angiogenesis -- Modeling Invasion and Metastasis -- Mechanical Models of Growth -- Index. 330 $aCancer is a complex and devastating disease, responsible for millions of deaths worldwide each year. While traditional oncology focuses on diagnosis and treatment through medical, surgical, radiation and clinical techniques, mathematical oncology has emerged as a powerful discipline that applies mathematical modelling to understand cancer growth, spread, and response to treatment. This volume provides a comprehensive exploration of mathematical approaches in oncology, offering a deep dive into differential equation models and biomechanical models. From reaction-diffusion equations that capture tumour growth and spread, to mechanical models that examine cellular interactions within tissues and vessels, this book presents both analytical techniques and computational modelling approaches that enhance our understanding of cancer dynamics. This book serves as both a reference for researchers and a foundation for integrating mathematical oncology into university curricula. By bridging the gap between mathematics, biology, and clinical research, it highlights the crucial role of mathematical modelling in advancing cancer treatment strategies and improving patient outcomes. 410 0$aInterdisciplinary Applied Mathematics,$x2196-9973 ;$v62 606 $aBiomathematics 606 $aImmunology 606 $aInternal medicine 606 $aPhysiology 606 $aMathematical and Computational Biology 606 $aImmunology 606 $aInternal Medicine 606 $aAnimal Physiology 615 0$aBiomathematics. 615 0$aImmunology. 615 0$aInternal medicine. 615 0$aPhysiology. 615 14$aMathematical and Computational Biology. 615 24$aImmunology. 615 24$aInternal Medicine. 615 24$aAnimal Physiology. 676 $a570.285 700 $aChaplain$b M. A. J$01891071 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049083803321 996 $aMathematical Oncology$94533711 997 $aUNINA