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Mathematical modeling of tumor growth and treatment
Item metadata
dc.contributor.author | Enderling, Heiko | |
dc.contributor.author | Chaplain, Mark A. J. | |
dc.date.accessioned | 2015-10-29T12:40:01Z | |
dc.date.available | 2015-10-29T12:40:01Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Enderling , H & Chaplain , M A J 2014 , ' Mathematical modeling of tumor growth and treatment ' , Current Pharmaceutical Design , vol. 20 , no. 30 , pp. 4934-4940 . https://doi.org/10.2174/1381612819666131125150434 | en |
dc.identifier.issn | 1381-6128 | |
dc.identifier.other | PURE: 206432506 | |
dc.identifier.other | PURE UUID: 0430b227-525f-4c9c-a1b8-e050b64f571c | |
dc.identifier.other | RIS: urn:4EA3D9AA2D70D8AA4A0F744011FF684D | |
dc.identifier.other | Scopus: 84906217098 | |
dc.identifier.other | ORCID: /0000-0001-5727-2160/work/55378874 | |
dc.identifier.uri | https://hdl.handle.net/10023/7710 | |
dc.description.abstract | Using mathematical models to simulate dynamic biological processes has a long history. Over the past couple of decades or so, quantitative approaches have also made their way into cancer research. An increasing number of mathematical, physical, computational and engineering techniques have been applied to various aspects of tumor growth, with the ultimate goal of understanding the response of the cancer population to clinical intervention. So-called in silico trials that predict patient-specific response to various dose schedules or treatment combinations and sequencing are on the way to becoming an invaluable tool to optimize patient care. Herein we describe fundamentals of mathematical modeling of tumor growth and tumor-host interactions, and summarize some of the seminal and most prominent approaches. | |
dc.format.extent | 7 | |
dc.language.iso | eng | |
dc.relation.ispartof | Current Pharmaceutical Design | en |
dc.rights | © 2014, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.euekaselect.com / https://dx.doi.org/10.2174/1381612819666131125150434 | en |
dc.subject | Ordinary differential equations | en |
dc.subject | Partial differential equation | en |
dc.subject | Tumor modelling | en |
dc.subject | Angiogenesis | en |
dc.subject | RC0254 Neoplasms. Tumors. Oncology (including Cancer) | en |
dc.subject | QA Mathematics | en |
dc.subject | QH301 Biology | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject.lcc | RC0254 | en |
dc.subject.lcc | QA | en |
dc.subject.lcc | QH301 | en |
dc.title | Mathematical modeling of tumor growth and treatment | en |
dc.type | Journal article | en |
dc.description.version | Postprint | en |
dc.contributor.institution | University of St Andrews. Applied Mathematics | en |
dc.identifier.doi | https://doi.org/10.2174/1381612819666131125150434 | |
dc.description.status | Peer reviewed | en |
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