A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: The paradigm of glioblastoma multiforme treated by temozolomide

Stamatakos, GS; Antipas, VP; Uzunoglu, NK

dc.contributor.author	Stamatakos, GS	en
dc.contributor.author	Antipas, VP	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T01:23:31Z
dc.date.available	2014-03-01T01:23:31Z
dc.date.issued	2006	en
dc.identifier.issn	0018-9294	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16992
dc.subject	Cancer	en
dc.subject	Chemotherapy	en
dc.subject	Chemotherapy optimization	en
dc.subject	Glioblastoma multiforme	en
dc.subject	In silico oncology	en
dc.subject	Monte Carlo	en
dc.subject	Neovasculature	en
dc.subject	Patient individualized optimization	en
dc.subject	Simulation model	en
dc.subject	Temodal™	en
dc.subject	Temodar™	en
dc.subject	Temozolomide	en
dc.subject	Tumor growth	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.other	Algorithms	en
dc.subject.other	Chemotherapy	en
dc.subject.other	Computer simulation	en
dc.subject.other	Medical imaging	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Oncology	en
dc.subject.other	Optimization	en
dc.subject.other	Pathology	en
dc.subject.other	Toxicity	en
dc.subject.other	Tumors	en
dc.subject.other	Chemotherapy optimization	en
dc.subject.other	Drug administration	en
dc.subject.other	Glioblastoma multiforme	en
dc.subject.other	In silico oncology	en
dc.subject.other	Neovasculature	en
dc.subject.other	Patient individualized optimization	en
dc.subject.other	Simulation models	en
dc.subject.other	Temozolomide	en
dc.subject.other	Tumor growth	en
dc.subject.other	Patient treatment	en
dc.subject.other	prodrug	en
dc.subject.other	temozolomide	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	calculation	en
dc.subject.other	cell	en
dc.subject.other	chemotherapy	en
dc.subject.other	genetics	en
dc.subject.other	glioblastoma	en
dc.subject.other	histopathology	en
dc.subject.other	human	en
dc.subject.other	imaging	en
dc.subject.other	metabolite	en
dc.subject.other	Monte Carlo method	en
dc.subject.other	simulation	en
dc.subject.other	solid tumor	en
dc.subject.other	Antineoplastic Agents, Alkylating	en
dc.subject.other	Cell Proliferation	en
dc.subject.other	Cell Survival	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Dacarbazine	en
dc.subject.other	Drug Therapy	en
dc.subject.other	Drug Therapy, Computer-Assisted	en
dc.subject.other	Glioblastoma	en
dc.subject.other	Humans	en
dc.subject.other	Models, Biological	en
dc.subject.other	Treatment Outcome	en
dc.title	A spatiotemporal, patient individualized simulation model of solid tumor response to chemotherapy in vivo: The paradigm of glioblastoma multiforme treated by temozolomide	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TBME.2006.873761	en
heal.identifier.secondary	1658141	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TBME.2006.873761	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A novel four-dimensional, patient-specific Monte Carlo simulation model of solid tumor response to chemotherapeutic treatment in vivo is presented. The special case of glioblastoma multiforme treated by temozolomide is addressed as a simulation paradigm. Nevertheless, a considerable number of the involved algorithms are generally applicable. The model is based on the patient's imaging, histopathologic and genetic data. For a given drug administration schedule lying within acceptable toxicity boundaries, the concentration of the prodrug and its metabolites within the tumor is calculated as a function of time based on the drug pharamacokinetics. A discretization mesh is superimposed upon the anatomical region of interest and within each geometrical cell of the mesh the most prominent biological ""laws"" (cell cycling, necrosis, apoptosis, mechanical restictions, etc.) are applied. The biological cell fates are predicted based on the drug pharmacodynamics. The outcome of the simulation is a prediction of the spatiotemporal activity of the entire tumor and is virtual reality visualized. A good qualitative agreement of the model's predictions with clinical experience supports the applicability of the approach. The proposed model primarily aims at providing a platform for performing patient individualized in silico experiments as a means of chemotherapeutic treatment optimization. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Biomedical Engineering	en
dc.identifier.doi	10.1109/TBME.2006.873761	en
dc.identifier.isi	ISI:000239263400002	en
dc.identifier.volume	53	en
dc.identifier.issue	8	en
dc.identifier.spage	1467	en
dc.identifier.epage	1477	en