Multilevel cancer modeling in the clinical environment: Simulating the behavior of wilms tumor in the context of the SIOP 2001/GPOH clinical trial and the ACGT project

Georgiadi, ECh; Stamatakos, GS; Graf, NM; Kolokotroni, EA; Dionysiou, DD; Hoppe, A; Uzunoglu, NK

dc.contributor.author	Georgiadi, ECh	en
dc.contributor.author	Stamatakos, GS	en
dc.contributor.author	Graf, NM	en
dc.contributor.author	Kolokotroni, EA	en
dc.contributor.author	Dionysiou, DD	en
dc.contributor.author	Hoppe, A	en
dc.contributor.author	Uzunoglu, NK	en
dc.date.accessioned	2014-03-01T02:45:39Z
dc.date.available	2014-03-01T02:45:39Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32317
dc.subject	Clinical Data	en
dc.subject	Clinical Practice	en
dc.subject	Clinical Trial	en
dc.subject	Dynamic Model	en
dc.subject	in silico	en
dc.subject	Model Development	en
dc.subject	Solid Tumor	en
dc.subject	Tumor Cells	en
dc.subject	Tumor Growth	en
dc.subject	wilms tumor	en
dc.subject.other	Biological mechanisms	en
dc.subject.other	Chemotherapeutic agents	en
dc.subject.other	Clinical data	en
dc.subject.other	Clinical environments	en
dc.subject.other	Clinical practices	en
dc.subject.other	Clinical trial	en
dc.subject.other	Dynamics models	en
dc.subject.other	In-silico	en
dc.subject.other	Multiscale	en
dc.subject.other	Numerical results	en
dc.subject.other	Optimal treatment	en
dc.subject.other	Optimization system	en
dc.subject.other	Patient specific	en
dc.subject.other	Relative importance	en
dc.subject.other	Solid tumors	en
dc.subject.other	Transition rates	en
dc.subject.other	Tumor cells	en
dc.subject.other	Tumor growth	en
dc.subject.other	Tumor models	en
dc.subject.other	Tumor response	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Clarification	en
dc.subject.other	Convergence of numerical methods	en
dc.subject.other	Dynamics	en
dc.subject.other	Optimization	en
dc.subject.other	Patient treatment	en
dc.subject.other	Tumors	en
dc.title	Multilevel cancer modeling in the clinical environment: Simulating the behavior of wilms tumor in the context of the SIOP 2001/GPOH clinical trial and the ACGT project	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696759	en
heal.identifier.secondary	4696759	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696759	en
heal.publicationDate	2008	en
heal.abstract	Mathematical and computational tumor dynamics models can provide considerable insight into the relative importance and interdependence of related biological mechanisms. They may also suggest the existence of optimal treatment windows in the generic setting. Nevertheless, they cannot be translated into clinical practice unless they undergo a strict and thorough clinical validation and adaptation. In this context one of the major actions of the EC funded project ""Advancing Clinico-Genomic Trials on Cancer"" (ACGT) is dedicated to the development of a patient specific four dimensional multiscale tumor model mimicking the nephroblastoma tumor response to chemotherapeutic agents according to the SIOP 2001/GPOH clinical trial. Combined administration of vincristine and dactinomycin is considered. The patient's pseudoanonymized imaging, histopathological, molecular and clinical data are carefully exploited. The paper briefly outlines the basics of the model developed by the In Silico Oncology Group and particularly stresses the effect of stem/clonogenic, progenitor and differentiated tumor cells on the overall tumor dynamics. The need for matching the cell category transition rates to the cell category relative populations of free tumor growth for an already large solid tumor at the start of simulation has been clarified. A technique has been suggested and succesfully applied in order to ensure satisfaction of this condition. The concept of a nomogram matching the cell category transition rates to the cell category relative populations at the treatment baseline is introduced. Convergence issues are addressed and indicative numerical results are presented. Qualitative agreement of the model's behavior with the corresponding clinical trial experience supports its potential to constitute the basis for an optimization system within the clinical environment following completion of its clinical validation and optimization. In silico treatment experimentation in the patient individualized context is expected to constitute the primary application of the model.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696759	en