Literature DB >> 19437361

'In silico' oncology for clinical decision making in the context of nephroblastoma.

N Graf1, A Hoppe, E Georgiadi, R Belleman, C Desmedt, D Dionysiou, M Erdt, J Jacques, E Kolokotroni, A Lunzer, M Tsiknakis, G Stamatakos.   

Abstract

The present paper outlines the initial version of the ACGT (Advancing Clinico-Genomic Trials) -- an Integrated Project, partly funded by the EC (FP6-2005-IST-026996)I-Oncosimulator as an integrated software system simulating in vivo tumour response to therapeutic modalities within the clinical trials environment aiming to support clinical decision making in individual patients. Cancer treatment optimization is the main goal of the system. The document refers to the technology of the system and the clinical requirements and the types of medical data needed for exploitation in the case of nephroblastoma. The outcome of an initial step towards the clinical adaptation and validation of the system is presented and discussed. Use of anonymized real data before and after chemotherapeutic treatment for the case of the SIOP 2001/GPOH nephroblastoma clinical trial constitutes the basis of the clinical adaptation and validation process. By using real medical data concerning nephroblastoma for a single patient in conjunction with plausible values for the model parameters (based on available literature) a reasonable prediction of the actual tumour volume shrinkage has been made possible. Obviously as more and more sets of medical data are exploited the reliability of the model "tuning" is expected to increase. The successful performance of the initial combined ACGT Oncosimulator platform, although usable up to now only as a test of principle, has been a particularly encouraging step towards the clinical translation of the system, being the first of its kind worldwide.

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Year:  2009        PMID: 19437361     DOI: 10.1055/s-0029-1216368

Source DB:  PubMed          Journal:  Klin Padiatr        ISSN: 0300-8630            Impact factor:   1.349


  5 in total

1.  Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm.

Authors:  K Marias; D Dionysiou; V Sakkalis; N Graf; R M Bohle; P V Coveney; S Wan; A Folarin; P Büchler; M Reyes; G Clapworthy; E Liu; J Sabczynski; T Bily; A Roniotis; M Tsiknakis; E Kolokotroni; S Giatili; C Veith; E Messe; H Stenzhorn; Yoo-Jin Kim; S Zasada; A N Haidar; C May; S Bauer; T Wang; Y Zhao; M Karasek; R Grewer; A Franz; G Stamatakos
Journal:  Interface Focus       Date:  2011-03-30       Impact factor: 3.906

2.  Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model.

Authors:  Georgios S Stamatakos; Eleni C Georgiadi; Norbert Graf; Eleni A Kolokotroni; Dimitra D Dionysiou
Journal:  PLoS One       Date:  2011-03-03       Impact factor: 3.240

3.  Multiscale Cancer Modeling and In Silico Oncology: Emerging Computational Frontiers in Basic and Translational Cancer Research.

Authors:  Georgios S Stamatakos; Norbert Graf; Ravi Radhakrishnan
Journal:  J Bioeng Biomed Sci       Date:  2013-05-25

Review 4.  Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.

Authors:  Mahnoor Naseer Gondal; Safee Ullah Chaudhary
Journal:  Front Oncol       Date:  2021-11-24       Impact factor: 6.244

5.  Dealing with diversity in computational cancer modeling.

Authors:  David Johnson; Steve McKeever; Georgios Stamatakos; Dimitra Dionysiou; Norbert Graf; Vangelis Sakkalis; Konstantinos Marias; Zhihui Wang; Thomas S Deisboeck
Journal:  Cancer Inform       Date:  2013-05-07
  5 in total

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