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Stamatakos G, Dionysiou D, Lunzer A, Belleman R, Kolokotroni E, Georgiadi E, Erdt M, Pukacki J, Rueping S, Giatili S, d'Onofrio A, Sfakianakis S, Marias K, Desmedt C, Tsiknakis M, Graf N. The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context. IEEE J Biomed Health Inform 2014; 18:840-54. [DOI: 10.1109/jbhi.2013.2284276] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Georgios Stamatakos
- Institute of Communication and Computer Systems, National Technical University of Athens, In Silico Oncology Group, 9 Iroon Polytechniou, Zografos, Greece
| | - Dimitra Dionysiou
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Eleni Kolokotroni
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | - Eleni Georgiadi
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | - Juliusz Pukacki
- Poznan Supercomputing and Networking Center (PSNC), Poznan, Poland
| | - Stefan Rueping
- Fraunhofer IAIS, Schloss Birlinghoven, St. Augustin, Germany
| | - Stavroula Giatili
- Oncology Group, Institute of Communication and Computer Systems, National Technical University of Athens, GR , Greece
| | | | | | - Kostas Marias
- Foundation for Research and Technology Hellas, Heraklion, Greece
| | | | - Manolis Tsiknakis
- Department of Informatics Engineering, TEI Crete and the Computational Medicine Laboratory, Institute of Computer Science, FORTH , Heraklion, Greece
| | - Norbert Graf
- University Hospital of the Saarland, Pediatric Haematology and Oncology, Homburg, Germany
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Georgiadi EC, Dionysiou DD, Graf N, Stamatakos GS. Towards in silico oncology: adapting a four dimensional nephroblastoma treatment model to a clinical trial case based on multi-method sensitivity analysis. Comput Biol Med 2012; 42:1064-78. [PMID: 23063290 DOI: 10.1016/j.compbiomed.2012.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2012] [Revised: 07/31/2012] [Accepted: 08/24/2012] [Indexed: 11/28/2022]
Abstract
In the past decades a great progress in cancer research has been made although medical treatment is still widely based on empirically established protocols which have many limitations. Computational models address such limitations by providing insight into the complex biological mechanisms of tumor progression. A set of clinically-oriented, multiscale models of solid tumor dynamics has been developed by the In Silico Oncology Group (ISOG), Institute of Communication and Computer Systems (ICCS)-National Technical University of Athens (NTUA) to study cancer growth and response to treatment. Within this context using certain representative parameter values, tumor growth and response have been modeled under a cancer preoperative chemotherapy protocol in the framework of the SIOP 2001/GPOH clinical trial. A thorough cross-method sensitivity analysis of the model has been performed. Based on the sensitivity analysis results, a reasonable adaptation of the values of the model parameters to a real clinical case of bilateral nephroblastomatosis has been achieved. The analysis presented supports the potential of the model for the study and eventually the future design of personalized treatment schemes and/or schedules using the data obtained from in vitro experiments and clinical studies.
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Affiliation(s)
- Eleni Ch Georgiadi
- In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Greece
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Stamatakos GS, Georgiadi EC, Graf N, Kolokotroni EA, Dionysiou DD. Exploiting clinical trial data drastically narrows the window of possible solutions to the problem of clinical adaptation of a multiscale cancer model. PLoS One 2011; 6:e17594. [PMID: 21407827 PMCID: PMC3048172 DOI: 10.1371/journal.pone.0017594] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Accepted: 01/28/2011] [Indexed: 11/30/2022] Open
Abstract
The development of computational models for simulating tumor growth and response to treatment has gained significant momentum during the last few decades. At the dawn of the era of personalized medicine, providing insight into complex mechanisms involved in cancer and contributing to patient-specific therapy optimization constitute particularly inspiring pursuits. The in silico oncology community is facing the great challenge of effectively translating simulation models into clinical practice, which presupposes a thorough sensitivity analysis, adaptation and validation process based on real clinical data. In this paper, the behavior of a clinically-oriented, multiscale model of solid tumor response to chemotherapy is investigated, using the paradigm of nephroblastoma response to preoperative chemotherapy in the context of the SIOP/GPOH clinical trial. A sorting of the model's parameters according to the magnitude of their effect on the output has unveiled the relative importance of the corresponding biological mechanisms; major impact on the result of therapy is credited to the oxygenation and nutrient availability status of the tumor and the balance between the symmetric and asymmetric modes of stem cell division. The effect of a number of parameter combinations on the extent of chemotherapy-induced tumor shrinkage and on the tumor's growth rate are discussed. A real clinical case of nephroblastoma has served as a proof of principle study case, demonstrating the basics of an ongoing clinical adaptation and validation process. By using clinical data in conjunction with plausible values of model parameters, an excellent fit of the model to the available medical data of the selected nephroblastoma case has been achieved, in terms of both volume reduction and histological constitution of the tumor. In this context, the exploitation of multiscale clinical data drastically narrows the window of possible solutions to the clinical adaptation problem.
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Affiliation(s)
- Georgios S Stamatakos
- In Silico Oncology Group, Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
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