DocumentCode :
636792
Title :
In silico oncology: Exploiting clinical studies to clinically adapt and validate multiscale oncosimulators
Author :
Stamatakos, Georgios S. ; Kolokotroni, Eleni ; Dionysiou, Dimitra ; Veith, Christian ; Yoo-Jin Kim ; Franz, Astrid ; Marias, Kostas ; Sabczynski, Joerg ; Bohle, Rainer ; Graf, N.
Author_Institution :
Inst. of Commun. & Comput. Syst., Nat. Tech. Univ. of Athens, Zografos, Greece
fYear :
2013
fDate :
3-7 July 2013
Firstpage :
5545
Lastpage :
5549
Abstract :
This paper presents a brief outline of the notion and the system of oncosimulator in conjunction with a high level description of the basics of its core multiscale model simulating clinical tumor response to treatment. The exemplary case of lung cancer preoperatively treated with a combination of chemotherapeutic agents is considered. The core oncosimulator model is based on a primarily top-down, discrete entity - discrete event multiscale simulation approach. The critical process of clinical adaptation of the model by exploiting sets of multiscale data originating from clinical studies/trials is also outlined. Concrete clinical adaptation results are presented. The adaptation process also conveys important aspects of the planned clinical validation procedure since the same type of multiscale data - although not the same data itself- is to be used for clinical validation. By having exploited actual clinical data in conjunction with plausible literature-based values of certain model parameters, a realistic tumor dynamics behavior has been demonstrated. The latter supports the potential of the specific oncosimulator to serve as a personalized treatment optimizer following an eventually successful completion of the clinical adaptation and validation process.
Keywords :
cancer; discrete event simulation; patient treatment; tumours; chemotherapeutic agents; clinical adaptation; clinical tumor response; clinical validation procedure; core multiscale model; core oncosimulator model; discrete event multiscale simulation approach; in silico oncology; lung cancer; multiscale oncosimulator; primarily top-down discrete entity; tumor dynamics behavior; Adaptation models; Biological system modeling; Cancer; DNA; Data models; Drugs; Tumors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
ISSN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2013.6610806
Filename :
6610806
Link To Document :
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