DocumentCode :
3167818
Title :
Visualization and modeling of cancer progression
Author :
Papadogiorgaki, Maria ; Zervakis, M.
Author_Institution :
Electron. & Comput. Eng. Dept., Tech. Univ. of Crete, Chania, Greece
fYear :
2013
fDate :
22-23 Oct. 2013
Firstpage :
106
Lastpage :
111
Abstract :
Cancer mathematical modeling constitutes an emerging area of research aiming to predict tumors spatial and temporal evolution. Several mathematical and computational models have appeared in the literature addressing the mechanisms that govern tumor progression and invasion. Modeling techniques are initialized based either on the actual tumor geometries derived from imaging modalities (such as serial MRIs), or on virtual tumor approximation. Cancer modeling is performed using various tissue modeling and evolution techniques, which are generally classified as continuum, discrete and hybrid methods. This paper aims to present a comprehensive overview of tumor modeling approaches and based on significant trends to propose a continuum mathematical model of avascular glioma spatiotemporal evolution. This model takes under consideration the oxygen concentration inside the tumor and its surroundings, which is engaged in tumor-cell survival, proliferation and invasion. The tumor area is divided into layers that form proliferating, hypoxic and necrotic regions of tumor cells. The simulation results for different evolution times demonstrate that the proposed model may provide an essential framework for a patient-specific simulation tool towards the reliable prediction of glioma spatiotemporal progression.
Keywords :
brain; cancer; data visualisation; medical diagnostic computing; solid modelling; tumours; avascular glioma spatiotemporal evolution; brain cancer; cancer mathematical modeling; cancer progression modeling; cancer progression visualization; continuum mathematical model; oxygen concentration; three-dimensional model; tumor-cell hypoxic regions; tumor-cell invasion; tumor-cell necrotic regions; tumor-cell proliferating regions; tumor-cell proliferation; tumor-cell survival; tumors spatial evolution; tumors temporal evolution; Brain modeling; Computational modeling; Equations; Mathematical model; Sociology; Statistics; Tumors; cancer; glioma; hypoxic; modeling; necrotic; oxygen; proliferative; spatiotemporal evolution; tumor cells;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Imaging Systems and Techniques (IST), 2013 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-5790-6
Type :
conf
DOI :
10.1109/IST.2013.6729672
Filename :
6729672
Link To Document :
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