Data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density. (July 2015)

• Record Type: Journal Article
• Authors: Tylcz, J.-B.; El Alaoui-Lasmaili, K.; Djermoune, E.-H.; Thomas, N.; Faivre, B.; Bastogne, T.
• Is Part Of: Biomedical signal processing and control. Volume 20(2015)
• Page Start: 52
• Page End: 60
• Publication Date: 2015-07
• Subjects: Data-driven modeling -- System identification -- Image processing -- Cancer -- Angiogenesis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.04.008
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Graphical abstract: Abstract : Highlights: Anti-angiogenic effects are characterized by a system identification approach. The solution relies on the association of in vivo protocol and image processing. An empirical model structure is selected according to experimental data. The identified model parameters are used to characterize effects of Bevacizumab. Abstract: Angiogenesis is the phenomenon by which new blood vessels are created from preexisting ones. But this natural process is also involved, in a chaotic way, in tumor development. Many molecules have shown particular efficiency in inhibiting this phenomenon, hopefully leading to either: (i) a reorganization of the neovessels allowing a better tumor uptake of cytotoxic molecules (as chemotherapy) or (ii) a deprivation of the tumor vascular network with the view to starve it. However, characterizing the anti-angiogenic effects of a molecule remains difficult, mainly because the proposed physical modeling approaches have barely been confronted to in vivo data, which are not directly available. This paper presents an original approach to characterize and analyze the anti-angiogenic responses in cancerology that allows biologists to account for spatial and dynamical dimensions of the problem. The proposed solution relies on the association of a specific biological in vivo protocol using skinfold chambers, image processing and dynamic system identification. An empirical model structure of the anti-angiogenic effect of a tested molecule is selected according to experimental data. Finally the model is identified and its parameters are used to characterize and compare responses of the tested molecule.