Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth Using a Mechanically Coupled Reaction–Diffusion Model

Abler, Daniel; Rockne, Russell C.; Büchler, Philippe (2019). Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth Using a Mechanically Coupled Reaction–Diffusion Model. In: Tavares, J.; Fernandes, P. (eds.) New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering. Lecture Notes in Computational Vision and Biomechanics: Vol. 33 (pp. 37-48). Cham: Springer 10.1007/978-3-030-23073-9_3

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Glioblastoma (GBM) is the most frequent malignant brain tumor in adults and presents with different growth phenotypes. We use a mechanically coupled reaction–diffusion model to study the influence of structural brain tissue anisotropy on tumor growth. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatiotemporal evolution was simulated using the Finite Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. The impact of tissue anisotropy on tumor shape was strongly location dependent and highest for tumors in brain regions with a single dominating white matter fiber direction, such as the corpus callosum. Despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding anatomic location and similar volume. These findings confirm previous simulation studies, suggesting that cell migration along WM fiber tracks is not a major determinant of tumor shape in the setting of reaction–diffusion-based tumor growth models and for most locations across the brain.

Item Type:

Book Section (Book Chapter)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

UniBE Contributor:

Abler, Daniel and Büchler, Philippe

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering

ISSN:

2212-9391

ISBN:

978-3-030-23072-2

Series:

Lecture Notes in Computational Vision and Biomechanics

Publisher:

Springer

Funders:

[124] H2020-MSCA-IF-2016 Project ID 753878

Language:

English

Submitter:

Daniel Jakob Silvester Abler

Date Deposited:

13 Aug 2019 12:53

Last Modified:

05 Nov 2019 07:07

Publisher DOI:

10.1007/978-3-030-23073-9_3

Related URLs:

BORIS DOI:

10.7892/boris.131979

URI:

https://boris.unibe.ch/id/eprint/131979

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