Evaluating the Effect of Tissue Anisotropy on Brain Tumor Growth Using a Mechanically Coupled Reaction–Diffusion Model
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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.
KeywordsGlioma Anisotropy DTI Mass effect Reaction–diffusion model Biomechanics
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 753878. Calculations were performed on UBELIX (http://www.id.unibe.ch/hpc), the HPC cluster at the University of Bern.
- 1.Abler D et al (2018) Evaluation of a mechanically coupled reaction-diffusion model for macroscopic brain tumor growth. In: Gefen A et al (eds) Computer methods in biomechanics and biomedical engineering. Springer International Publishing, Cham, pp 57–64Google Scholar
- 6.Elazab A et al (2017) Post-surgery glioma growth modeling from magnetic resonance images for patients with treatment. Sci Rep 7(1)Google Scholar
- 12.Menze B et al (2014) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging, p 33Google Scholar
- 13.Mohamed A et al (2005) Finite element modeling of brain tumor mass-effect from 3D medical images. In: Medical image computing and computer-assisted intervention-MICCAI 2005. Lecture notes in computer science 3749. Springer, Berlin, pp 400–408Google Scholar
- 17.Swan A et al (2017) A patient-specific anisotropic diffusion model for brain tumour spread. Bull Math BiolGoogle Scholar
- 21.Wittek A et al (2010) Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time; application to non-rigid neuroimage registration. Prog Biophys Mol Biol. Special Issue on Biomechanical Modelling of Soft Tissue Motion 103(2–3):292–303CrossRefGoogle Scholar