ArticleBiomechanics and modeling in mechanobiology2022
Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model.
Article in Biomechanics and modeling in mechanobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 14 citations in OpenAlex.
- Dynamic image-informed selection of biomechanical tumor growth models.Biomechanics and modeling in mechanobiology · 2026Article
- The Extracellular Matrix, the Silent 'Architect' of Glioma.Biomedicines · 2026Review
- Model-driven exploration of poro-viscoelasticity in human brain tissue: be careful with the parameters!Interface focus · 2024Article
- Simulating the impact of tumor mechanical forces on glymphatic networks in the brain parenchyma.Biomechanics and modeling in mechanobiology · 2024Article
- Mechanical models and measurement methods of solid stress in tumors.Applied microbiology and biotechnology · 2024Review
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4 authors at 3 institutions in 1 country.
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Abstract
Brain tumours are among the deadliest types of cancer, since they display a strong ability to invade the surrounding tissues and an extensive resistance to common therapeutic treatments. It is therefore important to reproduce the heterogeneity of brain microstructure through mathematical and computational models, that can provide powerful instruments to investigate cancer progression. However, only a few models include a proper mechanical and constitutive description of brain tissue, which instead may be relevant to predict the progression of the pathology and to analyse the reorganization of healthy tissues occurring during tumour growth and, possibly, after surgical resection. Motivated by the need to enrich the description of brain cancer growth through mechanics, in this paper we present a mathematical multiphase model that explicitly includes brain hyperelasticity. We find that our mechanical description allows to evaluate the impact of the growing tumour mass on the surrounding healthy tissue, quantifying the displacements, deformations, and stresses induced by its proliferation. At the same time, the knowledge of the mechanical variables may be used to model the stress-induced inhibition of growth, as well as to properly modify the preferential directions of white matter tracts as a consequence of deformations caused by the tumour. Finally, the simulations of our model are implemented in a personalized framework, which allows to incorporate the realistic brain geometry, the patient-specific diffusion and permeability tensors reconstructed from imaging data and to modify them as a consequence of the mechanical deformation due to cancer growth.
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