2020/11/06 | Research | Biomechanics

Patient-specific tumor growth model

Glioblastoma multiforme (GBM) is an aggressive type of brain cancer associated with dismal prognosis. Tumor growth often causes compression to the surrounding tissue and induces mechanical stress, increasing the intra cranial pressure and impairing neurological function. Daniel Abler, Marie Skłodowska-Curie research fellow and member of the ARTORG Computational Bioengineering lab, has now developed a computational approach to study the role of the biomechanical forces generated by GBM growth, together with scientists from the Beckman Research Institute, USA, for the Horizon2020 GlimS project.

Model-based approach for quantifying the infiltrative and displacive growth characteristics of brain tumors from routine clinical imaging (© Daniel Abler, ARTORG Center, University of Bern)

Abler worked under the assumptions that the infiltrative growth of GBM can be described mathematically as a reaction-diffusion process and that an increase in local tumour cell concentration results in volumetric growth which in turn induces mechanical stresses in the tissue. “I employed inverse modelling to estimate macroscopic growth characteristics of GBM from routine clinical MR-imaging,” he explains. In providing spatially resolved estimates of tumour cell density and tumour-induced mechanical stresses the approach supplements MRI data.

The GlimS tool enables non-invasive estimation of the macroscopic biomechanical forces acting during tumour growth. From a fundamental perspective, this will advance understanding the clinical consequences posed by these tumour induced forces, and enable further investigation into the role of distinct GBM growth phenotypes for clinical outcome. The GlimS team now wants to correlate the model-predicted growth characteristics with clinical data. This could pave the way towards identifying mechanically informed GBM biomarkers which may help further stratify patients and make more informed treatment decisions.


Full article on CORDIS, EU research results

Computational Bioengineering