Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - GlimS (Patient-specific tumour growth model for quantification of mechanical \'markers\' in malignant gliomas: Implications for treatment outcomes.)

Teaser

Brain tumors represent a rare but serious medical condition. Gliomas are the most frequent primary malignant brain tumors in adults with an incidence of six cases per 100000. Glioblastoma multiforme (GBM) is the most frequent and malignant subtype of glioma that accounts for...

Summary

Brain tumors represent a rare but serious medical condition. Gliomas are the most frequent primary malignant brain tumors in adults with an incidence of six cases per 100000. Glioblastoma multiforme (GBM) is the most frequent and malignant subtype of glioma that accounts for about 50% of these cases. GBM infiltrates surrounding healthy tissue, and its growth is often accompanied by tissue compression and displacement. This so-called ‘mass-effect’ leads to an increase in intra-cranial pressure and the progressive on-set of a multitude of pressure-induced symptoms. Standard-of-Care treatment therefore involves surgical resection of the bulk tumor to reduce the symptoms of mass-effect, followed by a combination of chemo- and radiation therapy to manage residual tumor. Long-term prognosis for GBM remains poor, with median overall-survival below 1.5 years.

GBM represents a very heterogeneous class of tumors that differ in their biological characteristics and macroscopic growth phenotypes: covering the spectrum from diffusely infiltrating tumors to nodular tumors with well delineated boundaries, and varying degrees of mass-effect including strongly displacing tumors that result in healthy-tissue deformation. The physical forces generated during tumor growth are recognized to shape the tumor microenvironment: Tissue compression and mechanical stresses unleash a cascade of biophysical and biochemical processes that drive tumors to more aggressive phenotypes and affect the normal functioning of the surrounding healthy tissue. In the brain, elevated mechanical stress results in reduced neuronal integrity which translates into functional loss and increased mortality in brain tumor patients. This suggests that the propensity of an individual tumor to displace healthy tissue can provide information about the tumor micro-environment and might be of predictive value for treatment and outcome.

The GlimS project seeks to improve the understanding of the role of biomechanics in GBM growth and to identify biomarkers that may be used to inform clinical decision making for individual patients. It addresses these questions by combining mechanistic mathematical modelling with statistical analysis of patient data.

Work performed

Over the course of this project, a model for biomechanically-coupled tumor growth has been developed that captures and simulates the main characteristics of macroscopic GBM growth: its infiltrative nature and the resulting tissue-displacing mass-effect. Based on this model, we implemented a framework for glioma growth simulation and parameter estimation. The framework is built from open source software components and can be accessed through the project’s webpage.

This platform enabled us to investigate in-silico the importance of brain tissue anisotropy for the formation of tumor shapes. Brain tissue is spatially heterogeneous and its microscopic organization gives rise to a spatial structure that influences tumor cell migration and the tissue’s biomechanical characteristics. Accounting for the direction-dependence of growth and mechanical tissue properties is therefore believed to be important for accurate modeling. Our results from an in-silico study of the combined effect of anisotropic growth and mechanical tissue characteristics on tumor shape indicate that tissue anisotropy is not a major determinant of macroscopic tumor shape, except for growth locations where a single dominant direction prevails throughout a larger contiguous volume segment.

Using our platform, we were also able to evaluate different image-derived surrogate measures of tumor mass-effect. Despite its importance, tumor mass-effect is poorly quantified in clinical practice. The most common measure of mass-effect is brain midline shift (MLs). An alternative measure, lateral ventricle displacement (LVd), has recently been proposed, however, no comparison between both measures existed. We evaluated MLs and LVd in an in-silico study and identified LVd as the more robust and predictive measure as it showed to be largely insensitive to tumor location, highly correlated with tumor volume and a good predictor of tumor-induced pressure.

As evaluation and refinement of this platform continues, we have devised an approach for estimating the parameters of our model from clinical MR-imaging. This allows us to characterize GBM growth not only on a spectrum from mostly nodular to diffuse, but additionally captures the displacive dimension of GBM growth. Using this approach, we are investigating the relation between invasive tumor growth, mass-effect and its manifestation on clinical imaging.

Final results

More than two decades of mathematical modeling research towards quantifying, explaining and reproducing in-silico the invasive growth characteristics of GBM resulted in modeling approaches that enable tumor characterization along a spectrum of growth phenotypes ranging from nodular to diffuse with differing degrees of healthy tissue involvement. However, tumor-induced mass-effect remains poorly quantified in clinical practice and best-practice modeling approaches for this GBM growth dimension are limited.

The GlimS project developed a mathematical model and simulation framework that accounts for the mechanical impact of the growing tumor. By establishing performance and limitations of the developed biomechanically-coupled tumor growth model, this work will inform best-practices for the mathematical modelling of macroscopic tumor-growth. The developments of this project towards an approach for the biomechanical characterization of growth phenotypes may lead to the discovery of a novel model-based ‘biomarker’ that distinguishes strongly from weakly-displacive tumors with potential clinical applications.

Website & more info

More info: http://www.glims.ch.