GlimS · Patient-specific tumour growth model for quantification of mechanical 'markers' in malignant gliomas: Implications for treatment outcomes.

Patient-specific tumour growth model for quantification of mechanical 'markers' in malignant gliomas: Implications for treatment outcomes.

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 100’000. Glioblastoma multiforme (GBM) is the most common and malignant subtype of glioma and accounts for about 50% of these cases. GBM infiltrates surrounding healthy tissue, and its growth generates physical forces that frequently result in tissue compression and displacement. This 'mass-effect' is a major cause of the clinical symptoms seen in brain cancer patients: Tumor-induced compressive mechanical forces are linked to neurological dysfunction and shortened survival in patients; they also provoke neuronal loss and drive tumors towards more aggressive phenotypes. 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 the residual tumor. Despite aggressive treatment, the long-term prognosis for GBM remains poor, with a median overall-survival below 1.5 years. The GlimS project is part of a growing body of research that employs mathematical modeling to gain mechanistic insights into the determinants of tumor growth with the ultimate goal to improve the management of this condition. Although brain tumor growth has been studied extensively, mathematical modeling studies frequently neglect the displacive growth characteristics of these tumors. Despite mounting evidence for the critical role of biomechanical forces in tumor growth, tumor-induced mass-effect remains poorly quantified in clinical practice. GlimS investigated the role of biomechanical forces for GBM by mathematical modeling and clinical image analysis. Its research objectives were to assess and improve models of tumor growth, and to characterize tumor growth phenotypes by their mechanical impact. The computational techniques and tools developed in GlimS were evaluated carefully, thus enabling personalized tumor growth simulations across virtual patient cohorts. Most importantly, GlimS demonstrated a method for simultaneously characterizing a tumors' tendency to infiltrate and displace surrounding healthy tissue from routine clinical imaging data. This ability to distinguish different tumor growth phenotypes enables further research into bio-mechanical imaging markers.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз