Periodic Reporting for period 2 - GlimS (Patient-specific tumour growth model for quantification of mechanical 'markers' in malignant gliomas: Implications for treatment outcomes.)
Berichtszeitraum: 2019-06-01 bis 2020-05-31
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.
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.
GlimS investigated mathematical models that capture two dominant characteristics of macroscopic glioma growth: healthy-tissue infiltration and tissue-displacing mass-effect. These models allow simulating the spatio-temporal evolution of tumor growth with patient-specific parameters in a computational representation of the human brain. GlimS evaluated models with and without mass-effect, and combined them with approaches to solve the inverse problem: to identify the model parameters that best reproduce the imaging data of a patient's tumor. Mathematical models, image processing algorithms, and parameter estimation approaches were implemented using open-source libraries. The resulting software is available on the project website and enabled the study of specific questions linked to the role of biomechanics in the mathematical modeling of this tumor:
First, GlimS investigated the significance of directional (anisotropic) brain tissue characteristics for the formation of tumor shapes in mathematical growth models. Brain tissue is spatially heterogeneous. Besides, its microscopic organization gives rise to spatial structures that influence the direction of tumor cell migration. In an in-silico study, GlimS examined how this direction-dependence of tissue properties affects tumor shape. The results indicate that tissue anisotropy is not a major determinant of macroscopic tumor shape in most growth locations.
Second, GlimS evaluated image-derived surrogate measures of tumor mass-effect. Tumor mass-effect is poorly quantified in clinical practice. The most common quantitative measure, Brain midline shift (MLs), is determined from 2D image slices. Lateral ventricle displacement (LVd) is an alternative metric that accounts for the entire 3D anatomy of the brain. Comparing MLs and LVd in an in-silico cohort study revealed LVd as the more robust and predictive measure. It is insensitive to tumor location, highly correlates with tumor volume, and is a good predictor of tumor-induced pressure.
Third, GlimS developed a method for characterizing the infiltrative and displacive growth tendencies of individual tumors. This approach relies on estimating patient-specific parameters of the mechanically-coupled tumor growth model from clinical Magnetic Resonance (MR) images. After confirming that model parameters are recoverable in an in-silico study, GlimS demonstrated that patient-specific tumor characteristics can be estimated from clinical MR imaging data.
GlimS research was presented at 9 international conferences, 8 national and institutional meetings, and resulted in 3 conference papers and publications so far. Currently, we seek to exploit the results in a follow-up study that focuses on evaluating the clinical predictiveness of model-derived biomechanical tumor characteristics.
First, GlimS investigated the significance of directional (anisotropic) brain tissue characteristics for the formation of tumor shapes in mathematical growth models. Brain tissue is spatially heterogeneous. Besides, its microscopic organization gives rise to spatial structures that influence the direction of tumor cell migration. In an in-silico study, GlimS examined how this direction-dependence of tissue properties affects tumor shape. The results indicate that tissue anisotropy is not a major determinant of macroscopic tumor shape in most growth locations.
Second, GlimS evaluated image-derived surrogate measures of tumor mass-effect. Tumor mass-effect is poorly quantified in clinical practice. The most common quantitative measure, Brain midline shift (MLs), is determined from 2D image slices. Lateral ventricle displacement (LVd) is an alternative metric that accounts for the entire 3D anatomy of the brain. Comparing MLs and LVd in an in-silico cohort study revealed LVd as the more robust and predictive measure. It is insensitive to tumor location, highly correlates with tumor volume, and is a good predictor of tumor-induced pressure.
Third, GlimS developed a method for characterizing the infiltrative and displacive growth tendencies of individual tumors. This approach relies on estimating patient-specific parameters of the mechanically-coupled tumor growth model from clinical Magnetic Resonance (MR) images. After confirming that model parameters are recoverable in an in-silico study, GlimS demonstrated that patient-specific tumor characteristics can be estimated from clinical MR imaging data.
GlimS research was presented at 9 international conferences, 8 national and institutional meetings, and resulted in 3 conference papers and publications so far. Currently, we seek to exploit the results in a follow-up study that focuses on evaluating the clinical predictiveness of model-derived biomechanical tumor characteristics.
GlimS research informs best-practices for the mathematical modeling of macroscopic tumor growth. It confirmed the limited effect of brain tissue anisotropy on tumor shapes for the first time in the context of mechanically-coupled growth models. Through a thorough evaluation of the developed biomechanical tumor growth model, GlimS provided an unprecedented amount of information for further refinement of mathematical modeling approaches.
GlimS research guides the use of existing methods for quantifying brain tumor mass-effect, and developed a new approach for characterizing tumor growth phenotypes from routine clinical MR imaging. Through a computational study, GlimS provided the first quantitative comparison of different image-based tumor mass-effect metrics. Furthermore, on a diverse cohort of patients, the project demonstrated that infiltrative and displacive growth characteristics of individual tumors can be estimated simultaneously from single time-point routine clinical imaging.
Jointly, these findings and developments enable a more accurate assessment of the mechanical impact of individual brain tumors. The software developed in GlimS is publicly available and implements workflows for automated tumor growth characterization from suitable medical imaging data.
GlimS thus enables and proposes multiple lines of further research into the clinical significance of distinct mechanical tumor growth phenotypes. It also constitutes an important technical step towards detailed noninvasive quantification of the macroscopic biomechanical forces acting during tumor growth. Both research directions will further advance our understanding of the biological effects of tumor-induced mechanical forces and their clinical implications.
GlimS research guides the use of existing methods for quantifying brain tumor mass-effect, and developed a new approach for characterizing tumor growth phenotypes from routine clinical MR imaging. Through a computational study, GlimS provided the first quantitative comparison of different image-based tumor mass-effect metrics. Furthermore, on a diverse cohort of patients, the project demonstrated that infiltrative and displacive growth characteristics of individual tumors can be estimated simultaneously from single time-point routine clinical imaging.
Jointly, these findings and developments enable a more accurate assessment of the mechanical impact of individual brain tumors. The software developed in GlimS is publicly available and implements workflows for automated tumor growth characterization from suitable medical imaging data.
GlimS thus enables and proposes multiple lines of further research into the clinical significance of distinct mechanical tumor growth phenotypes. It also constitutes an important technical step towards detailed noninvasive quantification of the macroscopic biomechanical forces acting during tumor growth. Both research directions will further advance our understanding of the biological effects of tumor-induced mechanical forces and their clinical implications.