Towards a Framework for Predictive Mathematical Modeling of the Biomechanical Forces Causing Brain Tumor Mass-Effect

GBMs present with different growth phenotypes, ranging from invasive lesions without notable mass-effect to strongly displacing lesions that induce mechanical stresses and result in healthy-tissue deformation, midline shift or herniation. Biomechanical forces, such as those resulting from displacive tumor growth, are recognized to shape the tumor environment and to contribute to tumor progression. We therefore expect that biomechanical forces exerted by lesions on the brain parenchyma have implications on the biophysical level, and that they may affect treatment response and outcome. To better understand the role of biomechanics in the formation of different GBM phenotypes we started developing a framework for the predictive mathematical modeling of mechanical tumor-healthy tissue interaction on the macroscopic level. The tumor’s mass-effect is represented by a solid-mechanics model of brain tissue that computes tumor-induced strain based on local tumor cell concentration. The framework allows to seed tumors at multiple locations in a human brain atlas. It simulates tumor evolution over time and across different brain regions using literature-based parameter estimates for tumor cell proliferation, as well as isotropic motility, and mechanical tissue properties. Despite its simplicity, the mathematical model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to publicly available GBM imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. Here we present and evaluate an extended version of this mechanically-coupled reaction-diffusion model that takes into account tissue anisotropies based on MRI diffusion tensor imaging (MR-DTI). Structural anisotropies in brain tissue have been found to affect the directionality of tumor cell migration and are critical to mechanical behavior. This makes them likely to play a role also in the development of GBM phenotypes

Coronal plane segmental flexibility in thoracic adolescent idiopathic scoliosis assessed by fulcrum-bending radiographs

Knowledge about segmental flexibility in adolescent idiopathic scoliosis is crucial for a better biomechanical understanding, particularly for the development of fusionless, growth-guiding techniques. Currently, there is lack of data in this field. The objective of this study was, therefore, to compute segmental flexibility indices (standing angle minus corrected angle/standing angle). We compared segmental disc angles in 76 preoperative sets of standing and fulcrum-bending radiographs of thoracic curves (paired, two-tailed t tests, p<0.05). The mean standing Cobb angle was 59.7° (range 41.3°-95°) and the flexibility index of the curve was 48.6% (range 16.6-78.8%). The disc angles showed symmetric periapical distribution with significant decrease (all p values <0.0001) for every cephalad (+) and caudad (−) level change. The periapical levels +1 and −1 wedged at 8.3° and 8.7° (range 3.5°-14.8°), respectively. All angles were significantly smaller on the-bending views (p values <0.0001). We noted mean periapical flexibility indices of 46% (+1), 49% (−1), 57% (+2) and 81% (−2), which were significantly less (p<0.001) than for the group of remote levels 105% (+3), 149% (−3), 231% (+4) and 300% (−4). The discal and bony wedging was 60 and 40%, respectively, and mean values 35° and 24° (p<0.0001). Their relationship with the Cobb angle showed a moderate correlation (r=0.56 and 0.45). Functional, radiographic analysis of idiopathic thoracic scoliosis revealed significant, homogenous segmental tethering confined to four periapical levels. Future research will aim at in vivo segmental measurements in three planes under defined load to provide in-depth data for novel therapeutic strategie

Intraoperative determination of the load-displacement behavior of scoliotic spinal motion segments: preliminary clinical results

Introduction: Spinal fusion is a widely and successfully performed strategy for the treatment of spinal deformities and degenerative diseases. The general approach has been to stabilize the spine with implants so that a solid bony fusion between the vertebrae can develop. However, new implant designs have emerged that aim at preservation or restoration of the motion of the spinal segment. In addition to static, load sharing principles, these designs also require a profound knowledge of kinematic and dynamic properties to properly characterise the in vivo performance of the implants. Methods: To address this, an apparatus was developed that enables the intraoperative determination of the load-displacement behavior of spinal motion segments. The apparatus consists of a sensor-equipped distractor to measure the applied force between the transverse processes, and an optoelectronic camera to track the motion of vertebrae and the distractor. In this intraoperative trial, measurements from two patients with adolescent idiopathic scoliosis with right thoracic curves were made at four motion segments each. Results: At a lateral bending moment of 5Nm, the mean flexibility of all eight motion segments was 0.18±0.08°/Nm on the convex side and 0.24±0.11°/Nm on the concave side. Discussion: The results agree with published data obtained from cadaver studies with and without axial preload. Intraoperatively acquired data with this method may serve as an input for mathematical models and contribute to the development of new implants and treatment strategie

Nitinol Stent Oversizing in Patients Undergoing Popliteal Artery Revascularization: A Finite Element Study

Nitinol stent oversizing is frequently performed in peripheral arteries to ensure a desirable lumen gain. However, the clinical effect of mis-sizing remains controversial. The goal of this study was to provide a better understanding of the structural and hemodynamic effects of Nitinol stent oversizing. Five patient-specific numerical models of non-calcified popliteal arteries were developed to simulate the deployment of Nitinol stents with oversizing ratios ranging from 1.1 to 1.8. In addition to arterial biomechanics, computational fluid dynamics methods were adopted to simulate the physiological blood flow inside the stented arteries. Results showed that stent oversizing led to a limited increase in the acute lumen gain, albeit at the cost of a significant increase in arterial wall stresses. Furthermore, localized areas affected by low Wall Shear Stress increased with higher oversizing ratios. Stents were also negatively impacted by the procedure as their fatigue safety factors gradually decreased with oversizing. These adverse effects to both the artery walls and stents may create circumstances for restenosis. Although the ideal oversizing ratio is stent-specific, this study showed that Nitinol stent oversizing has a very small impact on the immediate lumen gain, which contradicts the clinical motivations of the procedure.Swiss National Science FoundationResearch Council of the Kantonsspital AarauSwiss Heart FoundationGotthard Schettler Foundatio

Mesh-based vs. Image-based Statistical Appearance Model of the Human Femur: a Preliminary Comparison Study for the Creation of Finite Element Meshes

Statistical models have been recently introduced in computational orthopaedics to investigate the bone mechanical properties across several populations. A fundamental aspect for the construction of statistical models concerns the establishment of accurate anatomical correspondences among the objects of the training dataset. Various methods have been proposed to solve this problem such as mesh morphing or image registration algorithms. The objective of this study is to compare a mesh-based and an image-based statistical appearance model approaches for the creation of nite element(FE) meshes. A computer tomography (CT) dataset of 157 human left femurs was used for the comparison. For each approach, 30 finite element meshes were generated with the models. The quality of the obtained FE meshes was evaluated in terms of volume, size and shape of the elements. Results showed that the quality of the meshes obtained with the image-based approach was higher than the quality of the mesh-based approach. Future studies are required to evaluate the impact of this finding on the final mechanical simulations

CHIC – A Multi-scale Modelling Platform for in-silico Oncology

Models of normal physiology and disease are necessary in cancer research and clinical practice to optimally exploit the available (pre)clinical multi-scale and multi-modality data. Relevant models often cover multiple spatio-temporal scales and require automated access to heterogeneous and confidential data, making their development, validation and deployment challenging. The CHIC (Computational Horizons in Cancer) [1] project develops computational models for the cancer domain, as well as tools, services and a secure infrastructure for model and data access, and reuse. The architecture is designed to support the creation of complex disease models (hyper-models) by composition of reusable component models (hypo-models). It aims to provide individualized answers to concrete clinical questions by patient-specific parametrization of disease-specific hyper-models. We introduce the CHIC project and illustrate its approach to multi-scale cancer modelling by coupled execution of two component models operating on distinct spatial scales: - OncoSimulator (OS): a spatially discrete model of cancer cell proliferation and treatment effect in function of tumour, treatment and patient-specific parameters [2], implemented as cellular automaton model, - Bio-mechanical Simulator (BMS): a macroscopic continuum model of mechanical effects caused by tumour expansion in patient-specific anatomy, implemented as finite element model, based on [3]. Both component models exchange information about the spatial distribution of cancer cells and mechanical pressure in order to simulate the evolution of tumour volume and shape. Latter is achieved by correcting simple spherical growth (OS) by mechanically induced growth anisotropy (BMS). CHIC is working towards an extensible platform for in-silico oncology with a set of reusable component models at its core, covering sub-cellular, cellular and super-cellular scales. Viability of infrastructure and composite hyper-models is being evaluated against clinical questions in the treatment of Nephroblastoma, Glioblastoma and Non-small Cell Lung Cancer. [1] http://www.chic-vph.eu/ [2] Stamatakos, G., 2011. In silico oncology: PART I Clinically oriented cancer multilevel modeling based on discrete event simulation. In: Deisboeck, T., Stamatakos, G. (Eds.), Multiscale Cancer Modeling. Chapman & Hall/CRC, Boca Raton, Florida,USA. [3] C. P. May, E. Kolokotroni, G. S. Stamatakos, and P. Büchler, ‘Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation’, Progress in Biophysics and Molecular Biology, vol. 107, no. 1, pp. 193–199, Oct. 2011

A multi-criteria decision support for optimal instrumentation in scoliosis spine surgery

In adolescent idiopathic scoliosis, the selection of an optimal instrumentation configuration for correcting a specific spinal deformity is a challenging combinatorial problem. Current methods mostly rely on surgeons' expertise, which has been shown to lead to different treatment strategies for the same patients. In this work, a mathematical model of the human spine derived from in-vitro experimentally-obtained data was used to simulate the biomechanical behavior of the spine under the application of corrective forces and torques. The corrective forces and torques were optimized based on the particle swarm optimization algorithm for each combinatorially possible instrumentation strategy. Finally, a multi-criteria decision support for optimal instrumentation in scoliosis spine surgery has been proposed and applied to five patient data sets exhibiting similar spinal deformities according to two commonly used classification systems. Results indicated that the classification of the spinal deformities based on the current standardized clinical classifications systems is not a sufficient condition for recommending selective fusion of spinal motion segments. In addition, the particle swarm optimization algorithm was successfully applied to solve a realistic interdisciplinary clinical problem in a patient-specific fashion. The proposed method enables a better understanding of the biomechanical behavior of spinal structures and has the potential to become a standard tool in preoperative plannin