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The hemodynamic behavior following endovascular treatment of patients with peripheral arterial disease plays a significant role on the occurrence of restenosis in femoro-popliteal (FP) arteries. The atheroprone flow conditions that are generally accepted to promote restenosis can be calculated by computational fluid dynamics (CFD) analyses, and these results can be used to assess individualized treatment outcomes. However, the impact of endovascular therapy on the flow behaviors of FP arteries are still poorly understood, as the imaging modalities used in existing numerical works (X-ray angiography, computed tomography angiography) are unable to accurately represent the post-treatment arterial geometry due to their low resolutions. Therefore, this study proposes a new algorithm that combines intra-arterial lumen geometry obtained from high-resolution optical coherence tomography (OCT) images with centerlines generated from X-ray images to reconstruct the FP artery with an in-plane resolution of 10 µm. This superior accuracy allows modeling characteristic geometrical structures, such as angioplasty-induced arterial dissections, that are too small to be reconstructed with other imaging modalities. The framework is applied on the clinical data of patients treated either with only-percutaneous transluminal angioplasty (PTA) (n = 4) or PTA followed by stenting (n = 4). Based on the generated models, PTA was found to cause numerous arterial dissections, covering approximately 10% of the total surface area of the lumen, whereas no dissections were identified in the stented arteries. CFD simulations were performed to investigate the hemodynamic conditions before and after treatment. Regardless of the treatment method, the areas affected by low time-averaged wall shear stress (

Physical forces are recognized to play a critical role in shaping the micro-environment of tumors. Compression of cancer and stromal cells, as well as blood and lymphatic vessels, are direct consequences of mechanical solid stress, the compressive and tensile mechanical forces exerted by the solid components of the tissue. By altering the mechanical micro-environment of tumors, elevated solid stress can affect their pathophysiology, driving tumors to more aggressive phenotypes and compromise therapeutic outcome [1]. Mechanical stress also affects healthy tissue: It causes neuronal loss in brain tissue [2], and is linked to neurological deficits and reduced survival in patients with glioblastoma (GBM) [3], the most common malignant primary brain tumor in adults. Given their far-reaching micro- and macroscopic consequences, tumor-induced mechanical forces may provide mechanistic insights into inter and intra-tumor heterogeneities, differential response to treatment and other phenotypical characteristics. In this contribution, we survey the literature of spatial tumor growth modeling from a perspective of macroscopic tissue mechanics to assess the current status of mechanically-coupled growth models and to identify opportunities for further research: We summarize the types of modeling approaches previously used for capturing tumor-induced mechanical effects and their biological or physiological consequences. Based on this review, we identify the scenarios in which accounting for tissue mechanics proved to improve calibration to and prediction of clinical data. Drawing from examples of our [4] and others’ research on mechanically-coupled growth modeling for GBM, we discuss challenges involved in the implementation and calibration of such models. In this context, we identify areas of mechanically-coupled growth modeling where further research is needed and explore application opportunities that such models may open.

Glioblastoma (GBM), the most frequent malignant brain tumor in adults, is char- acterized by rapid growth and healthy tissue invasion. Long-term prognosis for GBM remains poor with median overall survival between 1 y to 2 y [15]. GBM presents with different growth phenotypes, ranging from invasive tumors without notable mass-effect to strongly displacing lesions. Biomechanical forces, such as those resulting from displacive tumor growth, shape the tumor environment and contribute to tumor progression [9]. We present an extended version of a mechanically–coupled reaction-diffusion model of brain tu- mor growth [1] that 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. This model yielded realistic estimates of the mechanical impact of a growing tumor on intra-cranial pressure. However, comparison to imaging data showed that asymmetric shapes could not be reproduced by isotropic growth assumptions. We modified this model to account for structural tissue anisotropy which is known to affect the directionality of tumor cell migration and may influence the mechanical behavior of brain tissue. Tumors were seeded at multiple locations in a human MR-DTI brain atlas and their spatio-temporal evolution was simulated using the Finite-Element Method. We evaluated the impact of tissue anisotropy on the model’s ability to reproduce the aspherical shapes of real pathologies by comparing predicted lesions to publicly available GBM imaging data. We found the impact on tumor shape to be strongly location dependent and highest for tumors located in brain regions that are characterized by a single dominant white matter direction, such as the corpus callosum. However, despite strongly anisotropic growth assumptions, all simulated tumors remained more spherical than real lesions at the corresponding location and similar volume. This finding is in agreement with previous studies [17, 6] suggesting that anisotropic cell migration along white matter fiber tracks is not a major determinant of tumor shape in the setting of reaction-diffusion based tumor growth models and for most locations across the brain.

The macroscopic growth of brain tumors has been studied by means of different computational modeling approaches. Glioblastoma multiforme (GBM) is the most common malignant type and is commonly modeled as a reaction--diffusion type system, accounting for its invasive growth pattern. Purely biomechanical models have been proposed to represent the mass effect caused by the growing tumor, but only a few models consider mass effect and tissue invasion effects in a single 3D model. We report first results of a comparative study that evaluates the ability of a simple computational model to reproduce the shape of pathologies found in patients. GBM invasion into brain tissue and the mechanical interaction between tumor and healthy tissue components are simulated using the finite element method (FEM). Cell proliferation and invasion are modeled as a reaction--diffusion process; simulation of the mechanic interaction relies on a linear elastic material model. Both are coupled by relating the local increase in tumor cell concentration to the generation of isotropic strain in the corresponding tissue element. The model accounts for multiple brain regions with values for proliferation, isotropic diffusion, and mechanical properties derived from literature. Tumors were seeded at multiple locations in FEM models derived from publicly available human brain atlases. Simulation results for a given tumor volume were compared to patient images. Simulated tumors showed a more symmetric growth pattern compared to their real counterparts. Resulting levels of tumor invasiveness were in agreement with simulation parameters and tumor-induced pressures of realistic magnitude were found.

BACKGROUND A large proportion of age-related fecal incontinence is attributed to weakness or degeneration of the muscles composing the anal continence organ. However, the individual role of these muscles and their functional interplay remain poorly understood. METHODS This study employs a novel technique based on the combination of MR imaging and FLIP measurements (MR-FLIP) to obtain anatomical and mechanical information simultaneously. Unlike previous methods used to assess the mechanics of the continence organ, MR-FLIP allows inter-individual comparisons and statistical analysis of the sphincter morpho-mechanical parameters. The anatomy as well as voluntary and involuntary mechanical properties of the anal continence organ were characterized in 20 healthy senior volunteers. RESULTS Results showed that the external anal sphincter (EAS) forms a funnel-like shape with wall thickness increasing by a factor of 2.5 from distal (6 ± 0 mm) to proximal (15 ± 3 mm). Both voluntary and involuntary mechanical properties in this region correlate strongly with the thickness of the muscle. The positions of least compliance and maximal orifice closing were both located toward the proximal EAS end. In addition, maximal contraction during squeeze maneuvers was reached after 2 s, but high muscle fatigue was measured during a 7 s holding phase, corresponding to about 60% loss of the energy produced by the muscles during the contraction phase. CONCLUSIONS This work reports baseline parameters describing the morpho-mechanical condition of the sphincter muscle of healthy elderly volunteers. New parameters were also proposed to quantify the active properties of the muscles based on the mechanical energy associated with muscle contraction and fatigue. This information could be used to assess patients suffering from AI or for the design of novel implants.

INTRODUCTION Continence results from a complex interplay between anal canal (AC) muscles and sensory-motor feedback mechanisms. The AC's passive ability to withstand opening pressure - its compliance - has recently been shown to correlate with continence. Functional lumen imaging probe (FLIP) is used to assess AC compliance, although it provides no anatomical information. Therefore, compliance assessment of specific anatomical structures has not been possible, and the anatomical position of critical functional zones remains unknown. In addition, FLIP assumes a circular orifice cross-section, which has not been shown for the AC. To address those shortcomings, a technique combining FLIP with a medical imaging modality is needed. METHOD We implemented a new research method (MR-FLIP) that combines FLIP with MR-imaging. Twenty healthy volunteers underwent MR-FLIP and conventional FLIP assessment. MR-FLIP was validated by comparison with FLIP results. Anatomical markers were identified, and the cross-sectional shape of the orifice was investigated. RESULTS MR-FLIP provides compliance measurements identical to those obtained by conventional FLIP. Anatomical analysis revealed that the least compliant AC zone was located at the proximal end of the external anal sphincter. The AC cross-sectional shape was found to deviate only slightly from circularity in healthy volunteers. CONCLUSION The proposed method was equivalent to classical FLIP. It establishes for the first time a direct mapping between local tissue compliance and anatomical structure, which is key for gaining novel insights into (in)continence. In addition, MR-FLIP provides a tool for better understanding conventional FLIP measurements in the AC by quantifying its limitations and assumptions. This article is protected by copyright. All rights reserved.

The CHIC (Computational Horizons in Cancer) project develops computational models for the cancer domain, as well as a secure infrastructure for data and model access, and reuse. It addresses challenges related to the development, validation and maintenance of multi-scale models by proposing the creation of complex disease models as composition of reusable component models. We present a versatile component model for the simulation of bio-mechanical aspects of macroscopic tumor growth. The model computes mechanical stresses and strains, resulting from tumor growth or shrinkage in a patient-specific anatomy, from a map of cancer cell concentration. In iterative coupled execution with other component models, its output can be used, for example, to guide the directionality of tumour expansion, or to simulate the effect of increased pressure on blood perfusion. Simulation of the bio-mechanic interaction relies on the finite element method (FEM); it is based on a hyper-elastic material model, as well as organ-specific boundary conditions and material properties. A pre-processing pipeline has been developed to automate the configuration process. In combination with automatic segmentation tools, this pipeline permits rapid generation of patient-specific FEM models for personalized simulations, including the assignment of suitable material parameters and boundary conditions from simple configuration options. Model and pre-processing pipeline are implemented using Open Source libraries and software packages (CGAL, VTK, FEBio). The model can be parametrised easily for different organs and body sites of interests; it has been applied to the simulation of kidney, lung and brain cancers in the context of CHIC.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.