A multiscale cell-based model of tumor growth for chemotherapy assessment and tumor-targeted therapy through a 3D computational approach

Multiscale computational modeling of aortic valve calcification

Calcific aortic valve disease (CAVD) is a common cardiovascular disease that affects millions of people worldwide. The disease is characterized by the formation of calcium nodules on the aortic valve leaflets, which can lead to stenosis and heart failure if left untreated. The pathogenesis of CAVD is still not well understood, but involves several signaling pathways, including the transforming growth factor beta (TGF β ) pathway. In this study, we developed a multiscale computational model for TGF β -stimulated CAVD. The model framework comprises cellular behavior dynamics, subcellular signaling pathways, and tissue-level diffusion fields of pertinent chemical species, where information is shared among different scales. Processes such as endothelial to mesenchymal transition (EndMT), fibrosis, and calcification are incorporated. The results indicate that the majority of myofibroblasts and osteoblast-like cells ultimately die due to lack of nutrients as they become trapped in areas with higher levels of fibrosis or calcification, and they subsequently act as sources for calcium nodules, which contribute to a polydispersed nodule size distribution. Additionally, fibrosis and calcification processes occur more frequently in regions closer to the endothelial layer where the cell activity is higher. Our results provide insights into the mechanisms of CAVD and TGF β signaling and could aid in the development of novel therapeutic approaches for CAVD and other related diseases such as cancer. More broadly, this type of modeling framework can pave the way for unraveling the complexity of biological systems by incorporating several signaling pathways in subcellular models to simulate tissue remodeling in diseases involving cellular mechanobiology.

Mathematical Modeling Support for Lung Cancer Therapy-A Short Review

The paper presents a review of models that can be used to describe dynamics of lung cancer growth and its response to treatment at both cell population and intracellular processes levels. To address the latter, models of signaling pathways associated with cellular responses to treatment are overviewed. First, treatment options for lung cancer are discussed, and main signaling pathways and regulatory networks are briefly reviewed. Then, approaches used to model specific therapies are discussed. Following that, models of intracellular processes that are crucial in responses to therapies are presented. The paper is concluded with a discussion of the applicability of the presented approaches in the context of lung cancer.

Development and CT image-domain validation of a computational lung lesion model for use in virtual imaging trials

PURPOSE

Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly-customizable lesion detectability studies on clinical computed tomography (CT) data, the ground-truth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomically-informed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically-informed model for use with simulated CT imaging studies.

METHODS

The lesion model was simulated initially from a seed cell with 10 μm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete time-intervals (corresponding to synchronous cell cycles) up to a size of ∼200 μm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time-step given sufficient space. At lesion sizes beyond 200 μm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 μm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly-varying material properties based on their cellular level health in each region, resulting in a multi-material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images.

RESULTS

The useable outputs of this work were voxel- or surface-based, validated, computational lesions, at a scale clearly visible on clinical CT (3-4 cm). Analysis of the observer study results indicated that the computationally-generated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non-inferior to an earlier image-based lesion model-indicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs.

CONCLUSIONS

Results indicated the non-inferiority of this model as compared to previous methods, indicating the utility of the model for use in both hybrid CT images and in simulated CT imaging.