Abstract
Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.
Highlights
Advances in medical imaging technologies allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions
Using microscale computed tomography (microCT) and a preclinical model of breast cancer, we present a series of computational simulation results that predict solid tumor growth based in part on image-derived model inputs
A multiscale computational model was developed and uses input data from tumor microvascular networks segmented from contrast-enhanced microCT image to simulate several phenomena associated with breast cancer growth
Summary
Advances in medical imaging technologies allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. Each agent is fully realized within the model and interactions are governed in part by machine learning methods This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. To help simulate complicated spatiotemporal and multiscale biological behavior as in the case of cancer growth, hybrid modeling approaches have emerged These hybrid models exploit a combination of continuous and discrete models for each scale[7], while considering three key phases of cancer development, namely, avascular growth, tumor-associated angiogenesis, and vascular growth. If using ultra high-resolution microscale CT (microCT) imaging and an intravascular contrast agent, blood vessels as small as a few micrometers can be resolved in small animal models of c ancer[18] These and other technologies represent an assortment of noninvasive methods for longitudinally imaging microvascular networks in cancerous tissue
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