Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

Area of Science:

• Oncology
• Biomedical Engineering
• Mathematical Biology

Background:

• Tumor vasculature is essential for nutrient supply, waste removal, and tumor growth beyond 2-3 mm³.
• The dynamic nature of tumor vascular networks significantly impacts treatment response (systemic and radiation therapy).
• Accurate prediction of tumor growth and optimal treatment protocols necessitate a thorough understanding of vascular dynamics.

Purpose of the Study:

• To review experimental techniques for visualizing and quantifying tumor vascular dynamics.
• To discuss the integration of experimental data with mathematical models for in silico investigations.
• To highlight the development of data-driven mathematical models for predicting tumor behavior and treatment response.

Main Methods:

• Quantitative and time-resolved imaging methods (e.g., MRI, confocal microscopy) for characterizing tumor vascular properties.
• Microfluidic devices for in vitro modeling of tumor microenvironments.
• Biologically based mathematical modeling for in silico analysis of tumor and vascular dynamics.

Main Results:

• Experimental techniques provide detailed, time-resolved data on tumor vascular properties at tissue and cellular scales.
• Mathematical models, when calibrated with experimental data, can predict future vascular growth and systemic agent delivery.
• Integration enables the generation of testable predictions for radiotherapy response and tumor control.

Conclusions:

• The synergy between experimental imaging and mathematical modeling is vital for advancing our understanding of tumor vascular dynamics.
• Data-driven mathematical models offer a powerful tool for personalized cancer treatment planning.
• This integrated approach facilitates the prediction of tumor growth, drug delivery, and radiotherapy efficacy, leading to optimized therapeutic strategies.