Patient-specific microvascular computational modeling for estimating radiotherapy outcomes.

Area of Science:

• Computational biology
• Medical physics
• Oncology

Background:

• Head-and-neck cancer treatment efficacy is influenced by the tumor microenvironment's vascular characteristics.
• Accurate modeling of microvasculature is crucial for predicting radiotherapy response.

Purpose of the Study:

• To develop and validate a personalized computational framework for modeling patient-specific head-and-neck cancer vascular microenvironments.
• To evaluate the impact of microvascular features on radiotherapy outcomes, including oxygen delivery and tumor control.

Main Methods:

• Population-based calibration of a microvascular model using sublingual microscopy data from 62 patients.
• Personalization of digital microvascular networks for nine individual patients.
• Integration of personalized models into a 3D virtual microenvironment for radiotherapy simulations.

Main Results:

• Calibrated models accurately reproduced physiological parameters like red blood cell velocity.
• Personalized models replicated individual vascular beds' structural and functional characteristics.
• Higher vascularization correlated with enhanced oxygenation, reduced hypoxia, and improved tumor control probability.

Conclusions:

• A personalized computational framework can create microvascular digital twins from patient sublingual microscopy data.
• Microvascular network properties significantly influence radiotherapy outcomes in head-and-neck cancer.
• This approach enhances understanding of the interplay between vascularization, oxygenation, and treatment effectiveness.