Predicting head and neck cancer response to radiotherapy using mathematical modeling of MRI-based habitats

Abstract Accurately predicting hypoxia may enable personalized radiotherapy to improve outcomes through biologically guided dose modulation. To predict hypoxia status, we integrate advanced MRI methods—oxygen-enhanced MRI (OE-MRI) for hypoxia, dynamic contrast-enhanced MRI (DCE-MRI) for perfusion and cellularity—with a mathematical model of radiation response. Data were collected before and during radiotherapy for 20 patients with HPV-associated oropharyngeal cancer. MRI data were analyzed to derive parameters describing hypoxia, perfusion, and cellularity, clustering each tumor into four habitats at each time point. The model was calibrated using n-fold cross-validation to determine optimal parameters describing response over weeks 2 and 4 of radiotherapy in primary and nodal disease. Prediction accuracy was evaluated on unseen data using Pearson (PCC) and concordance correlation coefficients (CCC). Predictions for perfused hypoxic primary and nodal tumors showed strong correlation (PCC ranging from 0.74 to 0.77) and agreement (CCC ranging from 0.68 to 0.70). Using MRI-based habitats, the model accurately forecasts patient-specific tumor response, potentially supporting personalized radiotherapy in head and neck cancer.