Modelling Intratumoral Hypoxia from ADC to Personalize Tumour Control Probability in Head-and-Neck Cancer
Please login to view abstract download link
Tumour hypoxia is a major cause of radioresistance in head-and-neck cancer, but it is rarely accounted for explicitly in clinical tumour control probability models because patient-specific hypoxia information is difficult to obtain. In this contribution, diffusion MRI–derived apparent diffusion coefficient (ADC) maps are used as a practical surrogate to identify hypoxia-enriched tumour subvolumes and to integrate intratumoural biological heterogeneity directly into personalised TCP modelling. By representing the tumour as coexisting normoxic and hypoxic compartments with different dose–response characteristics, ADC-defined hypoxia is incorporated into a mechanistic TCP framework. This approach reveals large inter-patient differences in predicted tumour control for the same prescribed dose, driven by variations in hypoxic burden rather than tumour size alone, and captures effects that are missed by conventional volume-based models. Overall, this work illustrates how functional imaging can be combined with computational modelling to enable biologically informed TCP estimates, providing a quantitative basis for hypoxia-aware dose escalation and subvolume boosting strategies in head-and-neck radiotherapy.
