Speaker
Description
RT for HPV+ oropharyngeal cancer has high cure rates, but this is often associated with significant toxicity. Despite broad interest in de-intensifying RT in this context, there isn’t a reliable biomarker to identify individual patients for safe de-escalation without sacrificing cure. We address this by creating a virtual cohort of head and neck cancer. The virtual cohort is based on two mathematical models: (1) a model of RT-response that simulates tumor volume dynamics during RT; (2) a model of tumor regrowth that simulates disease recurrence from post-treatment viable tumor burden dynamics. The virtual cohort’s RT response parameters are calibrated to weekly CT images from a cohort of 39 head and neck cancer patients that received fractionated RT. The recurrence/regrowth parameters are calibrated to 5-year locoregional recurrence data abstracted from a large cooperative group clinical trial that tested both standard and hyperfractionated RT (RTOG 9003). This outputs a calibrated virtual cohort of HNC patients that has the same recurrence patterns as its real counterparts in RTOG 9003, i.e. a digital twin of the trial. We then ran systematic in silico trials on this virtual cohort to determine patient-specific adaptive RT schedules that maximize locoregional control rates and minimizes both total RT dose and number of RT fractions.
