Speaker
Description
Radiotherapy (RT) is an effective localized therapy used to treat ~75% of head and neck cancer (HNC) patients. However, delivery to surrounding normal tissues induce toxicities that exacerbate patient symptoms. Motivated by a published dataset of longitudinal patient reported outcomes (PROs) in HNC patients treated with RT, we developed a mathematical model to capture both on-target tumor response and off-target toxicities. The classical linear-quadratic model was employed to describe tumor response to RT. To model off-target toxicities, we introduce a novel concept of radiation exposure analogous to drug exposure. We then employed a Markov chain model with radiation exposure as a time-varying covariate to describe PRO dynamics. While efficacy-toxicity trade-offs were sensitive to RT dose and use concurrent chemotherapy, toxicity-toxicity trade-offs were more sensitive to RT plan (sparing vs. non-sparing). We also derived minimum efficacious dose (MED) and maximum tolerable dose (MTD) for various tumor response and toxicity endpoints. Overall, this model offers adaptable, data-informed treatment decisions by integrating both tumor control and quality of life considerations. Future iterations of the model could aid clinicians in RT dose-finding and selecting a RT plan that will optimize tumor control and patients’ goals of care.
