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 with logistic growth. As a surrogate for normal tissue damage, we modeled absorbed dose kinetics to individual organs at risk (OARs) using a one-compartment pharmacokinetic model with linear elimination. We then employed a Markov chain model (with tumor size and absorbed dose as time-varying covariates) to describe PRO dynamics. Response-toxicity trade-offs were sensitive to dose, variably sensitive to OAR sparing (dependent on OAR-symptom associations), and sensitive to fractionation. Toxicity-toxicity trade-offs were insensitive to dose, sensitive to OAR sparing, and insensitive to fractionation. By integrating both tumor control and quality of life considerations into a singular model, recommendations of dose, OAR sparing, and fractionation can be made. Future iterations of the model could aid clinicians in RT dose-finding and selecting a RT plan that will optimize tumor control and patients’ quality of life.
