Identifying viable radiation dose ranges to balance competing objectives of tumor response and off-target toxicity

Abstract

11.1 BackgroundRadiotherapy (RT) is a localized therapy used to treat approximately 50% of all cancer patients and 75% of head and neck cancer (HNC) patients. Despite tumor response to RT, off-target effects to surrounding organs at risk (OAR) can result in toxicities that exacerbate patient symptoms and necessitate interventions such as tube feeding. While current models of normal tissue complication probability (NTCP) can account for complex OARs-symptoms interactions, they only allow for dichotomous outcomes without consideration of graded response. Polytomous patient-reported outcomes (PROs), collected via patient surveys, capture patients experience of their symptoms throughout treatment. Unfortunately, such measures are rarely incorporated when modeling treatment-related toxicities. 1.2 PurposeIn this study, we aimed to develop a model of NTCP to recapitulate population-level PRO dynamics from a published dataset and explore response and cross-toxicity trade-offs in HNC. 1.3 MethodsWe employed the classical linear-quadratic dose-response model to describe tumor response to RT with logistic growth. As a surrogate for normal tissue damage, we modeled absorbed dose kinetics to individual OARs (buccal mucosa, oral cavity, superior pharyngeal constrictor muscle (sPCM), and body) using a one-compartment pharmacokinetic model with linear elimination. We then employed a minimal inhomogeneous continuous-time Markov chain model (with tumor size and absorbed dose as time-varying covariates) to describe PRO dynamics (oral pain, dysphagia, weight loss, and tube feeding, measured on the EORTC-HN35 scale). We modeled symptom-specific NTCP as the cumulative incidence of severe symptom. Finally, we explored various response-toxicity and toxicity-toxicity trade-offs with respect to dose, OAR sparing, and fractionation. 1.4 ResultsThe developed mathematical model recapitulated the qualitative features of the motivating published dataset, including 1) a transient reduction of a subset of symptoms for 1-2 weeks; followed by 2) an acute exacerbation of symptoms throughout the rest of RT; followed by 3) a long-term relaxation of symptoms to below baseline levels. 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. 1.5 ConclusionsOverall, this mathematical model addresses some of the limitations of current NTCP models by explaining graded toxicity dynamics. 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.