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

Ionizing radiation is an effective localized therapeutic strategy that is used to treat approximately 50% of all cancer patients. However, off-target effects in the radiation field vicinity may induce toxicities that exacerbate patient symptoms, leading to diminished quality of life. In patients receiving radiotherapy (RT)--the standard of care for head and neck cancer (HNC)--a major toxicity of particular concern in feeding tube dependence. This is often due to the proximity of the tumor to vital organs such as the esophagus and larynx. Treatment in these areas can cause adverse events, such as difficulty swallowing and dry mouth, increasing the need for a feeding tube. Such symptoms can be measured using patient-reported outcomes (PROs), which provide a measure of a patients health, symptoms, and overall wellbeing, as perceived and reported by the patients themselves. To capture both on-target tumor response and off-target toxicities of RT in HNC, we developed a mathematical model to inform acceptable radiation doses that maximize tumor response while minimizing off-target toxicities. The classical linear-quadratic dose-response model was employed to describe tumor response to RT with exponential growth. To model off-target toxicities, we introduce a novel concept of radiation exposure analogous to drug exposure. We then employed an inhomogeneous continuous-time Markov chain model with radiation exposure as a time-varying covariate to describe time-to-feeding tube dependence. We then define two thresholds to be selected by the clinician in consultation with the patient to derive minimum efficacious dose (MED) and maximum tolerable dose (MTD). We observe that RT is either viable (MED[≤]MTD) or unviable (MED>MTD). To enhance the models applicability, we simulated administration of radiosensitizing agents and provision of symptom management therapy by altering model parameters. Overall, this model offers adaptable, data-informed treatment decisions by integrating both tumor control and quality of life considerations into a singular model.