Cost-effectiveness of personalized policies for implementing organ-at-risk sparing adaptive radiation therapy in head and neck cancer.

Background And Purpose: The principle of adaptive radiation therapy (ART) is to adjust radiation plans in response to anatomical changes during treatment. The purpose of this study was to develop a decision-making model for implementation of personalized ART that balances the costs and clinical benefits of radiation plan adaptations in head and neck cancer (HNC).

Materials And Methods: Using retrospective imaging data from 52 HNC patients, a Markov decision process (MDP) model was developed to determine optimal timing for plan adaptations based on the difference in normal tissue complication probability (ΔNTCP) between planned and delivered doses to organs-at-risk. To capture the trade-off between the costs and benefits of plan adaptations, the end-treatment ΔNTCPs were converted to Quality Adjusted Life Years (QALYs) and then to equivalent monetary values using a willingness-to-pay per QALY parameter.

Results: The optimal policies were derived for 96 combinations of willingness-to-pay per QALY (W) and re-planning cost (RC) and validated using Monte Carlo simulation for two representative scenarios: (1) W = $200,000, RC = $1,000; (2) W = $100,000, RC = $2,000. In scenario (1), the MDP model's policy reduced the probability of excessive toxicity (ΔNTCP ≥ 5 %) to zero (from 0.21 without re-planning) at an average cost of $380 per patient. In scenario (2), it reduced this probability to 0.02 at an average cost of $520 per patient.

Conclusions: The MDP model's policies outperformed the current fixed-time (one-size-fits-all) approaches in both clinical and financial outcomes in the simulations.