Surveillance imaging for patients with head and neck cancer treated with definitive radiotherapy: A partially observed Markov decision process model

Abstract

BACKGROUND: A possible surveillance model for patients with head and neck cancer (HNC) who received definitive radiotherapy was created using a partially observed Markov decision process. The goal of this model is to guide surveillance imaging policies after definitive radiotherapy. METHODS: The partially observed Markov decision process model was formulated to determine the optimal times to scan patients. Transition probabilities were computed using a data set of 1508 patients with HNC who received definitive radiotherapy between the years 2000 and 2010. Kernel density estimation was used to smooth the sample distributions. The reward function was derived using cost estimates from the literature. Additional model parameters were estimated using either data from the literature or clinical expertise. RESULTS: When considering all forms of relapse, the model showed that the optimal time between scans was longer than the time intervals used in the institutional guidelines. The optimal policy dictates that there should be less time between surveillance scans immediately after treatment compared with years after treatment. Comparable results also held when only locoregional relapses were considered as relapse events in the model. Simulation results for the inclusive relapse cases showed that <15% of patients experienced a relapse over a simulated 36-month surveillance program. CONCLUSIONS: This model suggests that less frequent surveillance scan policies can maintain adequate information on relapse status for patients with HNC treated with radiotherapy. This model could potentially translate into a more cost-effective surveillance program for this group of patients.