Sweet Ping Ng1,2, Temitayo Ajayi3, Andrew J Schaefer3, Courtney Pollard1, Houda Bahig1,4, Adam S Garden1, David I Rosenthal1, G Brandon Gunn1, Steven J Frank1, Jack Phan1, William H Morrison1, Jason M Johnson5, Abdallah S R Mohamed1, Erich M Sturgis6, Clifton D Fuller1. 1. Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. 2. Department of Radiation Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia. 3. Department of Computational and Applied Mathematics, Rice University, Houston, Texas. 4. Department of Radiation Oncology, Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada. 5. Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas. 6. Department of Head and Neck Surgery, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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.
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.
Authors: Vivek Nair; Samuel Auger; Sara Kochanny; Frederick M Howard; Daniel Ginat; Olga Pasternak-Wise; Aditya Juloori; Matthew Koshy; Evgeny Izumchenko; Nishant Agrawal; Ari Rosenberg; Everett E Vokes; M Reza Skandari; Alexander T Pearson Journal: JAMA Netw Open Date: 2022-04-01