Optimization of Active Surveillance Strategies for Heterogeneous Patients with Prostate Cancer

Prostate cancer (PCa) is common in American men with long latent periods during which the disease is asymptomatic. Active surveillance is a monitoring strategy that is commonly used for patients diagnosed with low-risk PCa who may harbor a latent high-risk PCa. The optimal monitoring strategy attempts to minimize the harm of testing while ensuring the patient is detected at the earliest time when the disease progresses. Guidelines for active surveillance of PCa are often one-size-fits-all strategies that ignore the heterogeneity of patients. In contrast, personalized strategies based on partially observable Markov decision process (POMDP) models are challenging to implement in practice because the number of strategies is so large. In contrast, this article presents a two-stage stochastic programming approach that selects a set of strategies of pre-defined cardinality based on patients' disease risks. The first-stage decision variables include binary variables for the selection of periods at which to test patients in each strategy and assignment of patients to strategies. The objective is to maximize a weighted reward function that considers the need for cancer detection, missed detection, and the cost of monitoring patients. We discuss the structure and complexity of the model, and we reformulate a logic-based Bender's decomposition formulation that can solve realistic instances to optimality. We present a case study for active surveillance for PCa and show that our model results in strategies that vary in intensity according to patient disease risk. Finally, we show that our model can generate a small number of strategies that can significantly improve upon the existing "one-size-fits-all'' guideline strategies used in practice.