Optimization of active surveillance strategies for heterogeneous patients with prostate cancer

• 成果类型: Article
• 署名作者: Zhang, Zheng; Denton, Brian T.; Morgan, Todd M.
• 署名单位: Zhejiang University; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13800
• 发表日期: 2022
• 页码: 4021-4037
• 关键词: radical prostatectomy; progression; management; disease; LIFE

摘要

Prostate cancer (PCa) is common in American men with long latent periods, during which the disease is asymptomatic. Active surveillance is a monitoring strategy commonly used for patients diagnosed with low-risk PCa who may harbor latent high-risk PCa. The optimal monitoring strategy attempts to minimize the disutility of testing while ensuring that the patient is detected at the earliest time when the disease progresses. Unfortunately, guidelines for the active surveillance of PCa are often one-size-fits-all strategies that ignore the heterogeneity among multiple patient types. In contrast, personalized strategies based on partially observable Markov decision process (POMDP) models are challenging to implement in practice given the large number of possible strategies that can be used. This article presents a two-stage stochastic programming approach that selects a set of strategies for predefined 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 the assignment of multiple patient types to strategies. The objective is to maximize a weighted reward function that considers the need for cancer detection, missed detection, and cost of monitoring patients. We discuss the structure and complexity of the model and reformulate a logic-based Bender's decomposition formulation that can solve realistic instances to optimality. We present a case study for the active surveillance of 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 the existing one-size-fits-all guideline strategies used in practice.