OR Forum—A POMDP Approach to Personalize Mammography Screening Decisions

Abstract

Breast cancer is the most common nonskin cancer and the second leading cause of cancer death in U.S. women. Although mammography is the most effective modality for breast cancer screening, it has several potential risks, including high false-positive rates. Therefore, the balance of benefits and risks, which depend on personal characteristics, is critical in designing a mammography screening schedule. In contrast to prior research and existing guidelines that consider population-based screening recommendations, we propose a personalized mammography screening policy based on the prior screening history and personal risk characteristics of women. We formulate a finite-horizon, partially observable Markov decision process (POMDP) model for this problem. Our POMDP model incorporates two methods of detection (self or screen), age-specific unobservable disease progression, and age-specific mammography test characteristics. We solve this POMDP optimally after setting transition probabilities to values estimated from a validated microsimulation model. Additional published data is used to specify other model inputs such as sensitivity and specificity of test results. Our results show that our proposed personalized screening schedules outperform the existing guidelines with respect to the total expected quality-adjusted life years, while significantly decreasing the number of mammograms and false-positives. We also report the lifetime risk of developing undetected invasive cancer associated with each screening scenario.