Purpose: Breast cancer is the most common non-skin cancer and the second leading cause of cancer-deaths in US women. Although mammography is cost-effective for breast cancer detection, questions remain about whom to screen and how frequently. The possible screening policies are innumerous and difficult to directly assess. The purpose of this study is to investigate the optimal personalized mammography-screening policy for a woman's lifetime using mathematical modeling.
Methods: We formulate a finite-horizon Partially Observable Markov Decision Process (POMDP) model that maximizes a woman's quality-adjusted life-years (QALYs). A POMDP is a generalization of a Markov decision process that allows sequential decision making when the information regarding the true state of the system is incomplete. Model inputs include state transition probabilities and rewards, estimated using the University of Wisconsin Breast Cancer Simulation. Our POMDP model incorporates unobservable disease progression, two methods of detection (self or screen), and mammography test characteristics. We solve this POMDP optimally to find the optimal personalized screening policy.
Results: A woman's optimal screening policy follows a threshold structure where the optimal decision is to screen if the current breast cancer risk is greater than a certain threshold risk, and wait for 6-months, otherwise. The threshold risk for screening increases with age (Figure 1). For example, the optimal policy for a 40-year old woman is to screen, if her current risk of in-situ or invasive breast cancer is above 0.4%; and wait for another six months, otherwise. For an 82-year old woman, the screening thresholds increase to 11.6% and 5.1% for in-situ and invasive cancer risks, respectively. Our results also suggest younger women should be more aggressively screened than older women. In terms of QALYs, a 40-year old woman with a 10% in-situ cancer risk would attain 40.2 QALYs following our optimal screening policy, a gain of 0.5 QALYs over the QALYs achievable from US screening guidelines. These gains increase with the woman's risk. For example, a 40-year old woman with a 30% in-situ cancer risk would gain over 1 QALY to attain 39.6 QALYs if she followed our optimal screening policy compared with US guidelines.
Conclusion: Unlike many prior studies, our POMDP model provides optimal screening policies for individual patients and has the potential to improve women's health.
Figure 1. Optimal mammography-screening policy.
Candidate for the Lee B. Lusted Student Prize Competition