OR forum-A POMDP approach to personalize mammography screening decisions

Turgay Ayer*, Oguzhan Alagoz, Natasha K. Stout

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


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 falsepositive 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.

Original languageEnglish (US)
Pages (from-to)1019-1034
Number of pages16
JournalOperations Research
Issue number5
StatePublished - Sep 2012
Externally publishedYes


  • Breast cancer
  • Decision analysis
  • Dynamic programming
  • Mammography screening
  • Medical decision making
  • Partially observable Markov decision processes
  • Personalized screening

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

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