Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.
Alagoz, Oguzhan Chhatwal, Jagpreet Burnside, Elizabeth S K07 CA114181/CA/NCI NIH HHS/United States R01 CA127379/CA/NCI NIH HHS/United States R01 CA165229/CA/NCI NIH HHS/United States R01 LM010921/LM/NLM NIH HHS/United States R21 CA129393/CA/NCI NIH HHS/United States Decis Anal. 2013 Sep;10(3):200-224.