Abstract
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 discretetime 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.
Original language | English (US) |
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Pages (from-to) | 200-224 |
Number of pages | 25 |
Journal | Decision Analysis |
Volume | 10 |
Issue number | 3 |
DOIs | |
State | Published - Sep 2013 |
Keywords
- BI-RADS
- Breast cancer diagnosis
- Double control limit policy
- Mammography interpretation
- Markov decision processes
- Medical decision making
- Practice
ASJC Scopus subject areas
- General Decision Sciences