Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis

Authors:
Oguzhan Alagoz;Jagpreet Chhatwal;Elizabeth S. Burnside
Affiliations:
Department of Industrial and Systems Engineering, University of Wisconsin--Madison, Madison, Wisconsin 53705;Department of Health Policy and Management and Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261;Department of Radiology, University of Wisconsin--Madison, Madison, Wisconsin 53792
Venue:
Decision Analysis
Year:
2013

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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 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.