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

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

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Article ID: iaor20134975
Volume: 10
Issue: 3
Start Page Number: 200
End Page Number: 224
Publication Date: Sep 2013
Journal: Decision Analysis
Authors: , ,
Keywords: programming: markov decision
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.

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