Optimal policies for reducing unnecessary follow-up mammography exams in breast cancer diagnosis

Oguzhan Alagoz, Jagpreet Chhatwal, Elizabeth S. Burnside

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

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 languageEnglish (US)
Pages (from-to)200-224
Number of pages25
JournalDecision Analysis
Volume10
Issue number3
DOIs
StatePublished - Sep 2013
Externally publishedYes

Keywords

  • BI-RADS
  • Breast cancer diagnosis
  • Double control limit policy
  • Mammography interpretation
  • Markov decision processes
  • Medical decision making
  • Practice

ASJC Scopus subject areas

  • Decision Sciences(all)

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