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

Author

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  • Oguzhan Alagoz

    (Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin 53705)

  • Jagpreet Chhatwal

    (Department of Health Policy and Management and Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261)

  • Elizabeth S. Burnside

    (Department of Radiology, University of Wisconsin–Madison, Madison, Wisconsin 53792)

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.

Suggested Citation

  • Oguzhan Alagoz & Jagpreet Chhatwal & Elizabeth S. Burnside, 2013. "Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis," Decision Analysis, INFORMS, vol. 10(3), pages 200-224, September.
    • Handle: RePEc:inm:ordeca:v:10:y:2013:i:3:p:200-224
    DOI: 10.1287/deca.2013.0272
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    References listed on IDEAS

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    7. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
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    Cited by:

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    3. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.
    4. Malek Ebadi & Raha Akhavan-Tabatabaei, 2021. "Personalized Cotesting Policies for Cervical Cancer Screening: A POMDP Approach," Mathematics, MDPI, vol. 9(6), pages 1-20, March.
    5. Mehmet A. Ergun & Ali Hajjar & Oguzhan Alagoz & Murtuza Rampurwala, 2022. "Optimal breast cancer risk reduction policies tailored to personal risk level," Health Care Management Science, Springer, vol. 25(3), pages 363-388, September.
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    7. Rakesh K. Sarin, 2013. "From the Editor —Optimal Betting, Reducing Unnecessary Mammography in Breast Cancer Diagnosis, Product Line Design, and Value of Information," Decision Analysis, INFORMS, vol. 10(3), pages 187-188, September.
    8. Vicki M. Bier & Simon French, 2020. "From the Editors: Decision Analysis Focus and Trends," Decision Analysis, INFORMS, vol. 17(1), pages 1-8, March.
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    10. Sait Tunç & Oguzhan Alagoz & Elizabeth S. Burnside, 2022. "A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2361-2378, May.
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