IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v58y2010i6p1577-1591.html
   My bibliography  Save this article

Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors

Author

Listed:
  • Jagpreet Chhatwal

    (Health Economic Statistics, Merck Research Laboratories, North Wales, Pennsylvania 19454)

  • Oguzhan Alagoz

    (Department of Industrial and Systems Engineering, University of Wisconsin--Madison, Madison, Wisconsin 53706)

  • Elizabeth S. Burnside

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

Abstract

Breast cancer is the most common non-skin cancer affecting women in the United States, where every year more than 20 million mammograms are performed. Breast biopsy is commonly performed on the suspicious findings on mammograms to confirm the presence of cancer. Currently, 700,000 biopsies are performed annually in the U.S.; 55%--85% of these biopsies ultimately are found to be benign breast lesions, resulting in unnecessary treatments, patient anxiety, and expenditures. This paper addresses the decision problem faced by radiologists: When should a woman be sent for biopsy based on her mammographic features and demographic factors? This problem is formulated as a finite-horizon discrete-time Markov decision process. The optimal policy of our model shows that the decision to biopsy should take the age of patient into account; particularly, an older patient's risk threshold for biopsy should be higher than that of a younger patient. When applied to the clinical data, our model outperforms radiologists in the biopsy decision-making problem. This study also derives structural properties of the model, including sufficiency conditions that ensure the existence of a control-limit type policy and nondecreasing control-limits with age.

Suggested Citation

  • Jagpreet Chhatwal & Oguzhan Alagoz & Elizabeth S. Burnside, 2010. "Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors," Operations Research, INFORMS, vol. 58(6), pages 1577-1591, December.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:6:p:1577-1591
    DOI: 10.1287/opre.1100.0877
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1100.0877
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.1100.0877?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lisa M. Maillart & Julie Simmons Ivy & Scott Ransom & Kathleen Diehl, 2008. "Assessing Dynamic Breast Cancer Screening Policies," Operations Research, INFORMS, vol. 56(6), pages 1411-1427, December.
    2. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2004. "The Optimal Timing of Living-Donor Liver Transplantation," Management Science, INFORMS, vol. 50(10), pages 1420-1430, October.
    3. Joseph S. Pliskin & Donald S. Shepard & Milton C. Weinstein, 1980. "Utility Functions for Life Years and Health Status," Operations Research, INFORMS, vol. 28(1), pages 206-224, February.
    4. Frank A. Sonnenberg & J. Robert Beck, 1993. "Markov Models in Medical Decision Making," Medical Decision Making, , vol. 13(4), pages 322-338, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hui Zhang & Christian Wernz & Danny R. Hughes, 2018. "A Stochastic Game Analysis of Incentives and Behavioral Barriers in Chronic Disease Management," Service Science, INFORMS, vol. 10(3), pages 302-319, September.
    2. 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.
    3. M. Reza Skandari & Steven M. Shechter & Nadia Zalunardo, 2015. "Optimal Vascular Access Choice for Patients on Hemodialysis," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 608-619, October.
    4. Hessam Bavafa & Sergei Savin & Christian Terwiesch, 2021. "Customizing Primary Care Delivery Using E‐Visits," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 4306-4327, November.
    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.
    6. Turgay Ayer, 2015. "Inverse optimization for assessing emerging technologies in breast cancer screening," Annals of Operations Research, Springer, vol. 230(1), pages 57-85, July.
    7. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    8. Zlatana Nenova & Jennifer Shang, 2022. "Personalized Chronic Disease Follow‐Up Appointments: Risk‐Stratified Care Through Big Data," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 583-606, February.
    9. Boloori, Alireza & Saghafian, Soroush & Chakkera, Harini A. A. & Cook, Curtiss B., 2017. "Data-Driven Management of Post-transplant Medications: An APOMDP Approach," Working Paper Series rwp17-036, Harvard University, John F. Kennedy School of Government.
    10. Alireza Sabouri & Woonghee Tim Huh & Steven M. Shechter, 2017. "Screening Strategies for Patients on the Kidney Transplant Waiting List," Operations Research, INFORMS, vol. 65(5), pages 1131-1146, October.
    11. Ting-Yu Ho & Shan Liu & Zelda B. Zabinsky, 2019. "A Multi-Fidelity Rollout Algorithm for Dynamic Resource Allocation in Population Disease Management," Health Care Management Science, Springer, vol. 22(4), pages 727-755, December.
    12. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    13. Sarang Deo & Seyed Iravani & Tingting Jiang & Karen Smilowitz & Stephen Samuelson, 2013. "Improving Health Outcomes Through Better Capacity Allocation in a Community-Based Chronic Care Model," Operations Research, INFORMS, vol. 61(6), pages 1277-1294, December.
    14. Mabel C. Chou & Mahmut Parlar & Yun Zhou, 2017. "Optimal Timing to Initiate Medical Treatment for a Disease Evolving as a Semi-Markov Process," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 194-217, October.
    15. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    16. Ali Hajjar & Oguzhan Alagoz, 2023. "Personalized Disease Screening Decisions Considering a Chronic Condition," Management Science, INFORMS, vol. 69(1), pages 260-282, January.
    17. Kotas, Jakob & Ghate, Archis, 2018. "Bayesian learning of dose–response parameters from a cohort under response-guided dosing," European Journal of Operational Research, Elsevier, vol. 265(1), pages 328-343.
    18. Muge Capan & Julie S. Ivy & James R. Wilson & Jeanne M. Huddleston, 2017. "A stochastic model of acute-care decisions based on patient and provider heterogeneity," Health Care Management Science, Springer, vol. 20(2), pages 187-206, June.
    19. F. Reed Johnson & John J. Sheehan & Semra Ozdemir & Matthew Wallace & Jui-Chen Yang, 2025. "How Much Better is Faster? Empirical Tests of QALY Assumptions in Health-Outcome Sequences," PharmacoEconomics, Springer, vol. 43(1), pages 45-52, January.
    20. Bengt Liljas, 2011. "Welfare, QALYs, and costs – a comment," Health Economics, John Wiley & Sons, Ltd., vol. 20(1), pages 68-72, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:oropre:v:58:y:2010:i:6:p:1577-1591. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.