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Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes’ Theorem to reduce costs and improve outcomes

Author

Listed:
  • Arthur J. Swersey

    (Yale School of Management)

  • John Colberg

    (Yale Medical School)

  • Ronald Evans

    (Prognos)

  • Michael W. Kattan

    (Cleveland Clinic)

  • Johannes Ledolter

    (University of Iowa)

  • Rodney Parker

    (Indiana University)

Abstract

Prostate cancer is the second leading cause of death from cancer, behind lung cancer, for men in the U. S, with nearly 30,000 deaths per year. A key problem is the difficulty in distinguishing, after biopsy, between significant cancers that should be treated immediately and clinically insignificant tumors that should be monitored by active surveillance. Prostate cancer has been over-treated; a recent European randomized screening trial shows overtreatment rates of 40%. Overtreatment of insignificant tumors reduces quality of life, while delayed treatment of significant cancers increases the incidence of metastatic disease and death. We develop a decision analysis approach based on simulation and probability modeling. For a given prostate volume and number of biopsy needles, our rule is to treat if total length of cancer in needle cores exceeds c, the cutoff value, with active surveillance otherwise, provided pathology is favorable. We determine the optimal cutoff value, c*. There are two misclassification costs: treating a minimal tumor and not treating a small or medium tumor (large tumors were never misclassified in our simulations). Bayes’ Theorem is used to predict the probabilities of minimal, small, medium, and large cancers given the total length of cancer found in biopsy cores. A 20 needle biopsy in conjunction with our new decision analysis approach significantly reduces the expected loss associated with a patient in our target population about to undergo a biopsy. Longer needles reduce expected loss. Increasing the number of biopsy cores from the current norm of 10–12 to about 20, in conjunction with our new decision model, should substantially improve the ability to distinguish minimal from significant prostate cancer by minimizing the expected loss from over-treating minimal tumors and delaying treatment of significant cancers.

Suggested Citation

  • Arthur J. Swersey & John Colberg & Ronald Evans & Michael W. Kattan & Johannes Ledolter & Rodney Parker, 2020. "Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes’ Theorem to reduce costs and improve outcomes," Health Care Management Science, Springer, vol. 23(1), pages 102-116, March.
    • Handle: RePEc:kap:hcarem:v:23:y:2020:i:1:d:10.1007_s10729-019-09480-6
    DOI: 10.1007/s10729-019-09480-6
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    References listed on IDEAS

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    1. Dimitris Bertsimas & John Silberholz & Thomas Trikalinos, 2018. "Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening," Health Care Management Science, Springer, vol. 21(1), pages 105-118, March.
    2. Daniel Underwood & Jingyu Zhang & Brian Denton & Nilay Shah & Brant Inman, 2012. "Simulation optimization of PSA-threshold based prostate cancer screening policies," Health Care Management Science, Springer, vol. 15(4), pages 293-309, December.
    3. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of PSA Screening Policies," Medical Decision Making, , vol. 32(2), pages 337-349, March.
    Full references (including those not matched with items on IDEAS)

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