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Optimizing active surveillance for prostate cancer using partially observable Markov decision processes

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  • Li, Weiyu
  • Denton, Brian T.
  • Morgan, Todd M.

Abstract

We describe a finite-horizon partially observable Markov decision process (POMDP) approach to optimize decisions about whether and when to perform biopsies for patients on active surveillance for prostate cancer. The objective is to minimize a weighted combination of two criteria, the number of biopsies to conduct over a patient’s lifetime and the delay in detecting high-risk cancer that warrants more aggressive treatment. Our study also considers the impact of parameter ambiguity caused by variation across models fitted to different clinical studies and variation in the weights attributed to the reward criteria according to patients’ preferences. We introduce two fast approximation algorithms for the proposed model and describe some properties of the optimal policy, including the existence of a control-limit type policy. The numerical results show that our approximations perform well, and we use them to compare the model-based biopsy policies to published guidelines. Although our focus is on prostate cancer active surveillance, there are lessons to be learned for applications to other chronic diseases.

Suggested Citation

  • Li, Weiyu & Denton, Brian T. & Morgan, Todd M., 2023. "Optimizing active surveillance for prostate cancer using partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 305(1), pages 386-399.
    • Handle: RePEc:eee:ejores:v:305:y:2023:i:1:p:386-399
    DOI: 10.1016/j.ejor.2022.05.043
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. William S. Lovejoy, 1987. "Some Monotonicity Results for Partially Observed Markov Decision Processes," Operations Research, INFORMS, vol. 35(5), pages 736-743, October.
    4. Miehling, Erik & Teneketzis, Demosthenis, 2020. "Monotonicity properties for two-action partially observable Markov decision processes on partially ordered spaces," European Journal of Operational Research, Elsevier, vol. 282(3), pages 936-944.
    5. Otten, Maarten & Timmer, Judith & Witteveen, Annemieke, 2020. "Stratified breast cancer follow-up using a continuous state partially observable Markov decision process," European Journal of Operational Research, Elsevier, vol. 281(2), pages 464-474.
    6. S. Christian Albright, 1979. "Structural Results for Partially Observable Markov Decision Processes," Operations Research, INFORMS, vol. 27(5), pages 1041-1053, October.
    7. Fatih Safa Erenay & Oguzhan Alagoz & Adnan Said, 2014. "Optimizing Colonoscopy Screening for Colorectal Cancer Prevention and Surveillance," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 381-400, July.
    8. Richard D. Smallwood & Edward J. Sondik, 1973. "The Optimal Control of Partially Observable Markov Processes over a Finite Horizon," Operations Research, INFORMS, vol. 21(5), pages 1071-1088, October.
    9. Rebecca Yates Coley & Aaron J. Fisher & Mufaddal Mamawala & Herbert Ballentine Carter & Kenneth J. Pienta & Scott L. Zeger, 2017. "A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer," Biometrics, The International Biometric Society, vol. 73(2), pages 625-634, June.
    10. Sheldon M. Ross, 1971. "Quality Control under Markovian Deterioration," Management Science, INFORMS, vol. 17(9), pages 587-596, May.
    11. Burhaneddin Sandıkçı & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2013. "Alleviating the Patient's Price of Privacy Through a Partially Observable Waiting List," Management Science, INFORMS, vol. 59(8), pages 1836-1854, August.
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