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A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data

Author

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  • Benoîte de Saporta
  • Aymar Thierry d’Argenlieu
  • Régis Sabbadin
  • Alice Cleynen

Abstract

Designing patient-specific follow-up strategies is key to personalized cancer care. Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. Instead of discretizing the problem with a discrete Markov Decision Process, we apply the Partially-Observed Monte-Carlo Planning algorithm to solve the continuous-time, continuous-state problem, leveraging the near-deterministic nature of cancer progression. Our approach, tested on multiple myeloma patient data, outperforms exact solutions of the discrete model and allows greater flexibility in cost function modeling, enabling patient-specific follow-ups. This method can also be adapted to other diseases.

Suggested Citation

  • Benoîte de Saporta & Aymar Thierry d’Argenlieu & Régis Sabbadin & Alice Cleynen, 2024. "A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-23, December.
    • Handle: RePEc:plo:pone00:0315661
    DOI: 10.1371/journal.pone.0315661
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    References listed on IDEAS

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    1. Benjamin Wölfl & Hedy te Rietmole & Monica Salvioli & Artem Kaznatcheev & Frank Thuijsman & Joel S. Brown & Boudewijn Burgering & Kateřina Staňková, 2022. "The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer," Dynamic Games and Applications, Springer, vol. 12(2), pages 313-342, June.
    2. repec:plo:pmed00:0050170 is not listed on IDEAS
    3. Jingsong Zhang & Jessica J. Cunningham & Joel S. Brown & Robert A. Gatenby, 2017. "Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer," Nature Communications, Nature, vol. 8(1), pages 1-9, December.
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