A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data.
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 progressio...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315661 |
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| author | Benoîte de Saporta Aymar Thierry d'Argenlieu Régis Sabbadin Alice Cleynen |
| author_facet | Benoîte de Saporta Aymar Thierry d'Argenlieu Régis Sabbadin Alice Cleynen |
| author_sort | Benoîte de Saporta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-94a296a1d9c44cc6b304edee81562796 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-94a296a1d9c44cc6b304edee815627962025-01-08T05:32:59ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031566110.1371/journal.pone.0315661A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data.Benoîte de SaportaAymar Thierry d'ArgenlieuRégis SabbadinAlice CleynenDesigning 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.https://doi.org/10.1371/journal.pone.0315661 |
| spellingShingle | Benoîte de Saporta Aymar Thierry d'Argenlieu Régis Sabbadin Alice Cleynen A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. PLoS ONE |
| title | A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. |
| title_full | A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. |
| title_fullStr | A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. |
| title_full_unstemmed | A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. |
| title_short | A Monte-Carlo planning strategy for medical follow-up optimization: Illustration on multiple myeloma data. |
| title_sort | monte carlo planning strategy for medical follow up optimization illustration on multiple myeloma data |
| url | https://doi.org/10.1371/journal.pone.0315661 |
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