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Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy
Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can es...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651664/ https://www.ncbi.nlm.nih.gov/pubmed/34876609 http://dx.doi.org/10.1038/s41598-021-02910-y |
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author | Niraula, Dipesh Jamaluddin, Jamalina Matuszak, Martha M. Haken, Randall K. Ten Naqa, Issam El |
author_facet | Niraula, Dipesh Jamaluddin, Jamalina Matuszak, Martha M. Haken, Randall K. Ten Naqa, Issam El |
author_sort | Niraula, Dipesh |
collection | PubMed |
description | Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT. |
format | Online Article Text |
id | pubmed-8651664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86516642021-12-08 Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy Niraula, Dipesh Jamaluddin, Jamalina Matuszak, Martha M. Haken, Randall K. Ten Naqa, Issam El Sci Rep Article Subtle differences in a patient’s genetics and physiology may alter radiotherapy (RT) treatment responses, motivating the need for a more personalized treatment plan. Accordingly, we have developed a novel quantum deep reinforcement learning (qDRL) framework for clinical decision support that can estimate an individual patient’s dose response mid-treatment and recommend an optimal dose adjustment. Our framework considers patients’ specific information including biological, physical, genetic, clinical, and dosimetric factors. Recognizing that physicians must make decisions amidst uncertainty in RT treatment outcomes, we employed indeterministic quantum states to represent human decision making in a real-life scenario. We paired quantum decision states with a model-based deep q-learning algorithm to optimize the clinical decision-making process in RT. We trained our proposed qDRL framework on an institutional dataset of 67 stage III non-small cell lung cancer (NSCLC) patients treated on prospective adaptive protocols and independently validated our framework in an external multi-institutional dataset of 174 NSCLC patients. For a comprehensive evaluation, we compared three frameworks: DRL, qDRL trained in a Qiskit quantum computing simulator, and qDRL trained in an IBM quantum computer. Two metrics were considered to evaluate our framework: (1) similarity score, defined as the root mean square error between retrospective clinical decisions and the AI recommendations, and (2) self-evaluation scheme that compares retrospective clinical decisions and AI recommendations based on the improvement in the observed clinical outcomes. Our analysis shows that our framework, which takes into consideration individual patient dose response in its decision-making, can potentially improve clinical RT decision-making by at least about 10% compared to unaided clinical practice. Further validation of our novel quantitative approach in a prospective study will provide a necessary framework for improving the standard of care in personalized RT. Nature Publishing Group UK 2021-12-07 /pmc/articles/PMC8651664/ /pubmed/34876609 http://dx.doi.org/10.1038/s41598-021-02910-y Text en © The Author(s) 2021, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Niraula, Dipesh Jamaluddin, Jamalina Matuszak, Martha M. Haken, Randall K. Ten Naqa, Issam El Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title | Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title_full | Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title_fullStr | Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title_full_unstemmed | Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title_short | Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
title_sort | quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651664/ https://www.ncbi.nlm.nih.gov/pubmed/34876609 http://dx.doi.org/10.1038/s41598-021-02910-y |
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