Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints
Background and purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients’ toxic...
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Elsevier
2025-01-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S240563162500020X |
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| author | Fatemeh Nosrat Cem Dede Lucas B. McCullum Raul Garcia Abdallah S.R. Mohamed Jacob G. Scott James E. Bates Brigid A. McDonald Kareem A. Wahid Mohamed A. Naser Renjie He Aysenur Karagoz Amy C. Moreno Lisanne V. van Dijk Kristy K. Brock Jolien Heukelom Seyedmohammadhossein Hosseinian Mehdi Hemmati Andrew J. Schaefer Clifton D. Fuller |
| author_facet | Fatemeh Nosrat Cem Dede Lucas B. McCullum Raul Garcia Abdallah S.R. Mohamed Jacob G. Scott James E. Bates Brigid A. McDonald Kareem A. Wahid Mohamed A. Naser Renjie He Aysenur Karagoz Amy C. Moreno Lisanne V. van Dijk Kristy K. Brock Jolien Heukelom Seyedmohammadhossein Hosseinian Mehdi Hemmati Andrew J. Schaefer Clifton D. Fuller |
| author_sort | Fatemeh Nosrat |
| collection | DOAJ |
| description | Background and purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients’ toxicities. The purpose of this study was to determine the personalized optimal timing of re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC). Materials and methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient’s expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated between 2007 and 2013. Kernel density estimation was used to smooth the sample distributions. Optimal re-planning strategies were obtained when the permissible number of re-plans throughout the treatment was limited to 1, 2, and 3, respectively. Results: The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3). Conclusion: In limited-resource settings that impeded high-frequency adaptations, ΔNTCP thresholds obtained from an MDP model could derive optimal timing of re-planning to minimize the likelihood of treatment toxicities. |
| format | Article |
| id | doaj-art-1ea217ed778344c1a5b921bf2c34b299 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-1ea217ed778344c1a5b921bf2c34b2992025-02-09T05:00:37ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-01-0133100715Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraintsFatemeh Nosrat0Cem Dede1Lucas B. McCullum2Raul Garcia3Abdallah S.R. Mohamed4Jacob G. Scott5James E. Bates6Brigid A. McDonald7Kareem A. Wahid8Mohamed A. Naser9Renjie He10Aysenur Karagoz11Amy C. Moreno12Lisanne V. van Dijk13Kristy K. Brock14Jolien Heukelom15Seyedmohammadhossein Hosseinian16Mehdi Hemmati17Andrew J. Schaefer18Clifton D. Fuller19Department of Computational Applied Mathematics and Operations Research, Rice University Houston TX USA; Corresponding authors at: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA (C.D. Fuller).Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USA; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences Houston TX USADepartment of Computational Applied Mathematics and Operations Research, Rice University Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USA; Department of Radiation Oncology, Baylor College of Medicine Houston TX USADepartment of Translational Hematology and Oncology Research, Lerner Research Institute Cleveland OH USADepartment of Radiation Oncology, Emory University Atlanta GA USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Computational Applied Mathematics and Operations Research, Rice University Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USA; Department of Radiation Oncology, University of Groningen University Medical Center Groningen Groningen NetherlandsDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center Houston TX USADepartment of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction Maastricht University Medical Centre+ Maastricht NetherlandsEdward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University Raleigh NC USASchool of Industrial and Systems Engineering, University of Oklahoma Norman OK USADepartment of Computational Applied Mathematics and Operations Research, Rice University Houston TX USA; Corresponding authors at: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA (C.D. Fuller).Department of Computational Applied Mathematics and Operations Research, Rice University Houston TX USA; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center Houston TX USA; Corresponding authors at: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA (C.D. Fuller).Background and purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients’ toxicities. The purpose of this study was to determine the personalized optimal timing of re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC). Materials and methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient’s expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities. The MDP parameters were derived from a dataset comprising 52 HNC patients treated between 2007 and 2013. Kernel density estimation was used to smooth the sample distributions. Optimal re-planning strategies were obtained when the permissible number of re-plans throughout the treatment was limited to 1, 2, and 3, respectively. Results: The MDP (optimal) solution recommended re-planning when the difference between planned and actual NTCPs (ΔNTCP) was greater than or equal to 1%, 2%, 2%, and 4% at treatment fractions 10, 15, 20, and 25, respectively, exhibiting a temporally increasing pattern. The ΔNTCP thresholds remained constant across the number of re-planning allowances (1, 2, and 3). Conclusion: In limited-resource settings that impeded high-frequency adaptations, ΔNTCP thresholds obtained from an MDP model could derive optimal timing of re-planning to minimize the likelihood of treatment toxicities.http://www.sciencedirect.com/science/article/pii/S240563162500020XPersonalized adaptive radiation therapyOrgans at riskNormal tissue complication probabilityMarkov decision processOptimal strategy |
| spellingShingle | Fatemeh Nosrat Cem Dede Lucas B. McCullum Raul Garcia Abdallah S.R. Mohamed Jacob G. Scott James E. Bates Brigid A. McDonald Kareem A. Wahid Mohamed A. Naser Renjie He Aysenur Karagoz Amy C. Moreno Lisanne V. van Dijk Kristy K. Brock Jolien Heukelom Seyedmohammadhossein Hosseinian Mehdi Hemmati Andrew J. Schaefer Clifton D. Fuller Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints Physics and Imaging in Radiation Oncology Personalized adaptive radiation therapy Organs at risk Normal tissue complication probability Markov decision process Optimal strategy |
| title | Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints |
| title_full | Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints |
| title_fullStr | Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints |
| title_full_unstemmed | Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints |
| title_short | Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints |
| title_sort | optimal timing of organs at risk sparing adaptive radiation therapy for head and neck cancer under re planning resource constraints |
| topic | Personalized adaptive radiation therapy Organs at risk Normal tissue complication probability Markov decision process Optimal strategy |
| url | http://www.sciencedirect.com/science/article/pii/S240563162500020X |
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