Abstract
Objective The objective of this study was to determine personalized optimal timing for re-planning in adaptive organ-at-risk-sparing radiation therapy under limited re-planning resources in patients with head and neck cancer (HNC).
Approach A novel Markov decision process (MDP) model was developed to determine optimal timing of re-plannings based on the patient’s expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities: xerostomia, dysphagia, parotid gland dysfunction, and feeding tube dependency at 6 months post-treatment. The MDP parameters were derived from a dataset comprising 52 HNC patients treated at the University of Texas MD Anderson Cancer Center between 2007 and 2013. Optimal re-planning strategies were obtained when the permissible number of re-plannings throughout the treatment was limited to 1, 2, and 3.
Main 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). This result underscores the importance of re-planning for patients experiencing the slightest change in ΔNTCP at fraction 10.
Significance In contrast to prior work that relies on a single re-planning allowance or predetermined time intervals using a one-size-fits-all approach, the MDP model proposed in this paper offers a personalized, resource-aware, and scalable decision-making tool; it identifies optimal dynamic re-planning schedules tailored to individual needs, guided by changes in NTCP.
Competing Interest Statement
Dr. Fuller has received related direct industry grant/in-kind support, honoraria, and travel funding from Elekta AB. Dr. Fuller has served in an unrelated consulting capacity for Varian/Siemens Healthineers. Philips Medical Systems, and Oncospace, Inc. Dr. Brock received unrelated support from the Helen Black Image Guided Fund, RaySearch Laboratories AB, the Apache Corporation.
Funding Statement
This work was supported by the National Institutes of Health (NIH) National Cancer Institute (NCI) Research Grant (R01CA257814), the NCI Cancer Center Support Grant Program in Image-Driven Biologically-informed Therapy (IDBT) Program (P30CA016672), and the Image Guided Cancer Therapy Research Program at The University of Texas MD Anderson Cancer Center, and the MD Anderson Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program.Lucas B. McCullum and Raul Garcia were supported by the NCI Supplement program under R01CA257814-02S2, and R01CA257814-03S2, respectively). Drs. Brigid McDonald and Kareem A. Wahid are both supported by the Image-guided Cancer Therapy T32 Fellowship (T32CA261856). Dr. Jolien Heukelom received related support from the Netherlands Rene Vogels Fond Fellowship. Drs. Heukelom and van Dijk received relevant support from the Dutch Cancer Society/KWF Kankerbestrijding. Dr. Fuller received related support from the National Institute of Dental and Craniofacial Research (NIDCR) (R01DE028290). Dr. Fuller has received related direct industry grant/in-kind support, honoraria, and travel funding from Elekta AB. Dr. Scott received relevant support under the NCI Case Comprehensive Cancer Center Support Grant (P30CA043703), with additional unrelated NIH support. Dr. Moreno received related support from NIDCR (K01DE030524) and NCI (K12CA088084) during the project period, with additional unrelated NIH support.
Author Declarations
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IRB of the University of Texas MD Anderson cancer Center gave ethical approval for this work (MDA RCR03-0800).
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Data Availability
The anonymized data from the enclosed manuscript has been deposited at DOI:10.6084/m9.figshare.25517338; and will be released upon peer-review publication.