Purpose: To develop a clinical decision-making model for implementation of personalized organ-at-risk (OAR)-sparing adaptive radiation therapy (ART) that balances the costs and clinical benefits of radiation plan adaptations, without limiting the number of re-plannings per patient, and derive optimal policies for head and neck cancer (HNC) radiation therapy. Methods and Materials: By leveraging retrospective CT-on-Rails imaging data from 52 HNC patients treated at the University of Texas MD Anderson Cancer Center, a Markov decision process (MDP) model was developed to identify the optimal timing for plan adaptations based on the difference in normal tissue complication probability (∆NTCP) between the planned and delivered dose to OARs. To capture the trade-off between the costs and clinical benefits of plan adaptations, the end-treatment ∆NTCPs were converted to Quality Adjusted Life Years (QALYs) and, subsequently, to equivalent monetary values, by applying a willingness-to-pay per QALY parameter. Results: The optimal policies were derived for 96 combinations of willingness-to-pay per QALY (W) and re-planning cost (RC). The results were validated through a Monte Carlo (MC) simulation analysis for two representative scenarios: (1) W = $200,000 and RC = $1,000; (2) W = $100,000 and RC = $2,000. In Scenario (1), the MDP model's policy was able to reduce the probability of excessive toxicity, characterized by ∆NTCP ≥ 5%, to zero (down from 0.21 when no re-planning was done) at an average cost of $380 per patient. Under Scenario (2), it reduced the probability of excessive toxicity to 0.02 at an average cost of $520 per patient. Conclusions: The MDP model's policies can significantly improve the treatment toxicity outcomes compared to the current fixed-time (one-size-fits-all) approaches, at a fraction of their costs per patient. This work lays the groundwork for developing an evidence-based and resource-aware workflow for the widespread implementation of ART under limited resources.

The authors have declared no competing interest.

A.J.S. and C.D.F. have received related funding and salary support from the National Science Foundation (NSF)/National Institutes of Health (NIH) National Cancer Institute (NCI) via the Smart and Connected Health (SCH) Program (R01CA257814). C.D.F has received related funding and program support from the NIH National Institute of Dental and Craniofacial Research (NIDCR) Academic-Industrial Partnership (R01DE028290), and the NIH NCI MD Anderson Cancer Center Support Grant (CCSG) Image-Driven Biologically-Informed (IDBT) Program (P30CA016672). C.D.F has additionally received related travel, honoraria, and/or registration fee waivers from Elekta AB, with further unrelated travel, honoraria, and/or registration fee waivers from institutions such as the American Society for Radiation Oncology and the Radiological Society of North America. C.D.F has also received direct industry grant support, in-kind contributions, and honoraria from Elekta AB and collaborated in an unrelated consulting capacity with Varian/Siemens Healthineers, Philips Medical Systems, and GE Healthcare. K.K.B. has received support from the NIH NCI through the Image Guided Cancer Therapy Research Program at MD Anderson Cancer Center (P30CA016672) and through a licensing agreement with RaySearch Laboratories AB. A.C.M. and S.Y.L. have received grant and infrastructure support from MD Anderson Cancer Center via the Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program (K01DE030524). A.C.M. has also received unrelated support from the NIH NCI (1P01CA285249-01A1). S.Y.L has received related support from NIH NCI through the MD Anderson CCSG Head and Neck Program (P30CA016672). R.G. has received related support from NSF/NIH NCI via the SCH Program, Diversity Supplement (R01CA257814-02S1). L.M. has received related support from NSF/NIH NCI via the SCH Program, Diversity Supplement (R01CA257814-02S2).

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All data produced are available online at https://github.com/DSuarez03/Cost_Effective_ART_MDP.