Optimal timing of organs-at-risk-sparing adaptive radiation therapy for head-and-neck cancer under re-planning resource constraints

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology Pub Date : 2025-01-01 DOI:10.1016/j.phro.2025.100715

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

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Abstract

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.

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