Dosimetric advantages of adaptive IMPT vs. Enhanced workload and treatment time - A need for automation.

Introduction: In head-and-neck IMPT, trigger-based offline plan adaptation (Offline _{trigger-based} ) is often used. Our goal was to compare this to four alternative adaptive strategies for dosimetry, workload and treatment time, considering also foreseen further technological advancements, including anticipated automation.
Materials and Methods: Alternative strategies included weekly offline re-planning (Offline _weekly ), daily plan selection from a library (Library _static and Library _progsressive ) and a fast, approximate daily online re-optimization approach (Online _re-opt ). Impact on CTV coverage and NTCPs was assessed by simulations based on repeat-CTs from 15 patients. Full daily re-planning was used as dosimetric benchmark. Increases in workload and treatment time were estimated.
Results: Both for coverage and NTCPs, fast Online _re-opt performed as well as full re-planning. Compared to current practice, Online _{re -opt} showed enhanced probabilities for high coverage, and resulted in reductions in grade ≥ II NTCPs of 4.6 ± 1.7 %-point for xerostomia and 4.2 ± 2.3 %-point for dysphagia. Offline _weekly and library strategies did not show coverage enhancements and resulted in smaller NTCP improvements. Further automation can largely limit workload and treatment time increases. With anticipated further automation, adaptation-related workload of Offline _weekly , Library _static , Library _progressive , and Online _re-opt was expected to increase by 3, 8, 21, and 66 h for 35 fraction treatment courses compared to Offline _{trigger-based} . The corresponding adaptation-related prolonged treatment times were estimated to be 0, 4, 6, and 29 min/fraction.
Conclusion: Online adaptive strategies could approach dosimetric quality of full re-planning at the cost of additional workload and prolonged treatment time compared to the current offline adaptive strategy. Automation needs to play a key role in making more complex adaptive approaches feasible.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)