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 (Offlinetrigger-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 (Offlineweekly), daily plan selection from a library (Librarystatic and Libraryprogsressive) and a fast, approximate daily online re-optimization approach (Onlinere-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 Onlinere-opt performed as well as full re-planning. Compared to current practice, Onlinere-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. Offlineweekly 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 Offlineweekly, Librarystatic, Libraryprogressive, and Onlinere-opt was expected to increase by 3, 8, 21, and 66 h for 35 fraction treatment courses compared to Offlinetrigger-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.