1193 - An Advanced Proton Arc Therapy Technology for Joint Regulation of Treatment Efficiency and LET Distribution
Presenter(s)
G. Liu1, Y. Qian2, Q. Fan3, R. Dao2, T. Li2, H. Quan2, S. Zhang1, X. Ding4, and K. Yang1; 1Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430023, China, 2School of physics and technology, Wuhan University, Wuhan, China, 3School of Mathematics and Statistics, Wuhan University, Wuhan, China, 4Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, MI
Purpose/Objective(s): Proton arc therapy (PAT) energy layer selection is commonly optimized for delivery efficiency, yet linear energy transfer (LET) is also clinically relevant for proton radiotherapy. We propose a novel PAT algorithm that jointly accounts for delivery time and LET, explicitly incorporating target depth.
Materials/Methods: A bi-criteria energy-selection framework (PATTLET) was developed to integrate (i) machine-specific delivery time model and (ii) depth-aware LET characteristics for each candidate energy layer. Simulated annealing algorithm was used to select the optimal energy layers. Meanwhile, tumor target depth information was combined with energy-dependent LET values to guide energy selection across the full arc. Ten patients were selected to evaluate the plan quality and the delivery efficiency compared with previously spot-scanning proton arc therapy energy sequence optimization algorithm (PATseq). Additionally, an alternating direction method of multipliers-based LET optimization was subsequently applied for two plan groups to further refine the LET distribution which has been previously published.
Results: Compared to the PATseq-LET, the PATTLET plan exhibits superior LET distribution with higher delivery efficiency. All treatment plans met clinical dose constraints for target coverage and organs at risk (OARs) sparing, with comparable or improved homogeneity. The mean LET within the Clinical Target Volume (CTV) increased by an average of 18.96%, from 5.75±1.44 to 6.84±1.23 keV/µm (p < 0.01). Notably, this enhancement in LET was achieved without compromising OARs, as both dose and LET values remained comparable between the two algorithms. Specifically, the mean OAR dose was the 6.50±6.94% vs. 5.68±3.94% (p=0.17) of the prescription dose, and the mean LET in the OAR was 0.62±0.45 vs. 0.61±0.41 keV/µm(p=0.91) for the PATTLET and PATseq-LET plans. More specifically, PATTLET effectively shortened the energy layer switching time by 12.06% thus reducing the beam delivery time by 11.23%, in average.
Conclusion: Integrating depth-dependent LET into energy layer selection for PAT significantly enhances delivery efficiency and target LET distribution. This strategy shortens treatment time to improve patient comfort and reduce intrafractional motion risk, while substantially elevating biologically effective LET within the CTV to better target hypoxic and radioresistant tumor cells. It enables a more biologically informed and practically efficient PAT workflow, with the potential to improve local tumor control without increasing normal tissue toxicity.