A novel adaptive energy switching algorithm for proton arc therapy based on the machine-specific delivery characteristics

• Published in: Medical physics (Lancaster) Vol. 52; no. 10; p. e70011
• Main Authors: Qian, Yujia, Dao, Riao, Zhao, Lewei, Zhou, Shiyi, Fan, Qingkun, Janssens, Guillaume, de Jong, Bas A, Both, Stefan, Korevaar, Erik, Hu, Ting, Peng, Gang, Yang, Zhiyong, Zhang, Sheng, Yin, FangFang, Liu, Manju, Yang, Kunyu, Quan, Hong, Ding, Xuanfeng, Liu, Gang
• Format: Journal Article
• Language: English
• Published: United States 01.10.2025
• Subjects: Algorithms; Humans; Proton Therapy - instrumentation; Proton Therapy - methods; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; energy layer switching time; beam delivery time; spot scanning; proton arc therapy; machine‐specific delivery characteristics
• ISSN: 2473-4209
• DOI: 10.1002/mp.70011

One of the main challenges of utilizing spot-scanning proton arc therapy (SPArc) is treatment delivery efficiency. Previous studies focus on reducing the number of energy layers by ascending switching to shorten the beam delivery time. However, this is not true of all proton therapy systems. The new energy layer switching system was recently upgraded in the University Medical Center Groningen (UMCG), which enables a fast energy layer ascending switching (ELAS). We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc- ) based on the machine-specific delivery characteristics of proton therapy systems. The SPArc- optimization algorithm is based on the polynomial increasing feature of energy layer ascending switching. K-Medoids clustering analysis and simulated annealing algorithm were used to optimize the energy delivery sequence. Ten cases were selected to evaluate the plan quality, plan robustness, and the delivery efficiency compared with the previously SPArc energy sequence optimization algorithm, SPArc_seq. Without extra constraints in the energy ascending constraints, the SPArc- offers a better plan quality and robustness, while the treatment delivery efficiency was significantly improved compared to the SPArc_seq. More specifically, SPArc- effectively shortened the energy layer switching time and the beam delivery time by 34.03% and 31.10%, respectively, while offering better target dose conformality and generally lower dose to organs-at-risk. Based on the machine-specific delivery characteristics, we introduced a novel adaptive energy switching algorithm for efficient SPArc optimization, which could significantly improve delivery efficiency while enhancing the plan quality by eliminating no longer necessary constraints on the total number of energy layer ascending switching.