Evidence map›Paper›PMID 41016831›Full record

ArticleMedical physics2025

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

Yujia Qian, Riao Dao, Lewei Zhao, Shiyi Zhou, Qingkun Fan, Guillaume Janssens, Bas A de Jong, Stefan Both, Erik Korevaar, Ting Hu and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

19 authors.

Yujia QianWuhan University, School of physics and technology, Wuhan, China.
Riao DaoWuhan University, School of physics and technology, Wuhan, China.
Lewei ZhaoDepartment of Radiation Oncology, Stanford University, California, USA.
Shiyi ZhouWuhan University, School of mathematics and statistics, Wuhan, China.
Qingkun FanWuhan University, School of mathematics and statistics, Wuhan, China.
Guillaume JanssensIon Beam Applications SA, Louvain-la-Neuve, Belgium.
Bas A de JongDepartment of Radiation Oncology, University Medical Center Groningen, Groningen, Netherlands.
Stefan BothDepartment of Radiation Oncology, University Medical Center Groningen, Groningen, Netherlands.
Erik KorevaarDepartment of Radiation Oncology, University Medical Center Groningen, Groningen, Netherlands.
Ting HuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Gang PengCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhiyong YangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Sheng ZhangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
FangFang YinMedical Physics Graduate Program, Duke Kunshan University, Suzhou, China.
Manju LiuMedical Physics Graduate Program, Duke Kunshan University, Suzhou, China.
Kunyu YangCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hong QuanWuhan University, School of physics and technology, Wuhan, China.
Xuanfeng DingDepartment of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, Michigan, USA.
Gang LiuCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

Develop a novel multi-approach dynamic Spot Scanning Proton Arc optimization frameworkR01CA301448 · NCI · WILLIAM BEAUMONT HOSPITAL RESEARCH INST · PI Xuanfeng Ding · 2025 to 2026
$2.2M
Elekta-Wuhan University Medical Physics Teaching and Research Fund 250000200Innovation Research Fund of Union Hospital 2024XHYN018National Natural Science Foundation of China 12005072National Natural Science Foundation of China 12575360National Natural Science Foundation of China 82373198NCI NIH HHS R01 CA301448NIH HHS R01CA301448Research Fund of Hubei Key Laboratory of Precision Radiation Oncology 2024ZLJZFL017Research Fund of Hubei Key Laboratory of Precision Radiation Oncology jzfs003Research Fund of Hubei Key Laboratory of Precision Radiation Oncology jzfs014
6 · The paper itself

Abstract

backgroundOne 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). PURPOSE: We introduce a novel adaptive energy switching SPArc optimization algorithm (SPArc-

methodsThe SPArc-

resultsWithout extra constraints in the energy ascending constraints, the SPArc-

conclusionsBased 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.

Indexed as

AlgorithmsProton TherapyRadiotherapy Planning, Computer-AssistedHumansRadiotherapy Dosagebeam delivery timeenergy layer switching timemachine‐specific delivery characteristicsproton arc therapyspot scanning

Identifiers

PMID41016831
PMCPMC13048771

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