Evidence map›Paper›PMID 40660836›Full record

ArticleMedical physics2025

Development and experimental validation of an in-house treatment planning system with greedy energy layer optimization for fast IMPT.

Aoxiang Wang, Ya-Nan Zhu, Jufri Setianegara, Yuting Lin, Peng Xiao, Qingguo Xie, Hao Gao

Abstract readValidation Study
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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Aoxiang WangDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Ya-Nan ZhuDepartment of Radiation Oncology, University of Kansas Medical Center, Kansas, USA.
Jufri SetianegaraDepartment of Radiation Oncology, University of Kansas Medical Center, Kansas, USA.
Yuting LinDepartment of Radiation Oncology, University of Kansas Medical Center, Kansas, USA.
Peng XiaoDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Qingguo XieDepartment of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China.
Hao GaoDepartment of Radiation Oncology, University of Kansas Medical Center, Kansas, USA.

Funding

Novel Optimization Methods and Treatment Planning System for Clinically-Deliverable Truly-Hybrid Proton-Photon RadiotherapyR37CA250921 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Hao Gao · 2021 to 2026
$2.2M
Simultaneous dose and dose rate optimization for clinical FLASH proton radiotherapyR01CA261964 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GAO, HAO · 2022 to 2025
$2.0M
NCI NIH HHS R01 CA261964NCI NIH HHS R37 CA250921NIH HHS R01CA261964NIH HHS R37CA250921
6 · The paper itself

Abstract

backgroundIntensity-modulated proton therapy (IMPT) using pencil beam technique scans tumor in a layer by layer, then spot by spot manner. It can provide highly conformal dose to tumor targets and spare nearby organs-at-risk (OAR). Fast delivery of IMPT can improve patient comfort and reduce motion-induced uncertainties. Since energy layer switching time dominants the plan delivery time, reducing the number of energy layers is important for improving delivery efficiency. Although various energy layer optimization (ELO) methods exist, they are rarely experimentally validated or clinically implemented, since it is technically challenging to integrate these methods into commercially available treatment planning system (TPS) that is not open-source. PURPOSE: This work develops and experimentally validates an in-house TPS (IH-TPS) that incorporates a novel ELO method for the purpose of fast IMPT.

methodsThe dose calculation accuracy of IH-TPS is verified against the measured beam data and the RayStation TPS. For treatment planning, a novel ELO method via greed selection algorithm is proposed to reduce energy layer switching time and total plan delivery time. To validate the planning accuracy of IH-TPS, the 3D gamma index is calculated between IH-TPS plans and RayStation plans for various scenarios. Patient-specific quality-assurance (QA) verifications are conducted to experimentally verify the delivered dose from the IH-TPS plans for several clinical cases.

resultsDose distributions in IH-TPS matched with those from RayStation TPS, with 3D gamma index results exceeding 95% (2 mm, 2%). The ELO method significantly reduced the delivery time while maintaining plan quality. For instance, in a brain case, the number of energy layers was reduced from 78 to 40 (reduction of 38 layers), leading to a 62% reduction in total delivery time. Patient-specific QA validation with the IBA ProteusONE proton machine confirmed a > 95% pass rate for all cases.

conclusionsAn IH-TPS equipped with a novel ELO algorithm is developed and experimentally validated for the purpose of fast IMPT, with enhanced delivery efficiency and preserved plan quality.

Indexed as

Proton TherapyRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsHumansRadiotherapy DosageTime Factorsenergy layer optimizationproton therapytreatment planning system

Identifiers

PMID40660836
PMCPMC13624894

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.