Evidence map›Paper›PMID 39902660›Full record

ArticleJournal of applied clinical medical physics2025

Dosimetric sensitivity of an enhanced leaf model (ELM) for individual versus averaged machines.

Rafail Panagi, Rhydian Caines, Carl G Rowbottom

Abstract read
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Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rafail PanagiMedical Physics Department, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.ORCID https://orcid.org/0009-0002-5008-3732
Rhydian CainesMedical Physics Department, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.ORCID https://orcid.org/0000-0002-9444-5902
Carl G RowbottomMedical Physics Department, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.ORCID https://orcid.org/0000-0002-5716-6840

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the introduction of a new multi-leaf collimator (MLC) enhanced leaf model (ELM) in the Varian Eclipse™ treatment planning system, there is currently limited data regarding the dosimetric sensitivity to real-world variation in the ELM parameters, and its clinical relevance. PURPOSE: To characterize the variation in ELM parameters across a large department with ten linear accelerators and investigate the feasibility of using a single machine-averaged ELM for treatment planning. This could achieve time and resource savings from reduced quality assurance, while allowing easy transfer of patients between machines.

methodsClinical plans of a range of sites (head and neck, prostate, breast, lung, and brain), techniques (VMAT, IMRT, SBRT, and SRS), and energies (6 MV, 6 MV FFF, 10 MV, and 10 MV FFF) were recalculated on Varian TrueBeam™ (120 MLC) and Varian EDGE™ (HD120 MLC), with machine-specific ELM beam models, an averaged machine and an outlier machine model. A range of clinically relevant metrics relating to target coverage (e.g. PTV D

resultsFor the target metrics, the maximum percentage deviation from the mean was 0.422%, 0.157%, and 1.956% for the cases of the individual machines, the averaged machine and the outlier machine correspondingly, while the maximum absolute dose differences were 0.28 Gy, 0.07 Gy, and 0.38 Gy. For the OAR metrics, the maximum deviation from the mean was 1.833%, 0.204%, and 5.722% for the individual, averaged, and outlier machines, while the maximum absolute dose differences were 0.41 Gy, 0.10 Gy, and 0.97 Gy.

conclusionsFor machines that are well matched in terms of dosimetry for transmission and sweeping gap fields, the use of an averaged machine model is unlikely to introduce clinically significant dosimetric differences to treatment plans.

Indexed as

NeoplasmsOrgans at RiskParticle AcceleratorsPhantoms, ImagingRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedAlgorithmsFemaleHumansMaleQuality Assurance, Health CareRadiometryRadiotherapy Dosagedosimetric leaf gapenhanced leaf modelingmulti‐leaf collimator

Identifiers

PMID39902660
PMCPMC11969076

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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.