Evidence map›Paper›PMID 40244535›Full record

ArticleEJNMMI physics2025

Robustness of textural analysis features in quantitative

Alastair J Gemmell, Colin M Brown, Surajit Ray, Alexander Small

Abstract read
In one paragraph

Article in EJNMMI physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Alastair J GemmellCollege of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK. alastair.gemmell@glasgow.ac.uk.ORCID http://orcid.org/0000-0002-6032-5152
Colin M BrownDepartment of Clinical Physics & Bioengineering, NHS Greater Glasgow & Clyde, Glasgow, UK.
Surajit RaySchool of Mathematics & Statistics, University of Glasgow, Glasgow, UK.
Alexander SmallDepartment of Clinical Physics & Bioengineering, NHS Greater Glasgow & Clyde, Glasgow, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTextural Analysis features in molecular imaging require to be robust under repeat measurement and to be independent of volume for optimum use in clinical studies. Recent EANM and SNMMI guidelines for radiomics provide advice on the potential use of phantoms to identify robust features (Hatt in EJNMMI, 2022). This study applies the suggested phantoms to use in SPECT quantification for two radionuclides,

methodsAcquisitions were made with a uniform phantom to test volume dependency and with a customised 'Revolver' phantom, based on the PET phantom described in Hatt (EJNMMI, 2022) but with local adaptations for SPECT. Each phantom was filled separately with

resultsFeatures showing high volume dependency or high Coefficient of Variation (indicating poor repeatability) were removed from the list of features that may be suitable for use in clinical studies. After feature reduction, there were 39 features for

conclusionThe use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using

Indexed as

177Lu99 mTcPhantomQuantificationRadiomicsSPECTTextural analysis

Identifiers

PMID40244535
PMCPMC12006590

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Registered trials

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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.