Evidence map›Paper›PMID 41166032›Full record

Trial reportLa Radiologia medica2026

Deep learning-based PSMA PET segmentation repeatability: A post-hoc analysis of a single-center, prospective, test-retest trial.

Jake Kendrick, Roslyn J Francis, Ghulam Mubashar Hassan, Jeremy S L Ong, Robert Jeraj, Nathaniel Barry, Martin A Ebert

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in La Radiologia medica, 2026. 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. Review
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

7 authors.

Jake KendrickSchool of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling Highway, Mailbag M013, Crawley, Perth, WA, 6009, Australia. jake.kendrick@uwa.edu.au.ORCID http://orcid.org/0000-0001-6524-539X
Roslyn J FrancisAustralian Centre for Quantitative Imaging, University of Western Australia, Crawley, WA, Australia.
Ghulam Mubashar HassanSchool of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling Highway, Mailbag M013, Crawley, Perth, WA, 6009, Australia.
Jeremy S L OngDepartment of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, WA, Australia.
Robert JerajDepartment of Medical Physics and Human Oncology, University of Wisconsin, Madison, WI, USA.
Nathaniel BarrySchool of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling Highway, Mailbag M013, Crawley, Perth, WA, 6009, Australia.
Martin A EbertSchool of Physics, Mathematics and Computing, The University of Western Australia, 35 Stirling Highway, Mailbag M013, Crawley, Perth, WA, 6009, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe primary aim of this study was to quantify the subject-level test-retest repeatability of artificial intelligence (AI)-derived PSMA PET imaging biomarkers using a previously developed model. The secondary aim was to assess the performance of this segmentation model, which was trained on [

methodsThis was a post-hoc analysis of a prospective, single-center, test-retest trial. Seventeen patients with metastatic prostate cancer (mPCa) were randomised into groups, either receiving the same tracer ([

resultsRepeatability analysis demonstrated poorer repeatability for all biomarkers in the inter-tracer group. In the intra-tracer group, the AI-derived PSMA-positive tumour volume had a repeatability coefficient of 13.8% for higher volume disease patients (≥ median tumour volume). There was no significant difference in the per-scan lesion-level positive predictive value of the AI model between [

conclusionAI-based PSMA-positive tumour volume calculations have repeatability limits that are consistent with the use of the Response Evaluation Criteria in PSMA PET/CT (RECIP 1.0) criteria for higher volume disease patients when the same tracer is used. Substantially wider repeatability limits in the inter-tracer group provide evidence that response assessment should be conducted using the same radiotracer.

Indexed as

Deep LearningPositron-Emission TomographyPositron Emission Tomography Computed TomographyProstatic NeoplasmsAgedGallium IsotopesGallium RadioisotopesGlutamate Carboxypeptidase IIHumansMaleMiddle AgedNiacinamideOligopeptidesProspective StudiesRadiopharmaceuticalsReproducibility of Resultsgallium 68 PSMA-11Gallium IsotopesGallium RadioisotopesGlutamate Carboxypeptidase IINiacinamideOligopeptidesPSMA-1007RadiopharmaceuticalsDeep LearningProstate CancerPSMA PETRECIP 1.0Repeatability

Identifiers

PMID41166032
PMCPMC12932408

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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.