Evidence map›Paper›PMID 40287890›Full record

ArticleEJNMMI physics2025

Auto-segmentation, radiomic reproducibility, and comparison of radiomics between manual and AI-derived segmentations for coronary arteries in cardiac [

Suning Li, Jake Kendrick, Martin A Ebert, Ghulam Mubashar Hassan, Nathaniel Barry, Keaton Wright, Sing Ching Lee, Jamie W Bellinge, Carl Schultz

Abstract read
PubMed Full text at PMC Publisher
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

9 authors.

Suning LiSchool of Physics, Mathematics and Computer Science, University of Western Australia, Crawley, WA, Australia. juliet.li@health.wa.gov.au.ORCID http://orcid.org/0009-0002-0007-9401
Jake KendrickSchool of Physics, Mathematics and Computer Science, University of Western Australia, Crawley, WA, Australia.
Martin A EbertSchool of Physics, Mathematics and Computer Science, University of Western Australia, Crawley, WA, Australia.
Ghulam Mubashar HassanSchool of Physics, Mathematics and Computer Science, University of Western Australia, Crawley, WA, Australia.
Nathaniel BarrySchool of Physics, Mathematics and Computer Science, University of Western Australia, Crawley, WA, Australia.
Keaton WrightDepartment of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley, WA, Australia.
Sing Ching LeeDepartment of Cardiology, Royal Perth Hospital, Perth, WA, Australia.
Jamie W BellingeMedical School, University of Western Australia, Crawley, WA, Australia.
Carl SchultzMedical School, University of Western Australia, Crawley, WA, Australia.

Funding

Charlies Foundation for Research N/AUniversity of Western Australia N/A
6 · The paper itself

Abstract

background[

results141 patients from the "effects of Vitamin K and Colchicine on vascular calcification activity" (VikCoVac, ACTRN12616000024448) trial were included. 113 were used to train an auto-segmentation model using nnUNet on [

conclusionsAn effective segmentation model for coronary arteries was developed and reproducible [ CLINICAL

trial registrationVIKCOVAC trial ("effects of Vitamin K and Colchicine on vascular calcification activity"). Unique identifier: ACTRN12616000024448. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=368825 .

Indexed as

[18F]NaF PETAuto-segmentationCoronary artery diseaseRadiomicsReproducibility

Identifiers

PMID40287890
PMCPMC12034606

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.