Evidence map›Paper›PMID 40042636›Full record

Trial reportEuropean journal of nuclear medicine and molecular imaging2025

Subendocardial quantification enhances coronary artery disease detection in

Valerie Builoff, Mark Lemley, Robert J H Miller, Hidesato Fujito, Giselle Ramirez, Paul Kavanagh, Christopher Buckley, Marcelo Di Carli, Daniel S Berman, Piotr Slomka

Abstract readClinical Trial, Phase III
PubMed Full text at PMC Publisher
In one paragraph

Trial report in European journal of nuclear medicine and molecular imaging, 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. Utility ofCirculation. Cardiovascular imaging · 2025
    Trial
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

10 authors.

Valerie BuiloffDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Mark LemleyDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Robert J H MillerDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Hidesato FujitoDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Giselle RamirezDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Paul KavanaghDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Christopher BuckleyPharmaceutical Diagnostics, GE Healthcare, Buckinghamshire, UK.
Marcelo Di CarliDivision of Nuclear Medicine and Molecular Imaging, Department of Radiology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Daniel S BermanDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA.
Piotr SlomkaDepartments of Medicine, (Division of Artificial Intelligence in Medicine), and Imaging, Cedars-Sinai Medical Center, 6500 Wilshire Boulevard, Los Angeles, CA, 90048, USA. Piotr.Slomka@cshs.org.ORCID 0000-0002-6110-938X

Funding

Patient-specific Outcome Prediction from Cardiovascular Multimodality Imaging by Artificial IntelligenceR35HL161195 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI Piotr J Slomka · 2022 to 2026
$5.0M
Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CTR01EB034586 · NIBIB · CEDARS-SINAI MEDICAL CENTER · PI DI CARLI, MARCELO F, SLOMKA, PIOTR J · 2022 to 2025
$2.9M
NHLBI NIH HHS R35 HL161195NIBIB NIH HHS R01 EB034586
6 · The paper itself

Abstract

purposeThe new high resolution positron emission tomography (PET) myocardial perfusion imaging tracer,

methodsMyocardial perfusion image data from the

resultsIn total, 753 cases were analyzed, with a median age of 63 (interquartile range 56,69) and 69% male. AUC for detecting ≥ 50% stenosis was higher for subendocardial than transmural analysis for stress (0.795 vs. 0.762, respectively; p = 0.013) and ischemic (0.795 vs. 0.767, respectively; p = 0.049) TPD. Subendocardial and transmural TPD achieved diagnostic performance greater than or comparable to that of the readers' assessments in the total population as well as across subgroups of interest.

conclusionSubendocardial analysis of ischemic perfusion improves the detection of CAD compared to transmural quantitative analysis or expert visual reading. These measures can be derived automatically with minimal user interaction. Integrating TPD quantitative measures could standardize the diagnostic approach for this novel tracer.

Indexed as

AminopyridinesCoronary Artery DiseaseEndocardiumPositron-Emission TomographyPyridazinesAgedFemaleHumansMaleMiddle AgedMyocardial Perfusion ImagingAminopyridinesBMS 747158-02Pyridazines18F-flurpiridazCoronary artery diseasePerfusion quantitationPET-MPISubendocardial perfusion

Identifiers

PMID40042636
PMCPMC12221820

What OpenQuestion holds

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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.