Evidence map›Paper›PMID 39343355›Full record

Trial reportJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2024

Head-to-head comparison of

Hidenobu Hashimoto, Keiichiro Kuronuma, Mark C Hyun, Donghee Han, Valerie Builoff, Sebastian Cadet, Damini Dey, Daniel S Berman, Jacek Kwiecinski, Piotr J Slomka

Abstract readClinical TrialComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

10 authors.

Hidenobu HashimotoDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Keiichiro KuronumaDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Cardiology, Nihon University, Tokyo, Japan.
Mark C HyunDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Donghee HanDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Valerie BuiloffDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Sebastian CadetDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Damini DeyDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Daniel S BermanDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jacek KwiecinskiDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Cardiology and Structural Heart Diseases, Medical University of Silesia in Katowice, Katowice, Poland.
Piotr J SlomkaDepartment of Medicine, Division of Artificial Intelligence in Medicine, Imaging, and Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Electronic address: Piotr.slomka@cshs.org.

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
Integrated analysis of coronary anatomy and biology with 18F-fluoride PET and CT angiographyR01HL135557 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2017 to 2020
$3.0M
NHLBI NIH HHS R01 HL135557NHLBI NIH HHS R35 HL161195
6 · The paper itself

Abstract

backgroundWe compared silicone photomultipliers with digital photon counting (SiPM) and photomultiplier tubes (PMT) positron emission tomography (PET) in imaging coronary plaque activity with

methodsIn 25 cardiovascular disease patients (mean age 67 ± 12 years), we conducted

resultsThere were significant differences in image noise and background activity between the two PETs (Image noise (%), PMT: 7.6 ± 3.7 vs SiPM: 4.0 ± 2.3, P < 0.001; background activity, PMT: 1.4 ± 0.4 vs SiPM: 1.0 ± 0.3, P < 0.001). Similarly, the SNR and TBR were significantly higher in vessels scanned with the SiPM PET (SNR, PMT: 16.3 ± 11.5 vs SiPM: 32.7 ± 29.8, P < 0.001; TBR, PMT: 0.8 ± 0.4 vs SiPM: 1.1 ± 0.6, P < 0.001). SiPM PET image reconstruction with a 256 matrix, 1.4 mm pixel, and 2 mm Gaussian filter provided best trade off in terms of maximal SNR, TBR, and clinically practical file size.

conclusionsIn

Indexed as

Coronary Artery DiseaseFluorine RadioisotopesPositron-Emission TomographySodium FluorideAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedPhotonsProspective StudiesRadiopharmaceuticalsSignal-To-Noise RatioFluorine-18Fluorine RadioisotopesRadiopharmaceuticalsSodium Fluoride(18)F-sodium fluoride coronary PETEVOLVEImage qualityPhotomultiplier tubes PETSilicone photomultipliers with digital photon counting PET

Identifiers

PMID39343355
PMCPMC11964467

What OpenQuestion holds

Textmetadata
LicenceTDM
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.