Evidence map›Paper›PMID 28378112›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2018

Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT.

Ivana Išgum, Bob D de Vos, Jelmer M Wolterink, Damini Dey, Daniel S Berman, Mathieu Rubeaux, Tim Leiner, Piotr J Slomka

Erratum issuedAbstract read
In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. 2022 Artificial intelligence primer for the nuclear cardiologist.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Review
  10. Future of Radionuclide Myocardial Perfusion Imaging: Transitioning from SPECT to PET.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023
    Article
  11. Automated cardiovascular risk categorization through AI-driven coronary calcium quantification in cardiac PET acquired attenuation correction CT.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Article
  12. Article
  13. Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023
    Article
  14. Article
  15. Article
  16. Performance of visual, manual, and automatic coronary calcium scoring of cardiacJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Article
  17. Using a deep learning algorithm to score coronary artery calcium in myocardial perfusion imaging: A real opportunity or just a new hype?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Article
  18. Review
  19. Article
  20. Future of nuclear cardiology is bright: Promise of cardiac PET/CT and artificial intelligence.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ivana IšgumImage Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands. i.isgum@umcutrecht.nl.
Bob D de VosImage Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Jelmer M WolterinkImage Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Damini DeyDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. A047N, Los Angeles, CA, 90048, USA.
Daniel S BermanDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. A047N, Los Angeles, CA, 90048, USA.
Mathieu RubeauxDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. A047N, Los Angeles, CA, 90048, USA.
Tim LeinerDepartment of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
Piotr J SlomkaDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd Ste. A047N, Los Angeles, CA, 90048, USA.

Funding

Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CTR01HL089765 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI SLOMKA, PIOTR J · 2007 to 2021
$6.1M
NHLBI NIH HHS R01 HL089765
6 · The paper itself

Abstract

backgroundWe investigated fully automatic coronary artery calcium (CAC) scoring and cardiovascular disease (CVD) risk categorization from CT attenuation correction (CTAC) acquired at rest and stress during cardiac PET/CT and compared it with manual annotations in CTAC and with dedicated calcium scoring CT (CSCT). METHODS AND

resultsWe included 133 consecutive patients undergoing myocardial perfusion

conclusionAutomatic CAC scoring from CTAC PET/CT may allow routine CVD risk assessment from the CTAC component of PET/CT without any additional radiation dose or scan time.

Indexed as

AdultAgedAged, 80 and overCalciumCardiovascular DiseasesCoronary VesselsFemaleHumansMaleMiddle AgedMyocardial Perfusion ImagingPositron-Emission TomographyPositron Emission Tomography Computed TomographyRubidium RadioisotopesCalciumRubidium-82Rubidium RadioisotopesAutomatic calcium scoringcardiac CTcardiovascular riskcoronary calciumCT attenuation correction map

Identifiers

PMID28378112
PMCPMC5628109

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.