Evidence map›Paper›PMID 26590779›Full record

ReviewCardiology clinics2016

Automated Quantitative Nuclear Cardiology Methods.

Manish Motwani, Daniel S Berman, Guido Germano, Piotr Slomka

Abstract readReview
In one paragraph

Review in Cardiology clinics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Myocardial perfusion and left ventricular quantitative parameters obtained using gated myocardial SPECT: Comparison of three software packages.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2018
    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

4 authors.

Manish MotwaniDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA.
Daniel S BermanDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA; David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Guido GermanoDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA; David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Piotr SlomkaDepartments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA; David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. Electronic address: piotr.slomka@cshs.org.

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

Quantitative analysis of SPECT and PET has become a major part of nuclear cardiology practice. Current software tools can automatically segment the left ventricle, quantify function, establish myocardial perfusion maps, and estimate global and local measures of stress/rest perfusion, all with minimal user input. State-of-the-art automated techniques have been shown to offer high diagnostic accuracy for detecting coronary artery disease, as well as predict prognostic outcomes. This article briefly reviews these techniques, highlights several challenges, and discusses the latest developments.

Indexed as

Myocardial Perfusion ImagingTomography, Emission-Computed, Single-PhotonHumansPositron-Emission TomographyPredictive Value of TestsReproducibility of ResultsAutomated quantitationIschemiaLeft ventricular ejection fractionMyocardial functionMyocardial perfusionPETSPECTTotal perfusion deficit

Identifiers

PMID26590779
PMCPMC4877137

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.