Evidence map›Paper›PMID 32861654›Full record

ArticleJACC. Cardiovascular imaging2020

Myocardial Infarction Associates With a Distinct Pericoronary Adipose Tissue Radiomic Phenotype: A Prospective Case-Control Study.

Andrew Lin, Márton Kolossváry, Jeremy Yuvaraj, Sebastien Cadet, Priscilla A McElhinney, Cathy Jiang, Nitesh Nerlekar, Stephen J Nicholls, Piotr J Slomka, Pál Maurovich-Horvat and 2 more

Registry-linked trialOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Cardiovascular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06029777 (Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients), which is not on this map. Cited by 99 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
99citing papers in PubMed, 5 pooled it
12.7field-weighted citation impact, top 1% of its field
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.

NCT06029777 completednot on this mapstarted 2023, after this paper: background citation

Peri-luminal COROnary CTa AI-driven radiOMICS to Identify Vulnerable Patients

TypeobservationalSponsorIRCCS San RaffaeleRan2023 to 2024Enrolled2,190ConditionsCoronary Artery Disease
3 · Its place in the literature

Who cites it

99 citing papers in PubMed, 5 syntheses or guidelines pooled it, 154 citations in OpenAlex.

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39 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 3 countries.

Andrew LinBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California; Monash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Márton KolossváryMTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
Jeremy YuvarajMonash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Sebastien CadetArtificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, California.
Priscilla A McElhinneyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California.
Cathy JiangMonash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Nitesh NerlekarMonash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Stephen J NichollsMonash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Piotr J SlomkaArtificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, Los Angeles, California.
Pál Maurovich-HorvatMedical Imaging Centre, Semmelweis University, Budapest, Hungary.
Dennis T L WongMonash Cardiovascular Research Centre, Monash University and MonashHeart, Monash Health, Clayton, Victoria, Australia.
Damini DeyBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California. Electronic address: damini.dey@cshs.org.
Monash Health · AUCedars-Sinai Medical Center · USSemmelweis University · HU

Funding

Integrated prediction of cardiovascular events by automated coronary plaque and pericoronary adipose tissue quantification from CT AngiographyR01HL148787 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2020 to 2023
$3.0M
Automated quantitative CT imaging of epicardial adipose tissue and risk of cardiac eventsR01HL133616 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI DEY, DAMINI · 2016 to 2019
$2.6M
NHLBI NIH HHS R01 HL133616NHLBI NIH HHS R01 HL148787
6 · The paper itself

Abstract

objectivesThis study sought to determine whether coronary computed tomography angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could distinguish patients with acute myocardial infarction (MI) from patients with stable or no coronary artery disease (CAD).

backgroundImaging of PCAT with CCTA enables detection of coronary inflammation. Radiomics involves extracting quantitative features from medical images to create big data and identify novel imaging biomarkers.

methodsIn a prospective case-control study, 60 patients with acute MI underwent CCTA within 48 h of admission, before invasive angiography. These subjects were matched to patients with stable CAD (n = 60) and controls with no CAD (n = 60) by age, sex, risk factors, medications, and CT tube voltage. PCAT was segmented around the proximal right coronary artery (RCA) in all patients and around culprit and nonculprit lesions in patients with MI. PCAT segmentations were analyzed using Radiomics Image Analysis software.

resultsOf 1,103 calculated radiomic parameters, 20.3% differed significantly between MI patients and controls, and 16.5% differed between patients with MI and stable CAD (critical p < 0.0006); whereas none differed between patients with stable CAD and controls. On cluster analysis, the most significant radiomic parameters were texture or geometry based. At 6 months post-MI, there was no significant change in the PCAT radiomic profile around the proximal RCA or nonculprit lesions. Using machine learning (XGBoost), a model integrating clinical features (risk factors, serum lipids, high-sensitivity C-reactive protein), PCAT attenuation, and radiomic parameters provided superior discrimination of acute MI (area under the receiver operator characteristic curve [AUC]: 0.87) compared with a model with clinical features and PCAT attenuation (AUC: 0.77; p = 0.001) or clinical features alone (AUC: 0.76; p < 0.001).

conclusionsPatients with acute MI have a distinct PCAT radiomic phenotype compared with patients with stable or no CAD. Using machine learning, a radiomics-based model outperforms a PCAT attenuation-based model in accurately identifying patients with MI.

Indexed as

Coronary Artery DiseaseMyocardial InfarctionAdipose TissueAgedAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor AntagonistsCoronary AngiographyFemaleHumansMaleMiddle AgedPhenotypePredictive Value of TestsProspective StudiesAngiotensin-Converting Enzyme InhibitorsAngiotensin Receptor Antagonistscoronary computed tomography angiographymachine learningmyocardial infarctionpericoronary adipose tissueradiomics

Identifiers

PMID32861654
PMCPMC7996075
OpenAlexW3080428383

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

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 Socratic graph.