Evidence map›Paper›PMID 41718510›Full record

ArticleJACC. Cardiovascular imaging2026

Aortic and Cardiac Structure From Routine CT Predict Cardiovascular Risk Beyond PREVENT and Coronary Calcium.

Daniel W Oo, Matthias Jung, Leonard Nürnberg, Jay Chandra, Audra Sturniolo, Nora Kerkovits, Saman Doroodgar Jorshery, Marcel Langenbach, Borek Foldyna, Douglas P Kiel and 4 more

Abstract readComparative Study
In one paragraph

Article in JACC. Cardiovascular imaging, 2026. 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. 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

14 authors.

Daniel W OoCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Matthias JungCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Leonard NürnbergArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
Jay ChandraCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Audra SturnioloCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Nora KerkovitsCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Saman Doroodgar JorsheryCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Marcel LangenbachCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Borek FoldynaCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
Douglas P KielHinda and Arthur Marcus Institute on Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
Hugo J W L AertsCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA; Radiology and Nuclear Medicine, GROW and CARIM Maastricht University, Maastricht, Netherlands.
Pradeep NatarajanCardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
Michael T LuCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA.
Vineet K RaghuCardiovascular Imaging Research Center (CIRC), Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: vraghu@mgh.harvard.edu.

Funding

Deep learning to assess cardiovascular disease risk from chest imagingK01HL168231 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Vineet Raghu · 2024 to 2026
$526k
NHLBI NIH HHS K01 HL168231
6 · The paper itself

Abstract

backgroundCardiovascular disease prevention relies on accurate risk assessment; however, existing scores are imprecise. Routine imaging may be opportunistically used to predict risk.

objectivesThe authors tested whether computed tomography (CT)-derived cardiac and aortic structure predicts major adverse cardiac events (MACE) beyond standard-of-care scores.

methodsThe authors developed a least absolute shrinkage and selection operator model to predict cardiovascular mortality using "radiomics" features describing cardiac and aortic structure from 13,437 lung cancer screening CTs from the NLST (National Lung Screening Trial). They compared this score to the PREVENT (Predicting Risk of Cardiovascular Disease Events) tool and the coronary artery calcium (CAC) score in patients with routine chest CT and no prior MACE from Mass General Brigham. They calculated discrimination using Harrel's C-index and MACE rates in high-risk groups by the PREVENT score (≥7.5% risk) or the radiomics score (≥3.0% in men, ≥1.5% in women).

resultsIn external testing (n = 14,577, mean age 61.1 ± 8.6 years, 47.5% male), 6.2% had incident MACE over a median of 5.7 years of follow-up. The radiomics score had higher discrimination for MACE than PREVENT (C-index 0.66 [95% CI: 0.64-0.68] vs 0.61 [95% CI: 0.59-0.63]) and was complementary to CAC (combined C-index 0.69 [95% CI: 0.67-0.71] vs CAC alone 0.66 [95% CI: 0.65-0.68]). High-risk patients by the radiomics score but not PREVENT had 3.6-fold higher MACE incidence than low-risk patients by both scores (23.1 [95% CI: 16.7-30.2] vs 6.5 [95% CI: 5.5-7.5] MACE per 1,000 person-years). Aortic surface-to-volume ratio, left ventricular volume, and left atrial short-axis length were among the most predictive features of MACE.

conclusionsCT-derived structural cardiac and aortic radiomics identified high-risk patients missed by clinical scores and further stratified risk among CAC risk groups. High-risk patients may benefit from intensified primary prevention.

Indexed as

Aortic DiseasesAortographyComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseMultidetector Computed TomographyVascular CalcificationAgedClinical RelevanceFemaleHeart Disease Risk FactorsHumansMaleMiddle AgedPredictive Value of TestsPrognosisartificial intelligenceatherosclerotic cardiovascular diseasecomputed tomography of the chestopportunistic screeningradiomicsrisk prediction

Identifiers

PMID41718510
PMCPMC12927619

What Socratic 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 Socratic graph.