Evidence map›Paper›PMID 42337462›Full record

SynthesisBMC medical imaging2026

Diagnostic accuracy of fetal cardiac magnetic resonance imaging in cardiac and thoracic vascular anomalies: a meta-analysis.

Chandana Bhargavi, Rathika Damodara Shenoy, Adrian Rassaf, Daiki Jonouchi, Elvis Anunwa, Suchetha S Rao, Nutan Kamath, Juan M Acuna

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Chandana Bhargavi *Department of Anatomy and Medical Imaging, American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda.ORCID http://orcid.org/0000-0002-7906-9654
Rathika Damodara Shenoy *Department of Pediatrics, Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, India.ORCID http://orcid.org/0000-0001-7518-3604
Adrian RassafDepartment of Anatomy and Medical Imaging, American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda.ORCID http://orcid.org/0009-0007-1136-444X
Daiki JonouchiDepartment of Anatomy and Medical Imaging, American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda.ORCID http://orcid.org/0009-0001-1025-5344
Elvis AnunwaDepartment of Anatomy and Medical Imaging, American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda.ORCID http://orcid.org/0009-0002-5926-3588
Suchetha S RaoDepartment of Pediatrics, Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, India.ORCID http://orcid.org/0000-0002-5232-9727
Nutan KamathDepartment of Pediatrics, Kasturba Medical College Mangalore, Manipal Academy of Higher Education, Manipal, India. nutan.kamath@manipal.edu.ORCID http://orcid.org/0000-0003-4458-3634
Juan M AcunaDepartment of Clinical Medicine, American University of Antigua College of Medicine, Osbourn, Antigua and Barbuda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFetal echocardiography (FE) is the primary modality for screening cardiac anomalies. Diagnostic limitations persist in complex lesions, thoracic vascular anomalies, late gestation, and cases with poor acoustic windows. Fetal cardiac magnetic resonance imaging (FCMRI) complements FE by providing additional anatomical details of cardiac and thoracic vascular anomalies, although its diagnostic test accuracy has not been systematically evaluated. This meta-analysis aimed to determine the diagnostic accuracy of FCMRI in detecting cardiac and thoracic vascular anomalies and to evaluate its potential role in prenatal decision-making.

methodsObservational studies evaluating FCMRI for cardiac and thoracic vascular anomalies were analyzed at the lesion-description level. Summary statistics were estimated using a bivariate random-effects and hierarchical summary receiver operating characteristic models. Subgroup analyses were performed across anatomical lesion categories, image acquisition methods, and acquisition eras.

resultsSeven studies involving 449 cardiac and thoracic vascular anomalies were analyzed. FCMRI demonstrated a sensitivity of 0.80 (95% CI: 0.66-0.89) and specificity of 0.98 (95% CI: 0.92-0.99), indicating acceptable accuracy, with a 95% prediction ellipse area of 0.29, suggesting moderate between-study heterogeneity. Pooled sensitivity [0.88 (95%CI: 0.79-0.93)] was highest for complex heart defects, with a tightly clustered prediction ellipse (0.04). Thoracic vascular anomalies showed lower sensitivity [0.75 (95%CI: 0.56-0.88)] and greater heterogeneity (0.39), reflecting diagnostic challenges. Only two studies showed incremental clinical utility of FCMRI in decision-making that varied by specific cardiac and thoracic vascular anomaly. The analysis was limited by the small number of studies, methodological heterogeneity, and the underrepresentation of true-negative cases.

conclusionsFetal CMRI can provide clinically useful anatomical information complementary to FE, particularly in late gestation and selected high-risk pregnancies. Although it demonstrated high specificity and moderate sensitivity overall, diagnostic performance varied across lesion subtypes, with aortic and aberrant vascular anomalies remaining the most challenging to diagnose prenatally. META ANALYSIS REGISTRATION: PROSPERO: CRD420251133115.

Indexed as

Fetal HeartHeart Defects, CongenitalMagnetic Resonance ImagingPrenatal DiagnosisVascular MalformationsFemaleHumansPregnancySensitivity and SpecificityAortaCardiac anomaliesComplex congenital heart diseaseHierarchical modelsPrenatalRandom effects model

Identifiers

PMID42337462
PMCPMC13681847

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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.