Evidence map›Paper›PMID 38755547›Full record

SynthesisBMC medical imaging2024

The value of fetal magnetic resonance imaging in diagnosis of congenital anomalies of the fetal body: a systematic review and meta-analysis.

Louise Wilson, Elspeth H Whitby

Abstract readSystematic ReviewMeta-Analysis
In one paragraph

Synthesis in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Genetic Disorders Detectable by Fetal MRI: A Review.Diagnostics (Basel, Switzerland) · 2026
    Review
  4. Article
  5. 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

2 authors.

Louise WilsonSchool of Medicine and Population Health, University of Sheffield, Level 4, Jessop Wing, Tree Root Walk, Sheffield, S10 2SF, UK. Louise.wilson@sheffield.ac.uk.
Elspeth H WhitbySchool of Medicine and Population Health, University of Sheffield, Level 4, Jessop Wing, Tree Root Walk, Sheffield, S10 2SF, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo undertake a systematic review to assess the accuracy of fetal MRI in diagnosis of non-CNS congenital anomalies of the fetal body in comparison with antenatal ultrasound when correlated to postnatal diagnosis.

methodsSearches were conducted from electronic databases, key journals and reference lists for eligible papers. Inclusion criteria was original research studies comparing the diagnostic results of antenatal ultrasound, fetal MRI and final postnatal diagnosis via imaging, surgery or post-mortem testing. Studies of CNS anomalies were excluded. Studies were assessed for risk of bias by two reviewers working independently and data was then extracted by a single reviewer.

results12 studies were included with a total of 361 eligible patients who underwent USS and MRI and had a postnatal diagnosis. USS alone had a diagnostic accuracy of 60.6% whereas MRI had an improved diagnostic accuracy of 86.4%. The overall odds ratio was 0.86 (CI 0.202-1.519 and p-value < 0.01).

conclusionFetal MRI makes a significant contribution to accurate diagnosis of congenital abnormalities of the fetal body; especially in genito-urinary anomalies. More research is needed to improve the evidence base for the role of fetal MRI in diagnosis of congenital anomalies in other body systems.

Indexed as

Magnetic Resonance ImagingPrenatal DiagnosisCongenital AbnormalitiesFemaleHumansPregnancyReproducibility of ResultsSensitivity and SpecificityUltrasonography, PrenatalCongenital anomalyFetalMagnetic resonance imagingPrenatal diagnosisUltrasound

Identifiers

PMID38755547
PMCPMC11097489

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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.