Evidence map›Paper›PMID 42287424›Full record

ArticlePediatric radiology2026

Normative volumetric growth modeling of the whole fetal body, placenta, and amniotic fluid for three-dimensional T2-weighted magnetic resonance imaging.

Alena Uus, Megan Hall, Charline Bradshaw, Anangsha Kumar, Jordina Aviles Verdera, Sara Neves Silva, Aysha Luis, Hadi Waheed, Pedro Alarcon Gil, Lucilio Cordero-Grande and 9 more

Abstract read
In one paragraph

Article in Pediatric radiology, 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. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Alena UusResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK. alena.uus@kcl.ac.uk.
Megan HallResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Charline BradshawResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Anangsha KumarResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Jordina Aviles VerderaResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Sara Neves SilvaResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Aysha LuisResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Hadi WaheedResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Pedro Alarcon GilBiomedical Image Technologies, ETSI Telecomunicacion, Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain.
Lucilio Cordero-GrandeResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Jacqueline MatthewResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Vanessa KyriakopoulouResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Maria DeprezResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Kathleen ColfordResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Alexia Egloff ColladoResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Joseph V HajnalResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Mary RutherfordResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Jana HutterResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.
Lisa StoryResearch Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, Westminster Bridge Road, London, SE1 7EH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMagnetic resonance imaging (MRI)-based volumetry of the fetus, placenta, and amniotic fluid is clinically valuable but rarely used due to labor-intensive manual segmentation of motion-corrupted two-dimensional (2-D) stacks. Existing deep learning approaches are typically limited to single structures and 2-D data, while no robust automated solution exists for whole-uterus volumetry in reconstructed three-dimensional (3-D) MRI, and normative reference ranges are lacking.

objectiveTo develop an automated pipeline for whole-uterus volumetry in 3-D T2-weighted fetal MRI and derive normative growth models for fetal, placental, and amniotic fluid volumes. MATERIALS AND

methodsMotion-corrupted T2-weighted stacks (0.55-3-T field strength) were reconstructed into 3-D isotropic images using deformable slice-to-volume reconstruction, followed by automated segmentation with a 3-D U-Net. The method was applied to 357 normal-control datasets with confirmed term birth (16-41 weeks gestational age range) to derive quadratic normative growth curves. Performance and clinical utility were further evaluated on 43 independent datasets.

resultsSegmentation was highly accurate (Dice: fetus 0.997, placenta 0.995, amniotic fluid 0.998) with low volume errors (<1%) and minimal manual refinement required in <25% of cases. In the control cohort, fetal and placental volumes increased with gestational age (P<0.001), while amniotic fluid followed a quadratic trend. Longitudinal growth rates were 146.6 cc/week (fetus) and 38.8 cc/week (placenta). Preterm pregnancies showed significantly lower fetal and placental volumes (P<0.001) and reduced amniotic fluid (P<0.01).

conclusionThis work presents the first automated pipeline for simultaneous whole-uterus volumetry in 3-D fetal MRI and establishes normative growth models across gestation. The approach enables accurate, standardized volumetric assessment and provides a practical tool for detecting abnormal growth patterns in both normal and high-risk pregnancies.

Indexed as

Amniotic FluidFetal DevelopmentImaging, Three-DimensionalMagnetic Resonance ImagingPlacentaFemaleHumansPregnancyReference ValuesAmniotic fluidFetusGrowthMagnetic resonance imagingPlacentaVolume

Identifiers

PMID42287424
PMCPMC13407711

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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.