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Cited by 9Pregnancy and preeclampsia studiesRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare

A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction

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Wang, Jida, Chen, Zhuying, Zhang, Hongxi, Li, Weikang, Li, Kui, Deng, Meixiang, Zou, Yu·2025·BMC Pregnancy and Childbirth
DOI 10.1186/s12884-025-07450-1PMC PMC11924743

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Abstract

OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR. MATERIALS AND METHODS: Retrospectively analyzed placental MRI and clinical data of 110 FGR cases and 158 healthy controls at 28-37 weeks of gestation from two campuses of ZWH. 227 cases from Hubin campus were randomly divided into training (n = 182) and internal testing set (n = 45). 41 cases from Xiaoshan campus were included in external testing set. Placental MRI features were extracted from sagittal T2WI. Mann-Whitney U test, redundancy analysis, and LASSO were used to identify the radiomics signature, and the best-performing radiomics model was constructed by comparing eight machine learning algorithms. Clinical factors determined by univariate and multivariate analyses. A united model and nomogram combining the radiomics Rad-score and clinical factors were established. The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. RESULTS: Of 1561 radiomics features, 10 strongly correlated with FGR were selected. The radiomics model using logistic regression performed best compared eight algorithms. 5 important clinical features identified by analysis. The united model demonstrated a good predictive performance in the training, internal testing and external testing sets, with AUC 0.941 (95% CI, 0.0.904-0.977), 0.899 (95% CI, 0.789-1) and 0.861 (95% CI 0.725-0.998), prediction accuracies 0.885, 0.844 and 0.805, precisions 0.871, 0.789 and 0.867, recalls 0.836, 0.833 and 0.684, and F1 scores 0.853, 0.811 and 0.765, respectively. The calibration and decision curves of the united model also showed good performance. Nomogram confirmed clinical applicability of the model. CONCLUSIONS: The proposed placental radiomics-clinical machine learning model is simple yet effective to predict FGR.

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  • Traditional screening approaches based on maternal factors and serum biomarkers typically achieve only moderate accuracy, with reported AUCs often in the range of 0.75-0.85.

    “Traditional screening approaches based on maternal factors and serum biomarkers typically achieve only moderate accuracy, with reported AUCs often in the range of 0.75–0.859,.18”

    From Early detection of severe fetal growth restriction using multimodal deep learning based on ultrasound and prenatal biomarkers (2026) · cites this paper as [18]

    The Methods describe a model using clinical factors, and the Results report AUCs for a combined MRI-and-clinical model, not for traditional screening based on maternal factors and serum biomarkers. Searches for serum biomarkers and the 0.75–0.85 AUC range found no passage in our copy that establishes the claimed typical performance; the indexed passages provide no page numbers. (LLM, conf=0.74)

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Authors
Wang, J., Chen, Z., Zhang, H., Li, W., Li, K., Deng, M., & Zou, Y.
Affiliations
  • Women's Hospital, School of Medicine, Zhejiang University (CN)
  • Children's Hospital of Zhejiang University (CN)
Corresponding author
Zou, Yu
ORCID
Venue
BMC Pregnancy and Childbirth
Publisher
Springer Science and Business Media LLC
Published
2025-03-20
Volume
25
Issue
1
Pages
325-325
ISSN
1471-2393 (ISSN-L)
DOI
10.1186/s12884-025-07450-1
PMC
PMC11924743
OpenAlex
W4408671913
Cited by
9
References
33
Citations by year
2026: 8 · 2025: 1
FWCI
10.45
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Pregnancy and Childbirth (publishedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
fetal growth restriction, placental MRI, radiomics, prediction model, nomogram, clinical factors, T2-weighted imaging, early pregnancy, logistic regression, LASSO feature selection
MeSH terms
Machine Learning, Adult, Female, Fetal Growth Retardation, Gestational Age, Humans, Magnetic Resonance Imaging, Placenta, Predictive Value of Tests, Pregnancy, Retrospective Studies, Case-Control Studies, Nomograms

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