ArticleBMC pregnancy and childbirth2025
Radiomics-based correlation analysis of fetal brain MRI features and children's neurodevelopmental outcomes in monochorionic twins.
Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Radiomics in fetal brain MRI: a narrative review.European radiology experimental · 2026Review
- A multi-task deep learning and radiomics framework for fetal anatomical structure detection and classification in ultrasound imaging.Scientific reports · 2026Article
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13 authors.
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Abstract
objectiveTo characterize fetal brain MRI features in monochorionic twin pregnancies based on radiomics; and to investigate the correlation between these radiomic features and subsequent neurodevelopmental outcomes.
methodsThis retrospective cohort study analyzed 26 monochorionic twin pregnancies (36 fetus included) who underwent fetal brain MRI (Siemens Magnetom Skyra 3.0 T or Philips Ingenia 3.0 T). Neurodevelopmental assessment categorized 20 monochorionic twins into the good neurodevelopmental group and 16 into the moderate neurodevelopmental group. MRI textural features of different brain areas were quantified by composite radiomics score and individual radiomics-feature score, and the correlation between these scores and neurodevelopmental outcomes during postnatal follow-up was analyzed.
resultsQuantitative radiomic analysis revealed significantly higher radiomics score in the good neurodevelopmental group for the following regions: periventricular white matter (PWM), frontal, parietal and temporal lobes (all P < 0.05). Four specific radiomics-feature score demonstrated significant group differences in these regions: Gray Level Dependence Matrix (GLDM) in PWM, first-order statistical feature in frontal lobe, Gray Level Size Zone Matrix (GLSZM) in parietal lobe, and GLSZM in temporal lobe. For predictive modeling, we identified five high-discriminatory features representing distinct feature categories: shape features (Elongation), first-order statistical features (Kurtosis), and texture features (GLCM: Cluster Shade, GLRLM: Long Run High Gray Level Emphasis, GLSZM: Size Zone Non Uniformity). The logistic regression model with nested cross-validation incorporating these features achieved excellent predictive performance for neurodevelopmental outcomes [Mean of AUC = 0.8900 (± 0.1133)].
conclusionsRadiomics scores were higher in good neurodevelopmental fetuses, and the selected radiomics features may be helpful for predicting the neurodevelopmental outcomes of monochorionic twins.
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