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Updated: Jan 15, 2026
Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods
Published on: March 10, 2020
Radiomics models predict early neurodevelopment in infants with congenital cardiac septal defects
Shuting Cheng1,2, Meijiao Zhu2, Feng Yang2
1Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Insights
Congenital cardiac septal defect (CCSD) infants may experience neurodevelopmental delays. A model combining brain MRI radiomics and clinical data can predict these delays early in infants with CCSD.
Area of Science:
- Pediatric Neurology
- Cardiology
- Medical Imaging
Background:
- Congenital cardiac septal defect (CCSD) is associated with potential neurodevelopmental abnormalities in infants.
- Early identification of neurodevelopmental risks in these children is crucial for timely intervention.
Purpose of the Study:
- To develop a predictive model for neurodevelopmental outcomes in infants with CCSD.
- To integrate brain magnetic resonance imaging (MRI) radiomics features with clinical data for enhanced prediction.
Main Methods:
- Prospective enrollment of 48 infants diagnosed with CCSD.
- Assessment of neurodevelopmental outcomes at 1 year using the Gesell Developmental Scale.
- Extraction of radiomics features from neonatal brain MRI and collection of clinical data.
Main Results:
- 25% of infants with CCSD showed developmental delays at 1 year, particularly in language.
- Brain MRI radiomics features (e.g., shape, wavelet, LoG) and clinical factors (e.g., guardian education, birth weight) were identified as independent predictors for various developmental domains.
Conclusions:
- Neurodevelopmental delays are present in a significant portion of infants with CCSD.
- A combined model utilizing neonatal brain MRI radiomics and clinical factors shows promise for early neurodevelopmental prediction in infants with CCSD.
Background:
Patients with congenital cardiac septal defect (CCSD) may still have neurodevelopmental abnormalities. This study aims to establish a combined model of brain magnetic resonance imaging (MRI) and clinical features for early prediction of neurodevelopment in infants with CCSDs.
Methods:
Forty-eight infants diagnosed with CCSD by cardiac ultrasonography were prospectively enrolled. Neurodevelopmental outcome was assessed at 1 year of age using the Gesell Developmental Scale. Radiomics features of neonatal brain MRI and clinical characteristics at birth and follow-up were obtained. After the relationship between neurodevelopmental outcomes and clinical or radiomics features was assessed using Pearson and Spearman correlation analysis, we performed multiple stepwise linear regression models to determine the most significant predictors of developmental status.
Results:
At 1 year of age, 25% (12/48) of infants with CCSD exhibited developmental delays, with the highest percentage of delays observed in language development (10.4%). Multiple stepwise linear regression analysis showed that shape features were independent predictors of gross motor (standardized coefficients β=-0.34; P=0.02). Guardians' education was an independent predictor of fine motor (standardized coefficients β=0.36; P=0.01). Wavelet-HLH-gray-level co-occurrence matrix-maximum correlation coefficient were independent predictors of adaptation (standardized coefficients β=-0.32; P=0.03). Wavelet-LHH-gray-level size zone matrix-small area low gray level emphasis (standardized coefficients β=0.31; P=0.03) and LoG-neighbourhood gray-tone difference matrix-strength (3D, σ=10 mm) (standardized coefficients β=0.29; P=0.03) were independent predictors of language development. Birth weight (standardized coefficients β=0.36; P<0.001) and wavelet-HLH-GLCM-cluster shade (standardized coefficients β=-0.27; P=0.04) were independent predictors of personal-social behavior.
Conclusions:
Some infants with CCSD may have neurodevelopmental delays at 1 year of age. A combined model based on radiomics features of neonatal brain MRI and clinical factors may be useful in predicting early neurodevelopment in infants with CCSD.
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