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Prediction of motor developmental outcomes based on MRI radiomics in premature infants
Han Meng1,2, Fang He3, Fei Li4
1Postgraduate Training Base of the Third Medical Center of Chinese PLA General Hospital, Jinzhou Medical University, Beijing, China.
Insights
Combining brain MRI radiomics with clinical data significantly improves prediction of motor developmental delays in preterm infants. This approach aids early detection and intervention for better outcomes.
Area of Science:
- Neonatal neuroimaging
- Developmental pediatrics
- Medical artificial intelligence
Background:
- Preterm infants are at high risk for motor developmental delays.
- Early and accurate prediction of these outcomes is crucial for timely intervention.
- Current prediction methods may lack sufficient accuracy.
Purpose of the Study:
- To develop and evaluate a combined prediction model for motor developmental outcomes in preterm infants.
- To integrate radiomic features from brain MRI with clinical variables.
- To assess the predictive performance of radiomics, clinical data, and their combination.
Main Methods:
- Retrospective collection of brain MRI at term-equivalent age (TEA) and clinical data from preterm infants.
- Radiomic analysis of conventional MRI sequences.
- Development of separate and combined prediction models (radiomics, clinical, combined).
- Evaluation of model performance using Area Under the Curve (AUC).
Main Results:
- The study included 218 preterm infants, with 41.3% experiencing motor developmental delays.
- The combined model achieved the highest predictive performance with an AUC of 0.94.
- Clinical variables alone showed a strong predictive performance (AUC 0.87).
Conclusions:
- A combined model integrating MRI-based radiomic biomarkers and clinical variables significantly enhances the accuracy of predicting motor developmental outcomes in preterm infants.
- Birth weight and maternal hypertensive disorders are identified as key risk factors.
- This integrated approach improves prediction efficiency for adverse prognoses, facilitating early detection and intervention.
Background:
This study aimed to combine radiomics and clinical variables to predict motor developmental outcomes in preterm infants.
Methods:
Brain MRI images at term-equivalent age (TEA), perinatal clinical variables, and Bayley Scales of Infant and Toddler Development assessment results at 12-24 months corrected age were retrospectively collected for each preterm infant. Radiomic analysis was conducted on conventional MRI sequences, and a binary classification model for predicting motor neurodevelopmental outcomes was developed. A clinical prediction model was established on the basis of perinatal data, and a combined prediction model was created by integrating selected radiomic features. The predictive performance of each model was evaluated via the AUC.
Results:
Preterm infants (n = 218) were included in the study, with 90 (41.3%) diagnosed with motor developmental delays. The AUCs of the prediction models were 0.75 (sensitivity 0.69, specificity 0.69) for multisequence radiomics, 0.87 (sensitivity 0.83, specificity 0.77) for clinical variables, and 0.94 (sensitivity 0.91, specificity 0.87) for the combined multisequence radiomics and clinical variables model.
Conclusion:
A prediction model combining MRI-based radiomic biomarkers with clinical variables increases the accuracy of predicting motor developmental outcomes in preterm infants.
Impact:
Our results showed that birth weight and maternal hypertensive disorders are important risk factors for motor nerve development abnormalities in preterm infants with no significant abnormalities on magnetic resonance imaging. The combination of multisequence radiomic Rad-scores and clinical variable models can improve the prediction efficiency of adverse prognoses. These findings contribute to the early detection of motor nerve abnormalities in related preterm infants and timely interventions for the benefit of families with preterm infants.
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