Predicting adverse perinatal outcomes in small-for-gestational-age fetuses using MRI, ultrasound, and clinical data.

Background: Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Existing sonographic approaches offer limited predictive accuracy. Combining fetal MRI, ultrasound and clinical data may improve perinatal prognostication. Purpose: To evaluate whether integrating prenatal MRI, ultrasound, and clinical features using machine learning (ML) improves prediction of adverse perinatal outcomes in FGR or small-for-gestational-age (SGA) pregnancies. Materials and Methods: This single-center study included prospectively enrolled FGR/SGA and retrospectively included appropriate-for-gestational-age cases, with follow-up through neonatal discharge. Twenty-seven features from MRI, ultrasound, and clinical data were used in the final analysis. Seven ML classifiers were trained using stratified 5-fold cross-validation to predict composite adverse neonatal outcomes (CANO) and non-reassuring fetal status (NRFS). Sensitivity and specificity of the top-performing model (based on area under the curve [AUC]) were compared to standard biometric thresholds (estimated fetal weight and/or abdominal circumference <10th/<3rd centiles). Multiparametric, MRI-only, and ultrasound-only models were compared, along with reduced models using 4 features for CANO and 2 for NRFS. Results: One hundred thirty-one participants were included (60 FGR/SGA, 71 appropriate-for-gestational-age). The random forest method achieved the highest AUC for predicting CANO (0.912; 95% confidence interval [CI], 0.83-0.99) and NRFS (0.834; 95% CI, 0.76-0.91). For CANO, the multiparametric model demonstrated a 25% higher sensitivity ( Conclusions: ML-based models integrating multimodal data may improve risk stratification for predicting adverse perinatal outcomes in FGR/SGA pregnancies.