Early prediction of periventricular leukomalacia from MRI changes: a machine learning approach for risk stratification.

objectivesTo develop an accessible model integrating clinical, MRI, and radiomic features to predict periventricular leukomalacia (PVL) in high-risk infants. MATERIALS AND

methodsTwo hundred and seventeen infants (2015-2022) with suspected motor abnormalities, stratified into training (n = 124), internal validation (n = 31), and external validation (n = 62) cohorts by MRI scanners. Radiomic features were extracted from white matter regions on axial sequences. Feature selection employed T-tests, correlation filtering, Random Forest, and LASSO regression. Multivariate logistic models were evaluated by receiver operating characteristic (ROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, calibration, decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI).

resultsClinical predictors (gestational age, neonatal hypoglycemia, hypoxic-ischemic events, infection) and MRI features (dilated lateral ventricle, delayed myelination, and periventricular abnormal signal) were retained through univariate and multivariate screening. Five clinical predictive models, including clinical model (Model C), MRI model (Model M), Clinical + MRI model (Model C + M), radiomic model and Clinical + MRI + Radiomics model (Model C + M + R), were developed and validated using internal testing, bootstrapping, and external cohorts. Among them, Model C + M + R achieved the best overall performance, with an area under curve (AUC) of 0.96 (95% CI: 0.90-1.00), accuracy of 0.87 (95% CI: 0.76-0.94), sensitivity of 0.88, specificity of 0.85, PPV of 0.96, and NPV of 0.65 in the external validation cohort. Comparison with Model C + M, Model C + M + R demonstrated significant reclassification (NRI = 0.631, p < 0.001) and discrimination improvements (IDI = 0.037, p = 0.020).

conclusionConventional MRI-derived radiomics enhances PVL risk stratification. Interpretable accessible model for clinical use provides a new tool for high-risk infant evaluation. KEY POINTS: Question Periventricular leukomalacia requires early identification to optimize neurorehabilitation. Early white matter injury in infants is challenging to identify through conventional MRI visual assessment. Findings The clinical-MRI-radiomic model demonstrates the best performance for predicting PVL, with an AUC of 0.93 in the training and 0.96 in the external validation cohort. Clinical relevance An accessible and interpretable predictive tool for periventricular leukomalacia prediction has been developed and validated, which may enable earlier targeted interventions.