Radiomics in fetal brain MRI: a narrative review

Abstract Fetal MRI has emerged as a crucial supplement to prenatal ultrasonography in the evaluation of the developing brain and in identifying congenital defects and minor developmental malformations. While fetal brain MRI interpretation has always depended on visual examination of signal properties and morphology, images can provide quantitative information that could be missed or hidden from the human eye. Radiomics allows for characterizing tissue characteristics and heterogeneity by extracting quantitative information from imaging data. In this narrative review, after summarizing the technical foundations of fetal MRI radiomics (acquisition, preprocessing, segmentation, feature extraction and types, machine learning models, feature reproducibility and quality), we consider the following major clinical applications: brain development assessment and phenotyping; Chiari II malformation and brain edema phenotype; isolated ventriculomegaly and prediction of its persistence; and prognosis and neurodevelopmental outcome prediction. MRI radiomics presents a promising technique to improve the assessment of the fetal brain. Larger multicenter studies with standardized protocols are essential to improve generalizability and reduce variability. Combining radiomics with deep learning could enhance performance and interpretability, while biological validation, linking features to known tissue properties, will help confirm clinical relevance. Relevance statement Despite its early stage, MRI radiomics offers a new, data-driven lens to evaluate fetal brain development. By revealing subtle imaging patterns not visible to the eye, it may eventually support more accurate diagnosis, risk stratification, and personalized care. Key Points Fetal MRI adds value beyond ultrasound in the prenatal setting. Radiomics reveals hidden imaging features. Radiomics enhances diagnosis and prognosis in fetal brain assessment. Large multicenter studies are needed. Graphical Abstract