# Radiomics in fetal brain MRI: a narrative review

**Authors:** Francesco Pacchiano, Mario Tortora, Valentina Bordin, Francesca Gentile, Mario Cirillo, Fabio Tortora, Ferdinando Caranci, Lorenzo Ugga

PMC · DOI: 10.1186/s41747-026-00697-z · European Radiology Experimental · 2026-03-16

## TL;DR

This paper reviews how radiomics can improve fetal brain MRI by extracting hidden imaging features to support diagnosis and prognosis.

## Contribution

The paper provides a narrative review of radiomics in fetal brain MRI, highlighting its potential for clinical applications and future research directions.

## Key findings

- Radiomics can reveal subtle imaging patterns not visible to the human eye in fetal brain MRI.
- Applications include brain development assessment, Chiari II malformation, and prediction of ventriculomegaly persistence.
- Combining radiomics with deep learning may improve performance and interpretability.

## 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.

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.

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.

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.

## Linked entities

- **Diseases:** brain edema (MONDO:0006684)

## Full-text entities

- **Diseases:** hindbrain herniation (MESH:D004677), Chiari (MESH:D006502), preeclampsia (MESH:D011225), ventricular dilation (MESH:C566255), hydrocephalus (MESH:D006849), gliosis (MESH:D005911), brain abnormalities (MESH:D001927), fetal growth restriction (MESH:D005317), brain edema (MESH:D001929), developmental malformations (MESH:C564254), congenital defects (MESH:D000013), gestational diabetes (MESH:D016640), edema (MESH:D004487), developmental delays (MESH:D002658), Chiari II (MESH:D001139), ventricular enlargement (MESH:D006332), brain malformations (MESH:D020785), diabetic (MESH:D003920)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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Source: https://tomesphere.com/paper/PMC12992843