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Radiomics in fetal brain MRI: a narrative review.

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Area of Science:

  • Medical Imaging
  • Quantitative Imaging
  • Radiology

Background:

  • Fetal MRI complements ultrasound for prenatal brain evaluation and malformation detection.
  • Traditional interpretation relies on visual assessment, potentially missing quantitative details.
  • Radiomics offers a method to extract quantitative tissue characteristics from medical images.

Purpose of the Study:

  • To review the technical foundations of fetal MRI radiomics.
  • To explore clinical applications of fetal MRI radiomics in brain development assessment and outcome prediction.
  • To highlight the potential of radiomics in enhancing prenatal diagnosis and care.

Main Methods:

  • Review of technical aspects: acquisition, preprocessing, segmentation, feature extraction, and machine learning models for fetal MRI radiomics.
  • Analysis of clinical applications including brain development, Chiari II malformation, ventriculomegaly, and neurodevelopmental outcome prediction.
  • Discussion on feature reproducibility, quality, and future directions like deep learning integration.

Main Results:

  • Fetal MRI radiomics provides quantitative insights beyond visual interpretation.
  • Identified applications in assessing brain development, specific malformations, and predicting outcomes.
  • Radiomics shows promise for improved diagnostic accuracy and risk stratification.

Conclusions:

  • Fetal MRI radiomics is an emerging technique with significant potential for fetal brain assessment.
  • Standardized protocols and larger multicenter studies are crucial for generalizability.
  • Integration with deep learning and biological validation will further enhance clinical relevance and confirm findings.