Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa.

Journal: Magnetic resonance imaging
PMID: 31102613

Abstract

PURPOSE: To evaluate whether a machine learning (ML) analysis employing MRI-derived texture analysis (TA) features could be useful in assessing the presence of placenta accreta spectrum (PAS) in patients with placenta previa (PP). The hypothesis is that TA features may reflect histological abnormalities underlying PAS in patients with PP thus helping in differentiating positive from negative cases.

Authors

  • Valeria Romeo
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Carlo Ricciardi
    Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy.
  • Renato Cuocolo
    Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
  • Arnaldo Stanzione
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
  • Francesco Verde
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Laura Sarno
    Department of Neurosciences and Reproductive and Dentistry Sciences, University of Naples Federico II, Naples, Italy.
  • Giovanni Improta
    Department of Public Health, University of Naples "Federico II", Naples, Italy.
  • Pier Paolo Mainenti
    Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
  • Maria D'Armiento
    University of Naples "Federico II", Department of Advanced Biomedical Sciences, Naples, Italy.
  • Arturo Brunetti
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Simone Maurea
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.

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