Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

Journal: Journal of magnetic resonance imaging : JMRI
Published Date: Jul 1, 2018

Abstract

BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest.

Authors

  • Valeria Romeo
    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.
  • Renato Cuocolo
    Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
  • Mario Petretta
    Department of Translational Medical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Pier Paolo Mainenti
    Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
  • Francesco Verde
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Milena Coppola
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Serena Dell'Aversana
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Arturo Brunetti
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.

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