Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions.

Journal: Magnetic resonance imaging
Published Date: Mar 13, 2021

Abstract

PURPOSE: To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant.

Authors

  • Arnaldo Stanzione
    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.
  • Francesco Verde
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Roberta Galatola
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Italy.
  • Valeria Romeo
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Pier Paolo Mainenti
    Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy.
  • Giovanni Aprea
    Department of Clinical Medicine and Surgery, University of Naples "Federico II", Italy.
  • Elia Guadagno
    Department of Advanced Biomedical Sciences, Pathology Section, University of Naples "Federico II", Naples, Italy.
  • Marialaura Del Basso De Caro
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Italy.
  • Simone Maurea
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.

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