Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.

Journal: European radiology
Published Date: Jun 30, 2020

Abstract

OBJECTIVES: The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI.

Authors

  • Renato Cuocolo
    Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy.
  • Maria Brunella Cipullo
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy.
  • Arnaldo Stanzione
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy.
  • Valeria Romeo
    Department of Advanced Biomedical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Roberta Green
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
  • Valeria Cantoni
    Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy.
  • Andrea Ponsiglione
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
  • Lorenzo Ugga
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80131, Naples, Italy.
  • Massimo Imbriaco
    Department of Advanced Biomedical Sciences, University of Naples "Federico II," Via Pansini 5, 80131 Naples, Italy.

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