Diagnostic accuracy of texture analysis applied to T- and T-Relaxation maps for liver fibrosis classification via machine-learning algorithms with liver histology as reference standard.

Journal: European journal of radiology
PMID: 39721334

Abstract

OBJECTIVES: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.

Authors

  • Diana Sitarcikova
    High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Sarah Poetter-Lang
    High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Nina Bastati
    Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Sami Ba-Ssalamah
    High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Siegfried Trattnig
    High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Ulrike Attenberger
    Universitätsklinik für Radiologie, Universitätsklinikum Bonn, Bonn, Deutschland.
  • Ahmed Ba-Ssalamah
    Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
  • Martin Krššák
    Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Austria.

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