Differentiation of Malignancy and Idiopathic Granulomatous Mastitis Presenting as Non-mass Lesions on MRI: Radiological, Clinical, Radiomics, and Clinical-Radiomics Models.

Journal: Academic radiology
PMID: 38641449

Abstract

RATIONALE AND OBJECTIVES: To investigate the effectiveness of machine learning-based clinical, radiomics, and combined models in differentiating idiopathic granulomatous mastitis (IGM) from malignancy, both presenting as non-mass enhancement (NME) lesions on magnetic resonance imaging (MRI), and to compare these models with radiological evaluation.

Authors

  • Yasemin Kayadibi
    Department of Radiology, Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Kocamustafapasa, Istanbul, Turkey. Electronic address: ysmnkayadibi@gmail.com.
  • Mehmet Sakıpcan Saracoglu
    Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye.
  • Seda Aladag Kurt
    Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye.
  • Enes Deger
    Istanbul University-Cerrahpasa, Cerrahpasa Medical Faculty, Department of Radiology, Kocamustafapasa, Istanbul, Türkiye.
  • Fatma Nur Soylu Boy
    Fatih Sultan Mehmet Education and Research Hospital, Department of Radiology, Atasehir, Istanbul, Türkiye.
  • Nese Ucar
    Department of Radiology, Gaziosmanspasa Education and Research Hospital,Gaziosmanpasa, Istanbul, Turkey. Electronic address: neseyigit@hotmail.com.
  • Gul Esen Icten
    Acibadem M.A.A. University Senology Research Institute, 34457, Sarıyer, Istanbul, Turkey (F.T., G.E.I., U.T.P.); Department of Radiology, Acibadem M.A.A. University School of Medicine, Acıbadem Maslak Hospital, Büyükdere St. 40, 34457, Maslak, Istanbul, Turkey (G.E.I.).

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