Follow-up of liver metastases: a comparison of deep learning and RECIST 1.1.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To compare liver metastases changes in CT assessed by radiologists using RECIST 1.1 and with aided simultaneous deep learning-based volumetric lesion changes analysis.

Authors

  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Adi Szeskin
    The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9190401, Israel.
  • Shalom Rochman
    The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 9190401, Israel.
  • Aviv Dodi
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Richard Lederman
    Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
  • Hila Fruchtman-Brot
    Dept of Radiology, Hadassah Medical Center, Faculty of Medicine, The Hebrew University of Jerusalem, POB 12000, 91120, Jerusalem, Israel.
  • Yusef Azraq
    Department of Diagnostic Imaging, Department of Radiology, Penn State Health Milton S Hershey Medical Center, Penn State University Hospital, 500 University Dr, Hershey, PA 17033-0850 (E.S.); Department of Diagnostic Imaging, Hadassah Hebrew University Medical Center, Jerusalem, Israel, Affiliated with the Hebrew University Medical School, Jerusalem, Israel (Y.A., J.M.G., S.F., T.S.); and Department of Diagnostic Imaging, Edith Wolfson Medical Center, Holon, Israel, Affiliated with the Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (T.A.K.).
  • Jacob Sosna
    Department of Radiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.