Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).

Authors

  • Annemarie Uhlig
    2 Department of Urology, University Medical Center Goettingen, Goettingen, Germany.
  • Johannes Uhlig
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Andreas Leha
    Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, 37073 Göttingen, Germany.
  • Lorenz Biggemann
    Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany.
  • Sophie Bachanek
    Department of Clinical and Interventional Radiology, University Medical Center Goettingen, Goettingen, Germany.
  • Michael Stöckle
    University of the Saarland, Homburg Saar, Germany.
  • Mathias Reichert
    Department of Urology, University Medical Center Goettingen, Goettingen, Germany.
  • Joachim Lotz
    1 Department of Diagnostic and Interventional Radiology, University Medical Center Goettingen, Robert Koch Strasse 40, Goettingen 37075, Germany.
  • Philip Zeuschner
    Department of Urology and Pediatric Urology, Saarland University, Homburg/Saar, Germany.
  • Alexander Maßmann
    Department of Radiology and Nuclear Medicine, Robert-Bosch-Clinic, Stuttgart, Germany.