A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

Abstract

Authors

• Andreas Stefan Brendlin
  Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany.
• Felix Peisen
  Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany.
• Haidara Almansour
  From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen.
• Saif Afat
  Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
• Thomas Eigentler
  Center of Dermatooncology, Department of Dermatology, Eberhard Karls Universitat Tubingen, Tubingen, Germany.
• Teresa Amaral
  Center of Dermatooncology, Department of Dermatology, Eberhard Karls Universitat Tubingen, Tubingen, Germany.
• Sebastian Faby
  Siemens Healthcare GmbH, Forchheim, Germany.
• Adria Font Calvarons
  Computed Tomography, Siemens Healthcare GmbH, Erlangen, Germany.
• Konstantin Nikolaou
  Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany.
• Ahmed E Othman
  Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.

Keywords

External Resources