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

• Konstantin Nikolaou,
• Thomas Eigentler,
• Ahmed E Othman,
• Andreas Stefan Brendlin,
• Felix Peisen,
• Haidara Almansour,
• Saif Afat,
• Sebastian Faby,
• Adria Font Calvarons

Affiliations

Konstantin Nikolaou

Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany

Thomas Eigentler

Center of Dermatooncology, Department of Dermatology, Eberhard Karls Universitat Tubingen, Tubingen, Germany

Ahmed E Othman

Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany

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

Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany

Saif Afat

Department of Diagnostic and Interventional Radiology, Universitätsklinikum Tübingen, Tubingen, Germany

Sebastian Faby

Computed Tomography, Siemens Healthcare GmbH, Erlangen, Germany

Adria Font Calvarons

Computed Tomography, Siemens Healthcare GmbH, Erlangen, Germany