A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging.

Journal: European radiology experimental
Published Date: Oct 17, 2019

Abstract

BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC).

Authors

  • Georgios Kaissis
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Sebastian Ziegelmayer
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Fabian Lohöfer
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Hana Algül
    Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.
  • Matthias Eiber
    Department of Urology, Technical University of Munich, Rechts der Isar Medical Center, Munich, Germany.
  • Wilko Weichert
    Department of Pathology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Roland Schmid
    Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.
  • Helmut Friess
    Department of Surgery, School of Medicine, Technical University of Munich, Munich, Germany.
  • Ernst Rummeny
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Donna Ankerst
    Department of Mathematics, Technical University of Munich, Garching, Germany.
  • Jens Siveke
    West German Cancer Center, University of Essen, Essen, Germany.
  • Rickmer Braren
    Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany.

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