Deep learning trained on lymph node status predicts outcome from gastric cancer histopathology: a retrospective multicentric study.

Journal: European journal of cancer (Oxford, England : 1990)
Published Date:

Abstract

AIM: Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL).

Authors

  • Hannah S Muti
    Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus Dresden, Dresden, Germany.
  • Christoph Röcken
    Department of Pathology, Christian-Albrechts University, Kiel, Germany.
  • Hans-Michael Behrens
    Department of Pathology, Christian-Albrechts University, Kiel, Germany.
  • Chiara M L Löffler
    Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany.
  • Nic G Reitsam
    Pathology, Faculty of Medicine, University of Augsburg, Augsburg, Germany.
  • Bianca Grosser
    Institute of Pathology and Molecular Diagnostics, University Hospital Augsburg, Augsburg, Germany.
  • Bruno Märkl
    Department of Pathology, University Hospital of Augsburg, Augsburg, Germany.
  • Daniel E Stange
    Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; National Center for Tumor Diseases (NCT/UCC), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany.
  • Xiaofeng Jiang
    Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Gregory P Velduizen
    Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
  • Daniel Truhn
    Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany (J.S., D.B.A., S.N.); Institute of Computer Vision and Imaging, RWTH University Aachen, Pauwelsstrasse 30, 52072 Aachen, Germany (J.S., D.M.); Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (D.T., M.P., F.M., C.K., S.N.); and Faculty of Mathematics and Natural Sciences, Institute of Informatics, Heinrich Heine University Düsseldorf, Düsseldorf, Germany (S.C.).
  • Matthias P Ebert
    Department of Medicine II, Mannheim Institute for Innate Immunoscience and Clinical Cooperation Unit Healthy Metabolism, Center of Preventive Medicine and Digital Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
  • Heike I Grabsch
    Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, Netherlands.
  • Jakob N Kather
    Department of Gastroenterology, University Hospital RWTH Aachen, Aachen, Germany. jakob.kather@gmail.com.