Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.

Journal: European journal of cancer (Oxford, England : 1990)
PMID: 37890350

Abstract

BACKGROUND: Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images.

Authors

  • Frederik Marmé
    Department of Obstetrics and Gynaecology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany.
  • Eva Krieghoff-Henning
    Digital Biomarkers for Oncology Group (DBO), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Bernd Gerber
    Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany.
  • Max Schmitt
    National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
  • Dirk-Michael Zahm
    Department of Gynecology, SRH Waldklinikum Gera GmbH, Gera, Germany.
  • Dirk Bauerschlag
    Department of Gynecology and Obstetrics, University Medical Center Schleswig-Holstein (UKSH), Campus Kiel, Kiel, Germany.
  • Helmut Forstbauer
    GOSPL-Gesellschaft für onkologische Studien, Troisdorf, Germany.
  • Guido Hildebrandt
    Universitätsklinikum Rostock, Klinik für Strahlentherapie, Rostock, Germany.
  • Beyhan Ataseven
    Department of Gynecology, Gynecologic Oncology and Obstetrics, Klinikum Lippe, Bielefeld University, Medical School and University Medical Center East Westphalia-Lippe, Bielefeld, Germany.
  • Tobias Brodkorb
    Department of Obstetrics and Gynaecology, Medical Faculty Mannheim of the University of Heidelberg, Heidelberg, Germany.
  • Carsten Denkert
    Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.
  • Angrit Stachs
    Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany.
  • David Krug
    Klinik für Strahlentherapie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany.
  • Jörg Heil
    Instituto Universitario de Lisboa (ISCTE), Lisboa, Portugal.
  • Michael Golatta
    University Breast Unit, Department of Obstetrics and Gynecology, Heidelberg University Hospital, Im Neuenheimer Feld 440, 69120, Heidelberg, Germany. Michael.golatta@med.uni-heidelberg.de.
  • Thorsten Kühn
    Department of Gynaecology and Obstetrics, Klinikum Esslingen, Neckar, Germany.
  • Valentina Nekljudova
    German Breast Group, GBG Forschungs GmbH, Neu-Isenburg, Germany.
  • Timo Gaiser
    Institute of Pathology, University Medical Center Mannheim, Mannheim, Germany.
  • Rebecca Schönmehl
    Institute of Pathology, University Medical Centre Mannheim, Heidelberg University, 68167 Mannheim, Germany.
  • Christoph Brochhausen
    Institute of Pathology & Central Biobank, University and University Clinic of Regensburg, Regensburg, Germany.
  • Sibylle Loibl
    German Breast Group, GBG Forschungs GmbH, Neu-Isenburg, Germany.
  • Toralf Reimer
    Department of Obstetrics and Gynecology, University Hospital of Rostock, Rostock, Germany.
  • Titus J Brinker
    National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.

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