Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

Journal: JCO clinical cancer informatics
Published Date: Aug 1, 2023

Abstract

PURPOSE: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-computed tomography (PET/CT)-based therapy response evaluation. Our objective was to investigate if deep learning (DL) algorithms are capable of predicting the therapy response of patients with GEJ adenocarcinoma to neoadjuvant chemotherapy on the basis of histologic tissue samples.

Authors

  • Fabian Horst
    Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Rhineland-Palatinate, Germany.
  • Saskia Ting
    Institute of Pathology Essen, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany.
  • Sven-Thorsten Liffers
    Bridge Institute of Experimental Tumor Therapy, West German Cancer Center Essen, University Hospital Essen (AöR), Essen, Germany.
  • Kelsey L Pomykala
    Institute for AI in Medicine (IKIM), University Medicine Essen, Girardetstraße 2, 45131 Essen, Germany.
  • Katja Steiger
    Department of Pathology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Markus Albertsmeier
    Department of General, Visceral and Transplantation Surgery, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany.
  • Martin K Angele
    Department of General, Visceral and Transplantation Surgery, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany.
  • Sylvie Lorenzen
    Clinic for Internal Medicine III, University Hospital rechts der Isar, Technical University of Munich (TUM), Munich, Germany.
  • Michael Quante
    Clinic for Internal Medicine II, Gastrointestinal Oncology, University Medical Center of Freiburg, Freiburg, Germany.
  • Wilko Weichert
    Department of Pathology, School of Medicine, Technical University of Munich, Munich, Germany.
  • Jan Egger
    Institute for Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria.
  • Jens T Siveke
    Division of Solid Tumor Translational Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany.
  • Jens Kleesiek
    AG Computational Radiology, Abteilung Radiologie, Deutsches Krebsforschungszentrum (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Deutschland. j.kleesiek@dkfz-heidelberg.de.

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