Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.

Journal: JCO clinical cancer informatics
PMID: 38588476

Abstract

PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC.

Authors

  • Matthias Jung
    Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany.
  • Thierno D Diallo
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Tobias Scheef
    Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Marco Reisert
    Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Alexander Rau
    Department of Neuroradiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Maximilan F Russe
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Fabian Bamberg
    Department of Diagnostic and Interventional Radiology, University Medical Center Tübingen, Tübingen, Germany.
  • Stefan Fichtner-Feigl
    Klinik für Allgemein- und Viszeralchirurgie, Universitätsklinikum Freiburg, Deutschland.
  • Michael Quante
    Clinic for Internal Medicine II, Gastrointestinal Oncology, University Medical Center of Freiburg, Freiburg, Germany.
  • Jakob Weiss
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.

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