Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population.

Journal: EBioMedicine
PMID: 39622188

Abstract

BACKGROUND: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population.

Authors

  • Matthias Jung
    Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, Siegen, Germany.
  • Vineet K Raghu
    Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.
  • Marco Reisert
    Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Hanna Rieder
    Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Susanne Rospleszcz
    Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
  • Tobias Pischon
    Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany.
  • Thoralf Niendorf
    Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
  • Hans-Ulrich Kauczor
    Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
  • Henry Völzke
    Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
  • Robin Bülow
    Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany.
  • Maximilian F Russe
    From the Department of Radiology (C.E.v.S., V.S.S., Y.L., F.G.G., S.C.F., F.T.G., M.R.M., K.W., A.S.G.), Department for Orthopedics and Orthopedic Sports Medicine (N.J.W., C.K., R.v.E., R.B.), and Institute of Pathology (C.M.), Klinikum Rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany; and the Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany (M.J., P.M.J., M.F.R.).
  • Christopher L Schlett
    From the MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, 68 Varosmajor St, 1122 Budapest, Hungary (M.K., J.K., B.M., P.M.H.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K., Y.K., A.I., M.T.L., B.F., H.J.A., U.H.); Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Hokkaido, Japan (Y.K.); Department for Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Freiburg, Germany (C.L.S.); and Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (H.J.A.).
  • Michael T Lu
    Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston.
  • Fabian Bamberg
    Department of Diagnostic and Interventional Radiology, University Medical Center Tübingen, Tübingen, Germany.
  • Jakob Weiss
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.

Keywords

External Resources

Popular Topics
Recent Journals