Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

Journal: Investigative radiology
Published Date:

Abstract

OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone.

Authors

  • Markus Wennmann
    From the Division of Radiology, German Cancer Research Center (DKFZ).
  • Peter Neher
    Division of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, 69120 Heidelberg, Germany. Electronic address: p.neher@dkfz.de.
  • Nikolas Stanczyk
  • Kim-Celine Kahl
    Medical Image Computing, German Cancer Research Center (DKFZ).
  • Jessica Kächele
    Medical Image Computing, German Cancer Research Center (DKFZ).
  • Vivienn Weru
    Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Thomas Hielscher
    Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Martin Grözinger
    From the Divisions of Radiology.
  • Jiri Chmelik
    Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno University of Technology, Brno, Technicka 3082/12, 616 00, Czechia. Electronic address: chmelikj@feec.vutbr.cz.
  • Kevin Sun Zhang
    Department of Radiology, German Cancer Research Centre, Heidelberg, Germany.
  • Fabian Bauer
  • Tobias Nonnenmacher
    Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg.
  • Manuel Debic
    Diagnostic and Interventional Radiology.
  • Sandra Sauer
    Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg.
  • Lukas Thomas Rotkopf
    From the Division of Radiology, German Cancer Research Center (DKFZ).
  • Anna Jauch
    Institute of Human Genetics.
  • Kai Schlamp
  • Elias Karl Mai
    Department of Internal Medicine V, Section Multiple Myeloma.
  • Niels Weinhold
    Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg.
  • Saif Afat
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Marius Horger
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, Hoppe-Seyler-Str.3, 72076 Tübingen, Germany. Electronic address: marius.horger@med.uni-tuebingen.de.
  • Hartmut Goldschmidt
  • Heinz-Peter Schlemmer
    From the Department of Radiology (D.B., P.S., J.P.R., P.K., K.Y., M.F., H.P.S.), Division of Medical Image Computing (S.K., M.G., N.G., K.H.M.H.), Division of Statistics (M.W.), and Department of Medical Physics (T.A.K., F.D.), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg 69120, Germany; German Cancer Consortium (DKTK), Heidelberg, Germany (D.B., H.P.S., K.H.M.H.); and Departments of Urology (J.P.R., B.H., M.H., B.A.H.) and Neuroradiology (P.K.), University of Heidelberg Medical Center, Heidelberg, Germany.
  • Tim Frederik Weber
    Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg.
  • Stefan Delorme
    From the Divisions of Radiology.
  • Felix Tobias Kurz
    From the Divisions of Radiology.
  • Klaus Maier-Hein
    Medical Image Analysis, Division Medical Image Computing, DKFZ Heidelberg, Germany.