Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.

Journal: European radiology
PMID: 32055950

Abstract

INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).

Authors

  • Paul Blanc-Durand
    Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France.
  • Luca Campedel
    Department of Oncology, Groupe Hospitalier Pitié Salpêtrière C. Foix/AP-HP, Paris, F-75013, France.
  • Sébastien Mulé
    Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France.
  • Simon Jégou
    Owkin, 75011, Paris, France.
  • Alain Luciani
    Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France.
  • Frédéric Pigneur
    Department of Radiology, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France.
  • Emmanuel Itti
    Department of Nuclear Medicine, Henri Mondor Hospital/AP-HP, Créteil, F-94010, France.

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