Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

Journal: Academic radiology
Published Date: Aug 1, 2025

Abstract

RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among available treatment options. Clinical attributes of graft survival prediction, such as allocation variables or results of pathological examinations, have been widely studied. Nevertheless, medical imaging is clinically used only to assess current transplant status. This study investigated the use of unsupervised deep learning-based algorithms to identify rich radiomic features that may be linked to graft survival from early dynamic contrast-enhanced magnetic resonance imaging data of renal transplants.

Authors

  • Leo Milecki
  • Sylvain Bodard
    Department of Radiology, University of Paris Cite, Necker Hospital, Paris, France.
  • Vicky Kalogeiton
    LIX, École Polytechnique, CNRS, Institut Polytechnique de Paris, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France (V.K.).
  • Florence Poinard
    Department of Urology and Renal Transplantation, Georges Pompidou European Hospital, APHP, 20 Rue Leblanc, 75015 Paris, France (F.P., M.O.T.).
  • Anne-Marie Tissier
    Department of Adult Radiology, Necker Hospital, APHP, 149 Rue de Sèvres, 75015 Paris, France (S.B., A.M.T., J.M.C.).
  • Idris Boudhabhay
    Department of Nephrology and Kidney Transplantation, Necker Hospital, APHP, 149 Rue de Sèvres, 75015 Paris, France (I.B., D.A.).
  • Jean-Michel Correas
    Department of Adult Radiology, Necker Hospital, APHP, 149 Rue de Sèvres, 75015 Paris, France (S.B., A.M.T., J.M.C.); UFR Médecine, Paris-Cité University, 15 Rue de l'Ecole de Médecine, 75006 Paris, France (J.M.C., D.A., M.O.T.).
  • Dany Anglicheau
    Department of Nephrology and Kidney Transplantation, Necker Hospital, APHP, 149 Rue de Sèvres, 75015 Paris, France (I.B., D.A.); UFR Médecine, Paris-Cité University, 15 Rue de l'Ecole de Médecine, 75006 Paris, France (J.M.C., D.A., M.O.T.).
  • Maria Vakalopoulou
    Ecole CentraleSupelec, 91190, Gif-sur-Yvette, France.
  • Marc-Olivier Timsit
    Department of Urology, Hôpital Européen Georges Pompidou and Necker Hospital, Paris, France.

Keywords

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