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A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients

Author

Listed:
  • Daniel Yoo

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration)

  • Gillian Divard

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Saint-Louis Hospital, Assistance Publique – Hôpitaux de Paris)

  • Marc Raynaud

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration)

  • Aaron Cohen

    (OneLegacy)

  • Tom D. Mone

    (OneLegacy)

  • John Thomas Rosenthal

    (David Geffen School of Medicine at UCLA)

  • Andrew J. Bentall

    (Mayo Clinic Transplant Center)

  • Mark D. Stegall

    (Department of Surgery, Mayo Clinic)

  • Maarten Naesens

    (Immunology and Transplantation, KU Leuven)

  • Huanxi Zhang

    (Sun Yat-sen University, Guangzhou)

  • Changxi Wang

    (Sun Yat-sen University, Guangzhou)

  • Juliette Gueguen

    (Néphrologie-Immunologie Clinique, Hôpital Bretonneau, CHU Tours)

  • Nassim Kamar

    (Paul Sabatier University, INSERM)

  • Antoine Bouquegneau

    (Centre hospitalier universitaire de Liège)

  • Ibrahim Batal

    (Columbia University Medical Center)

  • Shana M. Coley

    (Columbia University Medical Center)

  • John S. Gill

    (University of British Columbia)

  • Federico Oppenheimer

    (Hospital Clínic i Provincial de Barcelona)

  • Erika De Sousa-Amorim

    (Hospital Clínic i Provincial de Barcelona)

  • Dirk R. J. Kuypers

    (Immunology and Transplantation, KU Leuven)

  • Antoine Durrbach

    (AP-HP Hôpital Henri Mondor)

  • Daniel Seron

    (Autonomous University of Barcelona)

  • Marion Rabant

    (Assistance Publique - Hôpitaux de Paris)

  • Jean-Paul Duong Van Huyen

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Assistance Publique - Hôpitaux de Paris)

  • Patricia Campbell

    (University of Alberta)

  • Soroush Shojai

    (University of Alberta)

  • Michael Mengel

    (University of Alberta)

  • Oriol Bestard

    (Autonomous University of Barcelona)

  • Nikolina Basic-Jukic

    (University Hospital Centre Zagreb)

  • Ivana Jurić

    (University Hospital Centre Zagreb)

  • Peter Boor

    (RWTH Aachen University Hospital)

  • Lynn D. Cornell

    (Mayo Clinic)

  • Mariam P. Alexander

    (Mayo Clinic)

  • P. Toby Coates

    (University of Adelaide, Royal Adelaide Hospital Campus)

  • Christophe Legendre

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Assistance Publique - Hôpitaux de Paris)

  • Peter P. Reese

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    University of Pennsylvania)

  • Carmen Lefaucheur

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Saint-Louis Hospital, Assistance Publique – Hôpitaux de Paris)

  • Olivier Aubert

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Assistance Publique - Hôpitaux de Paris)

  • Alexandre Loupy

    (Université Paris Cité, INSERM U970 PARCC, Paris Institute for Transplantation and Organ Regeneration
    Assistance Publique - Hôpitaux de Paris)

Abstract

In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.

Suggested Citation

  • Daniel Yoo & Gillian Divard & Marc Raynaud & Aaron Cohen & Tom D. Mone & John Thomas Rosenthal & Andrew J. Bentall & Mark D. Stegall & Maarten Naesens & Huanxi Zhang & Changxi Wang & Juliette Gueguen , 2024. "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44595-z
    DOI: 10.1038/s41467-023-44595-z
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    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
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