IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-023-44595-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-44595-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-44595-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    2. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2023. "Business cycle and realized losses in the consumer credit industry," LIDAM Discussion Papers LFIN 2023007, Université catholique de Louvain, Louvain Finance (LFIN).
    3. Filgueiras, Roberto & Almeida, Thomé Simpliciano & Mantovani, Everardo Chartuni & Dias, Santos Henrique Brant & Fernandes-Filho, Elpídio Inácio & da Cunha, Fernando França & Venancio, Luan Peroni, 2020. "Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data," Agricultural Water Management, Elsevier, vol. 241(C).
    4. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    5. Somodi, Imelda & Bede-Fazekas, Ákos & Botta-Dukát, Zoltán & Molnár, Zsolt, 2024. "Confidence and consistency in discrimination: A new family of evaluation metrics for potential distribution models," Ecological Modelling, Elsevier, vol. 491(C).
    6. Rachel Sippy & Daniel F Farrell & Daniel A Lichtenstein & Ryan Nightingale & Megan A Harris & Joseph Toth & Paris Hantztidiamantis & Nicholas Usher & Cinthya Cueva Aponte & Julio Barzallo Aguilar & An, 2020. "Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(2), pages 1-20, February.
    7. Miten Mistry & Dimitrios Letsios & Gerhard Krennrich & Robert M. Lee & Ruth Misener, 2021. "Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 1103-1119, July.
    8. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
    9. Sharan Srinivas, 2020. "A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    10. Magboul M. Sulieman & Fuat Kaya & Mohammed A. Elsheikh & Levent Başayiğit & Rosa Francaviglia, 2023. "Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions," Land, MDPI, vol. 12(9), pages 1-22, August.
    11. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    12. Mansoor, Umer & Jamal, Arshad & Su, Junbiao & Sze, N.N. & Chen, Anthony, 2023. "Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations," Transport Policy, Elsevier, vol. 139(C), pages 21-38.
    13. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    14. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    15. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    16. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    17. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    18. Banks, Jonathan & Rabbani, Arif & Nadkarni, Kabir & Renaud, Evan, 2020. "Estimating parasitic loads related to brine production from a hot sedimentary aquifer geothermal project: A case study from the Clarke Lake gas field, British Columbia," Renewable Energy, Elsevier, vol. 153(C), pages 539-552.
    19. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    20. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-023-44595-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.