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Scientific paper - Original scientific paper
A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients
Nature Communications, 15 (2024), 1; 554. https://doi.org/10.1038/s41467-023-44595-z
Yoo, Daniel; Divard, Gillian; Raynaud, Marc; Cohen, Aaron; Mone, Tom D.; Rosenthal, John Thomas; Bentall, Andrew J.; Stegall, Mark D.; Naesens, Maarten; Zhang, Huanxi; Wang, Changxi; Gueguen, Juliette; Kamar, Nassim; Bouquegneau, Antoine; Batal, Ibrahim; Coley, Shana M.; Gill, John S.; Oppenheimer, Federico; De Sousa-Amorim, Erika; Kuypers, Dirk R. J.; Durrbach, Antoine; Seron, Daniel; Rabant, Marion; Van Huyen, Jean-Paul Duong; Campbell, Patricia; Shojai, Soroush; Mengel, Michael; Bestard, Oriol; Bašić-Jukić, Nikolina; Jurić, Ivana; Boor, Peter; Cornell, Lynn D.; Alexander, Mariam P.; Toby Coates, P.; Legendre, Christophe; Reese, Peter P.; Lefaucheur, Carmen; Aubert, Olivier; Loupy, Alexandre More authors...

Cite this document

Yoo, D., Divard, G., Raynaud, M., Cohen, A., Mone, T. D., Rosenthal, J. T. ... Loupy, A. (2024). A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nature Communications, 15. (1). doi: 10.1038/s41467-023-44595-z

Yoo, Daniel, et al. "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients." Nature Communications, vol. 15, no. 1, 2024. https://doi.org/10.1038/s41467-023-44595-z

Yoo, Daniel, Gillian Divard, Marc Raynaud, Aaron Cohen, Tom D. Mone, John Thomas Rosenthal, Andrew J. Bentall, et al. "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients." Nature Communications 15, no. 1 (2024). https://doi.org/10.1038/s41467-023-44595-z

Yoo, D., et al. (2024) 'A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients', Nature Communications, 15(1). doi: 10.1038/s41467-023-44595-z

Yoo D, Divard G, Raynaud M, Cohen A, Mone TD, Rosenthal JT, and sur.. A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nature Communications [Internet]. 2024 January 16 [cited 2025 January 12];15(1). doi: 10.1038/s41467-023-44595-z

D. Yoo, et al., "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients", Nature Communications, vol. 15, no. 1, January 2024. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:105:006945. [Accessed: 12 January 2025]

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Metadata
Title (english)A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients
AuthorDaniel Yoo
AuthorGillian Divard
AuthorMarc Raynaud
AuthorAaron Cohen
AuthorTom D. Mone
AuthorJohn Thomas Rosenthal
AuthorAndrew J. Bentall
AuthorMark D. Stegall
AuthorMaarten Naesens
AuthorHuanxi Zhang
AuthorChangxi Wang
AuthorJuliette Gueguen
AuthorNassim Kamar
AuthorAntoine Bouquegneau
AuthorIbrahim Batal
AuthorShana M. Coley
AuthorJohn S. Gill
AuthorFederico Oppenheimer
AuthorErika De Sousa-Amorim
AuthorDirk R. J. Kuypers
AuthorAntoine Durrbach
AuthorDaniel Seron
AuthorMarion Rabant
AuthorJean-Paul Duong Van Huyen
AuthorPatricia Campbell
AuthorSoroush Shojai
AuthorMichael Mengel
AuthorOriol Bestard
AuthorNikolina Bašić-Jukić
AuthorIvana Jurić
AuthorPeter Boor
AuthorLynn D. Cornell
AuthorMariam P. Alexander
AuthorP. Toby Coates
AuthorChristophe Legendre
AuthorPeter P. Reese
AuthorCarmen Lefaucheur
AuthorOlivier Aubert
AuthorAlexandre Loupy
Author's institutionUniversity of Zagreb
School of Medicine
(Department of Internal Medicine)
Scientific / art field,
discipline and subdiscipline		BIOMEDICINE AND HEALTHCARE
Clinical Medical Sciences
Internal Medicine
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review
Publication versionPublished version
Journal titleNature Communications
Numberingvol. 15, no. 1, 554
e-ISSN2041-1723
DOIhttps://doi.org/10.1038/s41467-023-44595-z
URN:NBNurn:nbn:hr:105:006945
Publication2024-01-16
Document URLhttps://www.nature.com/articles/s41467-023-44595-z
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-11 13:54:42