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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 Više autora...

Institucijski repozitorij: Dr Med - digitalni repozitorij Medicinskog fakulteta Sveučilišta u Zagrebu

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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, br. 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, br. 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, i sur.. A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nature Communications [Internet]. 16.01.2024. [pristupljeno 16.01.2025.];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, br. 1, Siječanj 2024. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:105:006945. [Citirano: 16.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:105:006945
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Naslov (engleski)A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients
AutorDaniel Yoo
AutorGillian Divard
AutorMarc Raynaud
AutorAaron Cohen
AutorTom D. Mone
AutorJohn Thomas Rosenthal
AutorAndrew J. Bentall
AutorMark D. Stegall
AutorMaarten Naesens
AutorHuanxi Zhang
AutorChangxi Wang
AutorJuliette Gueguen
AutorNassim Kamar
AutorAntoine Bouquegneau
AutorIbrahim Batal
AutorShana M. Coley
AutorJohn S. Gill
AutorFederico Oppenheimer
AutorErika De Sousa-Amorim
AutorDirk R. J. Kuypers
AutorAntoine Durrbach
AutorDaniel Seron
AutorMarion Rabant
AutorJean-Paul Duong Van Huyen
AutorPatricia Campbell
AutorSoroush Shojai
AutorMichael Mengel
AutorOriol Bestard
AutorNikolina Bašić-Jukić
AutorIvana Jurić
AutorPeter Boor
AutorLynn D. Cornell
AutorMariam P. Alexander
AutorP. Toby Coates
AutorChristophe Legendre
AutorPeter P. Reese
AutorCarmen Lefaucheur
AutorOlivier Aubert
AutorAlexandre Loupy
Autorova ustanovaSveučilište u Zagrebu
Medicinski fakultet
(KATEDRA ZA INTERNU MEDICINU)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Interna medicina
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaNature Communications
Brojčani podacivol. 15, br. 1, 554
e-ISSN2041-1723
DOIhttps://doi.org/10.1038/s41467-023-44595-z
URN:NBNurn:nbn:hr:105:006945
Datum objave publikacije2024-01-16
URL dokumentahttps://www.nature.com/articles/s41467-023-44595-z
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-11 13:54:42