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

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; Nassim Kamar; Antoine Bouquegneau; Ibrahim Batal; Shana M. Coley; John S. Gill; Federico Oppenheimer; Erika De Sousa-Amorim; Dirk R.J. Kuypers; Antoine Durrbach; Daniel Seron; Marion Rabant; Jean Paul Duong Van Huyen; Patricia Campbell; Soroush Shojai; Michael Mengel; Oriol Bestard; Nikolina Basic-Jukic; Ivana Jurić; Peter Boor; Lynn D. Cornell; Mariam P. Alexander; P. Toby Coates; Christophe Legendre; Peter P. Reese; Carmen Lefaucheur; Olivier Aubert; Alexandre Loupy

doi:10.1038/s41467-023-44595-z

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

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, Nassim Kamar, Antoine Bouquegneau, Ibrahim Batal, Shana M. Coley, John S. Gill, Federico Oppenheimer, Erika De Sousa-Amorim, Dirk R.J. KuypersAntoine Durrbach, Daniel Seron, Marion Rabant, Jean Paul Duong Van Huyen, Patricia Campbell, Soroush Shojai, Michael Mengel, Oriol Bestard, Nikolina Basic-Jukic, Ivana Jurić, Peter Boor, Lynn D. Cornell, Mariam P. Alexander, P. Toby Coates, Christophe Legendre, Peter P. Reese, Carmen Lefaucheur, Olivier Aubert, Alexandre Loupy

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	554
Journal	Nature communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41467-023-44595-z
State	Published - Dec 2024

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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Yoo, D., Divard, G., Raynaud, M., Cohen, A., Mone, T. D., Rosenthal, J. T., Bentall, A. J., Stegall, M. D., Naesens, M., Zhang, H., Wang, C., Gueguen, J., Kamar, N., Bouquegneau, A., Batal, I., Coley, S. M., Gill, J. S., Oppenheimer, F., De Sousa-Amorim, E., ... Loupy, A. (2024). A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. Nature communications, 15(1), Article 554. https://doi.org/10.1038/s41467-023-44595-z

Yoo, D, Divard, G, Raynaud, M, Cohen, A, Mone, TD, Rosenthal, JT, Bentall, AJ , Stegall, MD, Naesens, M, Zhang, H, Wang, C, Gueguen, J, Kamar, N, Bouquegneau, A, Batal, I, Coley, SM, Gill, JS, Oppenheimer, F, De Sousa-Amorim, E, Kuypers, DRJ, Durrbach, A, Seron, D, Rabant, M, Van Huyen, JPD, Campbell, P, Shojai, S, Mengel, M, Bestard, O, Basic-Jukic, N, Jurić, I, Boor, P, Cornell, LD, Alexander, MP, Toby Coates, P, Legendre, C, Reese, PP, Lefaucheur, C, Aubert, O & Loupy, A 2024, 'A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients', Nature communications, vol. 15, no. 1, 554. https://doi.org/10.1038/s41467-023-44595-z

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title = "A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients",

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.",

author = "Daniel Yoo and Gillian Divard and Marc Raynaud and Aaron Cohen and Mone, {Tom D.} and Rosenthal, {John Thomas} and Bentall, {Andrew J.} and Stegall, {Mark D.} and Maarten Naesens and Huanxi Zhang and Changxi Wang and Juliette Gueguen and Nassim Kamar and Antoine Bouquegneau and Ibrahim Batal and Coley, {Shana M.} and Gill, {John S.} and Federico Oppenheimer and {De Sousa-Amorim}, Erika and Kuypers, {Dirk R.J.} and Antoine Durrbach and Daniel Seron and Marion Rabant and {Van Huyen}, {Jean Paul Duong} and Patricia Campbell and Soroush Shojai and Michael Mengel and Oriol Bestard and Nikolina Basic-Jukic and Ivana Juri{\'c} and Peter Boor and Cornell, {Lynn D.} and Alexander, {Mariam P.} and {Toby Coates}, P. and Christophe Legendre and Reese, {Peter P.} and Carmen Lefaucheur and Olivier Aubert and Alexandre Loupy",

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doi = "10.1038/s41467-023-44595-z",

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AU - Yoo, Daniel

AU - Divard, Gillian

AU - Raynaud, Marc

AU - Cohen, Aaron

AU - Mone, Tom D.

AU - Rosenthal, John Thomas

AU - Bentall, Andrew J.

AU - Stegall, Mark D.

AU - Naesens, Maarten

AU - Zhang, Huanxi

AU - Wang, Changxi

AU - Gueguen, Juliette

AU - Kamar, Nassim

AU - Bouquegneau, Antoine

AU - Batal, Ibrahim

AU - Coley, Shana M.

AU - Gill, John S.

AU - Oppenheimer, Federico

AU - De Sousa-Amorim, Erika

AU - Kuypers, Dirk R.J.

AU - Durrbach, Antoine

AU - Seron, Daniel

AU - Rabant, Marion

AU - Van Huyen, Jean Paul Duong

AU - Campbell, Patricia

AU - Shojai, Soroush

AU - Mengel, Michael

AU - Bestard, Oriol

AU - Basic-Jukic, Nikolina

AU - Jurić, Ivana

AU - Boor, Peter

AU - Cornell, Lynn D.

AU - Alexander, Mariam P.

AU - Toby Coates, P.

AU - Legendre, Christophe

AU - Reese, Peter P.

AU - Lefaucheur, Carmen

AU - Aubert, Olivier

AU - Loupy, Alexandre

PY - 2024/12

Y1 - 2024/12

N2 - 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.

AB - 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.

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ER -

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

Abstract

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