Bibliographic Details
| Title: |
A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients. |
| Authors: |
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. |
| Source: |
Nature Communications; 1/16/2024, Vol. 15 Issue 1, p1-12, 12p |
| Subject Terms: |
DEAD, RENAL biopsy, VIRTUAL machine systems, MACHINE learning, KIDNEYS, KIDNEY transplantation |
| Geographic Terms: |
BANFF (Alta.) |
| 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. Despite being recommended, day-zero biopsies are often not performed, due to the cost and time. Here, the authors show that machine learning and donor's basic parameters can predict the biopsy, offering a reliable virtual estimation of the day-zero biopsy findings. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
