Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering Article Swipe
Thibaut Vaulet
,
Gillian Divard
,
Olivier Thaunat
,
Evelyne Lerut
,
Aleksandar Senev
,
Olivier Aubert
,
Elisabet Van Loon
,
Jasper Callemeyn
,
Marie‐Paule Emonds
,
Amaryllis H. Van Craenenbroeck
,
Katrien De Vusser
,
Ben Sprangers
,
Maud Rabeyrin
,
Valérie Dubois
,
Dirk Kuypers
,
Maarten De Vos
,
Alexandre Loupy
,
Bart De Moor
,
Maarten Naesens
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1681/asn.2020101418
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1681/asn.2020101418
Significance Statement The current Banff classification of kidney transplant rejection is on the basis of complex and discretionary combinations of histologic scores. As a purely empiric classification, it was not primarily developed to reflect clinically meaningful outcomes such as graft failure, and allows ambiguous phenotypes to overlap. This paper describes the use of data-driven clustering methods to produce a phenotypic reclassification of kidney transplant rejection that is both histologically and clinically relevant. Six novel cluster phenotypes are validated on external data. Each of these new phenotypes is significantly associated with graft failure and overcomes the current limitations of intermediate and mixed phenotypes. The data-driven phenotypic reclassification of kidney transplant rejection is a proof of concept, opening future research directions. Background Over the past decades, an international group of experts iteratively developed a consensus classification of kidney transplant rejection phenotypes, known as the Banff classification. Data-driven clustering of kidney transplant histologic data could simplify the complex and discretionary rules of the Banff classification, while improving the association with graft failure. Methods The data consisted of a training set of 3510 kidney-transplant biopsies from an observational cohort of 936 recipients. Independent validation of the results was performed on an external set of 3835 biopsies from 1989 patients. On the basis of acute histologic lesion scores and the presence of donor-specific HLA antibodies, stable clustering was achieved on the basis of a consensus of 400 different clustering partitions. Additional information on kidney-transplant failure was introduced with a weighted Euclidean distance. Results Based on the proportion of ambiguous clustering, six clinically meaningful cluster phenotypes were identified. There was significant overlap with the existing Banff classification (adjusted rand index, 0.48). However, the data-driven approach eliminated intermediate and mixed phenotypes and created acute rejection clusters that are each significantly associated with graft failure. Finally, a novel visualization tool presents disease phenotypes and severity in a continuous manner, as a complement to the discrete clusters. Conclusions A semisupervised clustering approach for the identification of clinically meaningful novel phenotypes of kidney transplant rejection has been developed and validated. The approach has the potential to offer a more quantitative evaluation of rejection subtypes and severity, especially in situations in which the current histologic categorization is ambiguous.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1681/asn.2020101418
- OA Status
- green
- Cited By
- 61
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3135417128
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3135417128Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1681/asn.2020101418Digital Object Identifier
- Title
-
Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised ClusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-09Full publication date if available
- Authors
-
Thibaut Vaulet, Gillian Divard, Olivier Thaunat, Evelyne Lerut, Aleksandar Senev, Olivier Aubert, Elisabet Van Loon, Jasper Callemeyn, Marie‐Paule Emonds, Amaryllis H. Van Craenenbroeck, Katrien De Vusser, Ben Sprangers, Maud Rabeyrin, Valérie Dubois, Dirk Kuypers, Maarten De Vos, Alexandre Loupy, Bart De Moor, Maarten NaesensList of authors in order
- Landing page
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https://doi.org/10.1681/asn.2020101418Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/8259675Direct OA link when available
- Concepts
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Cluster analysis, Medicine, Kidney transplantation, Data mining, Transplantation, Computer science, Internal medicine, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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61Total citation count in OpenAlex
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2025: 7, 2024: 8, 2023: 11, 2022: 29, 2021: 6Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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