Next-Generation Morphometry for pathomics-data mining in histopathology.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 01 2023
28 01 2023
Historique:
received:
26
10
2022
accepted:
16
01
2023
pubmed:
29
1
2023
medline:
1
2
2023
entrez:
28
1
2023
Statut:
epublish
Résumé
Pathology diagnostics relies on the assessment of morphology by trained experts, which remains subjective and qualitative. Here we developed a framework for large-scale histomorphometry (FLASH) performing deep learning-based semantic segmentation and subsequent large-scale extraction of interpretable, quantitative, morphometric features in non-tumour kidney histology. We use two internal and three external, multi-centre cohorts to analyse over 1000 kidney biopsies and nephrectomies. By associating morphometric features with clinical parameters, we confirm previous concepts and reveal unexpected relations. We show that the extracted features are independent predictors of long-term clinical outcomes in IgA-nephropathy. We introduce single-structure morphometric analysis by applying techniques from single-cell transcriptomics, identifying distinct glomerular populations and morphometric phenotypes along a trajectory of disease progression. Our study provides a concept for Next-generation Morphometry (NGM), enabling comprehensive quantitative pathology data mining, i.e., pathomics.
Identifiants
pubmed: 36709324
doi: 10.1038/s41467-023-36173-0
pii: 10.1038/s41467-023-36173-0
pmc: PMC9884209
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
470Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
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