Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
11 Jun 2024
11 Jun 2024
Historique:
received:
11
08
2023
accepted:
08
05
2024
medline:
12
6
2024
pubmed:
12
6
2024
entrez:
11
6
2024
Statut:
epublish
Résumé
Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.
Identifiants
pubmed: 38862472
doi: 10.1038/s41467-024-48666-7
pii: 10.1038/s41467-024-48666-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4596Subventions
Organisme : RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
ID : BB/V016067/1
Organisme : RCUK | Engineering and Physical Sciences Research Council (EPSRC)
ID : EP/R018634/1
Informations de copyright
© 2024. The Author(s).
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