ArticleNature communications2025
A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders.bioRxiv : the preprint server for biology · 2026Article
- A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images.Nature communications · 2025Article
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Authors and funding
19 authors.
Funding
Abstract
Mesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 88% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.
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