A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images

Farzaneh Seyedshahi, Kai Rakovic, Nicolas Poulain, Adalberto Claudio Quiros, Ian R. Powley, Cathy Richards, Hussein Uraiby, Sonja Klebe, David A. Moore, Apostolos Nakas, Claire R. Wilson, Marco Sereno, Leah Officer-Jones, Catherine Ficken, Ana Teodosio, Fiona Ballantyne, Daniel Murphy, Ke Yuan () and John Quesne ()
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Farzaneh Seyedshahi: University of Glasgow
Kai Rakovic: University of Glasgow
Nicolas Poulain: Cancer Research UK Scotland Institute
Adalberto Claudio Quiros: University of Glasgow
Ian R. Powley: Cancer Research UK Scotland Institute
Cathy Richards: University Hospitals of Leicester
Hussein Uraiby: University Hospitals of Leicester
Sonja Klebe: Flinders Health and Medical Research Institute
David A. Moore: UCL Cancer Institute
Apostolos Nakas: University Hospitals of Leicester
Claire R. Wilson: University of Leicester
Marco Sereno: University of Leicester
Leah Officer-Jones: Cancer Research UK Scotland Institute
Catherine Ficken: Cancer Research UK Scotland Institute
Ana Teodosio: University of Birmingham
Fiona Ballantyne: Cancer Research UK Scotland Institute
Daniel Murphy: University of Glasgow
Ke Yuan: University of Glasgow
John Quesne: University of Glasgow

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-63846-9

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