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arXiv · 2512.01681

Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies

Farzaneh Seyedshahi·Francesca Damiola·Sylvie Lantuejoul·Ke Yuan·John Le Quesne
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Abstract

Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and patient care. Most computational pathology models are trained on large tissue images from resection specimens, limiting their use in real-world settings where small biopsies are common. We show that a self-supervised encoder trained on resection tissue can be applied to biopsy material, capturing meaningful morphological patterns. Using these patterns, the model can predict patient survival and classify tumor subtypes. This approach demonstrates the potential of AI-driven tools to support diagnosis and treatment planning in mesothelioma.

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Farzaneh Seyedshahi, Francesca Damiola, Sylvie Lantuejoul, Ke Yuan, John Le Quesne. 2025-12-01. Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies. https://arxiv.org/abs/2512.01681

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