Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911296398557184 |
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| author | Seyedshahi, Farzaneh Damiola, Francesca Lantuejoul, Sylvie Yuan, Ke Quesne, John Le |
| author_facet | Seyedshahi, Farzaneh Damiola, Francesca Lantuejoul, Sylvie Yuan, Ke Quesne, John Le |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies Seyedshahi, Farzaneh Damiola, Francesca Lantuejoul, Sylvie Yuan, Ke Quesne, John Le Computer Vision and Pattern Recognition 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. |
| title | Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.01681 |