Cross-Domain Validation of a Resection-Trained Self-Supervised Model on Multicentre Mesothelioma Biopsies
This work addresses the challenge of using AI tools in real-world clinical settings where small biopsies are common, though it is incremental as it adapts existing methods to new data.
The study tackled the problem of applying computational pathology models trained on large resection specimens to small biopsies for mesothelioma, showing that a self-supervised encoder trained on resection tissue can predict patient survival and classify tumor subtypes on biopsy material.
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.