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

TL;DR

This study demonstrates that a self-supervised model trained on resection mesothelioma tissue can effectively be applied to biopsy samples, enabling accurate subtype classification and survival prediction, thus bridging the gap between research and clinical practice.

Contribution

The paper introduces a cross-domain application of a resection-trained self-supervised model to biopsy data, enhancing its clinical utility in mesothelioma diagnosis and prognosis.

Findings

Model predicts patient survival accurately

Model classifies tumor subtypes effectively

Self-supervised encoder captures meaningful morphological patterns

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.