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

Farzaneh Seyedshahi, Francesca Damiola, Sylvie Lantuejoul, Ke Yuan, John LeQuesne

Keywords: Mesothelioma, Biopsy, Self-supervised Learning, Histopathology

TL;DR: The Validation of a Mesothelioma Resection-Trained Self-Supervised Model on Biopsies

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Abstract: Accurate subtype classification and outcome prediction in mesothelioma are essential for guiding therapy and predicting patient outcomes. However, most computational pathology models are trained exclusively on large tissue images from resection specimens, which limits their relevance in real-world diagnostic settings where small biopsies are the primary tissue source. Here, we assess the biopsy-level generalisability of a self-supervised encoder using a large, multicentre French cohort. We identify 53 biopsy-specific histomorphological clusters, quantify each patient’s proportional representation across these clusters, and use these profiles as inputs for two downstream tasks. The results show that a self-supervised encoder trained on resection tissue can be reliably transferred to biopsy material despite significant domain shifts.

Reproducibility: https://github.com/FarzanehSeyedshahi/Histomorphological-Phenotype-Learning

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Submission Number: 6