Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

Predicting transcriptomic classes directly from routine histological slides has the potential to enhance the clinical management of intrahepatic cholangiocarcinoma, enabling more accurate prognostication and supporting therapeutic decision-making. By eliminating the need for manual slide annotation, large tissue samples, or resource-intensive molecular analyses, our self-supervised learning-based model offers a practical and scalable solution that can be applied to both biopsy and surgical specimens. This approach could accelerate the adoption of transcriptomic classification in everyday practice and help guide more personalized treatment strategies for patients with intrahepatic cholangiocarcinoma.

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Publication Details

Journal
JHEP Reports
Published
2025-11-13
DOI
https://doi.org/10.1016/j.jhepr.2025.101675
Primary Topic
Cholangiocarcinoma and Gallbladder Cancer Studies
Type
article
Field-Weighted Citation Impact
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article

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

Aurélie Bèaufrere, Thomas Walter, Rémy Nicolle, Victoria Priori et al.
JHEP Reports
Cholangiocarcinoma and Gallbladder Cancer Studies
article

Self-supervised learning to predict intrahepatic cholangiocarcinoma transcriptomic classes on routine histology

Aurélie Bèaufrere, Thomas Walter, Rémy Nicolle, Victoria Priori, Nathalie Théou–Anton, Jérémy Augustin, Valérie Paradis, Tristan Lazard, Jérôme Cros, Julien Caldéraro, Mickaël Lesurtel, Mohamed Bouattour, Kévin Mondet, Gwladys Lubuela, Camille Pignolet, Miguel Albuquerque, Baptiste Pichon
article en

Abstract

Predicting transcriptomic classes directly from routine histological slides has the potential to enhance the clinical management of intrahepatic cholangiocarcinoma, enabling more accurate prognostication and supporting therapeutic decision-making. By eliminating the need for manual slide annotation, large tissue samples, or resource-intensive molecular analyses, our self-supervised learning-based model offers a practical and scalable solution that can be applied to both biopsy and surgical specimens. This approach could accelerate the adoption of transcriptomic classification in everyday practice and help guide more personalized treatment strategies for patients with intrahepatic cholangiocarcinoma.

JHEP ReportsVol. 8(2)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Université Paris Cité (FR), Université Paris Sciences et Lettres (FR), Hôpital Beaujon (FR), Assistance Publique – Hôpitaux de Paris (FR), Centre de Recherche sur l'Inflammation (FR), Hôpital Bichat-Claude-Bernard (FR), Hôpital Albert-Chenevier (FR)
Openalex Percentile: Top 47%
Cholangiocarcinoma and Gallbladder Cancer Studies
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