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

ABSTRACT:

Background & aims

The transcriptomic classification of intrahepatic cholangiocarcinoma (iCCA) has recently been refined from two to five classes, each associated with pathological features, targetable genetic alterations, and survival outcomes. Despite its potential prognostic and therapeutic value, the transcriptomic classification is not routinely used in practice because of technical limitations, including insufficient tissue material and the high cost of molecular analyses. Here, we assessed a self-supervised learning (SSL) model for predicting iCCA transcriptomic classes on digitised whole-slide images (WSIs).

Methods

Transcriptomic classes defined from RNA sequencing data were available for all samples. The SSL method (Giga-SSL) was used to train our model on a discove

SUBMITTER: Beaufrere A 

PROVIDER: S-EPMC12800354 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature