Interpretable multimodal deep learning improves postoperative risk stratification in intrahepatic cholangiocarcinoma in multicentre cohorts.

ABSTRACT: Surgical resection is the primary curative treatment for intrahepatic cholangiocarcinoma (ICC), yet high postoperative recurrence rates pose a significant challenge. We developed an interpretable, transformer-based deep-learning pipeline that integrates multimodal data-including clinical variables, radiomic features, and whole-slide pathology images-by fusing a pre-trained encoder with a transformer network. To biologically validate our model, we leveraged spatial transcriptomics and proteomics to decipher the attention mechanisms underlying its predictions. It demonstrated robust performance in predicting 2-year overall survival, with area under the curve (AUC) values of 0.952 (95% CI: 0.909-0.983), 0.924 (95% CI: 0.804-1.000), and 0.924 (95% CI: 0.828-0.993) in three independent validati

SUBMITTER: Wan M 

PROVIDER: S-EPMC12855921 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature