An interpretable deep learning framework based on TabNet-Cox for risk stratification and prognostic assessment in hepatocellular carcinoma immunotherapy.

ABSTRACT:

Objective

Therapeutic outcomes after immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC) are highly heterogeneous. Accurate prognostic assessment is essential for risk stratification and clinical management. This study aimed to develop and validate an interpretable deep-learning survival model, TabNet-Cox, for predicting overall survival (OS) in ICI-treated HCC patients.

Methods

A total of 453 consecutive HCC patients treated with ICIs at Harbin Medical University Cancer Hospital between January 2018 and December 2023 were retrospectively enrolled and randomly assigned to a training cohort (n = 339) and an internal validation cohort (n = 114). An independent external validation cohort of 105 patients was collected from the Second Affiliated Hospital of Harb

SUBMITTER: Dong W 

PROVIDER: S-EPMC12932533 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature