Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

ABSTRACT:

Objective

To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs).

Methods

This retrospective study included 172 patients with uHCC who underwent combination therapy of TACE-HAIC with TKIs and PD-1 inhibitors. Among them, 122 were from the Interventional Department of the Harbin Medical University Cancer Hospital, with 92 randomly assigned to the training cohort and 30 cases randomly assigned to the testing cohort. The remaining 50 cases were from the Inte

SUBMITTER: Yin L 

PROVIDER: S-EPMC11748841 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature