Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.

ABSTRACT:

Purpose

Hepatocellular carcinoma (HCC) is a perilous malignant tumor, and transcatheter arterial chemoembolization (TACE) is a widely adopted treatment technique for advanced HCC. Nevertheless, TACE may not effectively reduce the risk of distant metastases. As emerging and rapidly evolving technologies, radiomics (RAD) and deep learning (DL) have potential for predicting the outcomes post-TACE for HCC patients. This study aimed to develop a predictive model that integrates clinical data, Rad and DL to assess the risk of distant metastasis in HCC patients following TACE, and to evaluate its efficacy for these patients.

Methods

475 cases were included in our study and were divided into training, testing, and external validation cohorts. Rad, DL, DLR (Deep learning and radiomic

SUBMITTER: Liu C 

PROVIDER: S-EPMC12436248 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature