Using Machine Learning to Identify Responders to TACE or HAIC for uHCC

The goal of this observational study is to learn about the efficacy of Transarterial Chemoembolization (TACE) versus Hepatic Arterial Infusion Chemotherapy (HAIC) in patients with unresectable hepatocellular carcinoma (HCC). The main questions it aims to answer are: Can distinct imaging phenotype subtypes be identified in unresectable HCC patients using radiomics and unsupervised clustering? Do these different imaging subtypes show significant differences in treatment efficacy (such as objective response rate, progression-free survival, and overall survival) after receiving TACE or HAIC? Can this method objectively identify which imaging subtype of patients is more suitable for TACE and which may benefit more from HAIC? Participants in this study are adult patients diagnosed with unresectable HCC (BCLC stage B or C) who have already undergone complete TACE or HAIC treatment as part of their regular medical care between January 2015 and December 2024. Researchers will retrospectively analyze their existing clinical data and pre-treatment medical images to compare outcomes.