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This study demonstrates that machine learning-based radiomic models derived from pre-treatment CT imaging, combined with clinical data, outperform established clinical biomarkers such as BCLC stage and ALBI grade in predicting survival and response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma (HCC). The integrated model accurately stratified patients into high- and low-risk groups with significant differences in overall survival (OS), progression-free survival (PFS), and immune checkpoint inhibitor response rates, validated across independent international cohorts.
