Radiomic features at contrast-enhanced CT predict proliferative hepatocellular carcinoma and its prognosis after...

Proliferative hepatocellular carcinoma (HCC) is an aggressive phenotype associated with unfavorable clinical outcomes. Predicting the preoperative subtype of HCC can aid in the development of individualized treatment. We retrospectively recruited 180 HCC patients who underwent hepatic resection and established a CT-based radiomics model for predicting proliferative HCCs. The evaluation of tumor response to transarterial chemoembolization therapy and progression-free survival (PFS) according to the radiomics model was further performed in internal (n = 54) and external (n = 80) outcome cohorts. In our study, 98 of 180 (54%) patients were confirmed to have proliferative HCCs. The radiomics model comprising 9 radiomic features and exhibited good performance for predicting proliferative HCCs. The nomogram integrated radiomics and serum α-fetoprotein level showed good calibration and discrimination in both the training cohort (AUC = 0.848) and the validation cohort (AUC = 0.825). Predicted proliferative HCCs (high radiomics scores) were associated with lower response rate (P < 0.05) and worse PFS (P < 0.05) compared to predicted non-proliferative HCCs in outcomes cohorts. We linked radiomics model to gene expression, unveiling that activated/immature B cells and tertiary lymphoid structures were downregulated in the high radiomics group. The proposed CT radiomics model exhibited good performance for identifying proliferative HCCs, which may facilitate clinical decision-making. Our findings suggest a potential correlation between proliferative HCC and immunosuppressive tumor microenvironment.