BACKGROUND

To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tumor immune microenvironment (TIME) into two types: immune-activated (IA), characterized by a high CD8 + score and high PD-L1 combined positive score (CPS), and non-immune-activated (NIA), encompassing all other conditions. We aimed to develop an MRI-based radiomics model to predict TIME types and validate its predictive capability for ICIs efficacy in HCC patients receiving anti-PD-1/PD-L1 therapy.

METHODS

The study included 200 HCC patients who underwent preoperative/pretreatment multiparametric contrast-enhanced MRI (Cohort 1: 168 HCC patients with hepatectomy from two centres; Cohort 2: 42 advanced HCC patients on anti-PD-1/PD-L1 therapy). In Cohort 1, after feature selection, clinical, intratumoral radiomics, peritumoral radiomics, combined radiomics, and clinical-radiomics models were established using machine learning algorithms. In cohort 2, the clinical-radiomics model's predictive ability for ICIs efficacy was assessed.

RESULTS

In Cohort 1, the AUC values for intratumoral, peritumoral, and combined radiomics models were 0.825, 0.809, and 0.868, respectively, in the internal validation set, and 0.73, 0.759, and 0.822 in the external validation set; the clinical-radiomics model incorporating neutrophil-to-lymphocyte ratio, tumor size, and combined radiomics score achieved an AUC of 0.887 in the internal validation set, outperforming clinical model (P = 0.049), and an AUC of 0.837 in the external validation set. In cohort 2, the clinical-radiomics model stratified patients into low- and high-score groups, demonstrating a significant difference in objective response rate (p = 0.003) and progression-free survival (p = 0.031).

CONCLUSIONS

The clinical-radiomics model is effective in predicting TIME types and efficacy of ICIs in HCC, potentially aiding in treatment decision-making.