Decoding tumor heterogeneity using MRI for predicting response to immune checkpoint inhibitors in hepatocellular carcinoma: a multicenter study

Tumor heterogeneity hinders accurate prediction of response to immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC). We developed a habitat model using routine in-phase (IP) and opposed-phase (OP) MRI sequences, which provide complementary structure- and lipid-related information, to predict response to ICI therapy in HCC.

This study included 883 HCC patients from six institutions and public databases. We developed habitat, radiomics, peritumoral, clinical, and combined models to predict partial response (PR) in five institutions. Kaplan-Meier analysis was performed to evaluate survival differences based on prediction scores. Habitat model interpretability was assessed using Shapley additive explanations (SHAP) along with multi-omics and pathological analyses in the public databases and one external institution.

The habitat model showed numerically higher AUCs among the independent models, with AUCs of 0.923, 0.918, and 0.897 in the training set and two external validation sets, respectively. The combined model also showed favorable performance with AUCs of 0.959, 0.965, and 0.955, respectively. Calibration curves showed generally close agreement, while decision curve analyses suggested potential clinical net benefit of the habitat and combined models. Patients with higher habitat scores had significantly longer progression‑free and overall survival (all P < 0.05). Exploratory biological analyses showed that the higher habitat score group was associated with enhanced immune activity, downregulated fatty acid-related pathways, and shorter tumor-inflammatory spatial distances.

The IP and OP MRI-based habitat model showed potential for treatment-response prediction and prognostic stratification in patients with HCC receiving ICI therapy.