ArticleJournal of translational medicine2026
MRI-based clinical-radiologic model to predict the efficacy and prognosis of interventional with systemic therapy in advanced hepatocellular carcinoma: a multicenter study.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
aimUsing magnetic resonance imaging (MRI) features combined with clinical information to predict treatment response in advanced hepatocellular carcinoma (aHCC) patients receiving tyrosine kinase inhibitors (TKIs), programmed cell death protein 1 (PD-1) inhibitors with interventional therapy (TPI therapy).
methodsThis retrospective, multicenter study included patients with baseline contrast-enhanced MRI images between December 2018 and December 2023. Clinical data of patients were collected and features in MRI images were used for radiomic analysis. A clinical-radiomic model was constructed by integrating clinical variables with radiomic scores.
resultsA total of 239 patients were enrolled of which 15 patients achieved complete response, 161 achieved partial response, 26 displayed stable disease, and 37 displayed progressive disease. To further investigate the efficacy of TPI therapy by clinical-radiomic model, patients were randomly assigned to training (N = 191) and validation group (N = 48) according to the ratio of 3:1. The area under the curve (AUC) of the clinical-radiologic model (training cohort 0.947, validation cohort 0.803) was better than that of the clinical model (training cohort 0.674, validation cohort 0.706) and radiologic model (training cohort 0.930, validation cohort 0.790). Meanwhile, participants were divided into high-risk and low-risk groups for analysis of overall survival (OS) and progression-free survival (PFS) in the clinical-radiologic model. The prognosis of OS and PFS in high risk patients in both training (OS, HR 2.074, 95%CI 1.294–3.324, p = 0.002; PFS, HR 2.097, 95%CI 1.433–3.068, p = 0.001) and validation cohort (OS, HR 4.534, 95%CI 1.054–19.494, p = 0.026; PFS, HR 2.969, 95%CI 1.383–6.377, p = 0.003) was worse.
conclusionRadiographic features based on MRI images with clinical data can predict the efficacy and risk stratification of TPI therapy in aHCC.
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