ArticleHealth care science2026
A Computed Tomography Radiomics Approach for Assessing the T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma and Its Association With Immunotherapy Response.
Article in Health care science, 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
Background: The T cell-inflamed gene expression profile (GEP) provides a biomarker for immunotherapy response across various cancers. However, specific biomarkers for immunotherapy response in hepatocellular carcinoma (HCC) are lacking. In this study, we established a computed tomography (CT)-based radiomics model to predict the T cell-inflamed GEP and immunotherapy response in HCC. Methods: This retrospective, multicenter study included 270 patients with HCC, who were divided into training ( Results: The areas under the receiver operating characteristic curve of the radiomics model for predicting GEP in the training and test sets were 0.824 (95% confidence interval [CI] = 0.757-0.892) and 0.827 (95% CI: 0.674-0.980), respectively. In the immunotherapy dataset, the response rate of liver lesions at 6 months was significantly higher in the GEP-high group than in the GEP-low group (48.1% Conclusions: This study provides the first CT-based radiomics model for predicting the T cell-inflamed GEP in HCC. This model highlights the potential of the T cell-inflamed GEP as a noninvasive biomarker to help guide immunotherapy selection.
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