Abstract
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 (
n
= 227) and test sets (
n
= 43). All included patients had available contrast‐enhanced CT and RNA sequencing data. The T cell‐inflamed GEP consisted of 18 genes derived from RNA sequencing data, and patients were classified into GEP‐high and GEP‐low groups. A support vector machine was used to establish the radiomics model. The immunotherapy dataset comprised patients who underwent contrast‐enhanced CT and received anti‐programmed cell death protein 1/programmed cell death ligand 1 immunotherapy at three hospitals (209 lesions). This immunotherapy dataset was used to evaluate the association between the predicted GEP status and immunotherapy response. Additionally, overall survival was compared between patients with and without predicted GEP‐high lesions in the immunotherapy dataset.
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%
vs
. 9.3%,
p
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.