The prognostic value of TNFRSF4 in hepatocellular carcinoma using a two-zone CT imaging model

Introduction We aimed to evaluate the possible association between TNFRSF4 and the prognosis of hepatocellular carcinoma (HCC), and particularly to develop CT-based radiomics models to predict TNFRSF4 levels before surgery with either tumor per se or peritumoral changes. Methods 286 HCC patients' genomic data from the Cancer Genome Atlas (TCGA) and 35 individuals' arterial phase enhanced computed tomography (CT) data from the Cancer Imaging Archive (TCIA) were used for the prognostic analysis and radiomics feature extraction. Accordingly, the machine-learning algorithms, including maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE), were employed to construct the whole-tumor and whole-tumor-peritumoral (whole tumor and a 3 mm peritumor area) models with the receiver operating characteristic curve evaluating the accuracy. Results & Conclusions High TNFRSF4 expression was associated with shorter overall survival in this cohort. For the whole tumor model, 20 optimal features were extracted with mRMR, followed by screening out 3 representative subsets with RFE. When combined with the logistic regression model, the area under the receiver operating characteristic curve (AUC) for the training and verification sets equaled 0.813 and 0.759, respectively. Subsequently, the whole-tumor-peritumoral model was established, as well as 20 features derived from the mRMR algorithm and 4 typical subsets from RFE, and it was found that the AUC value was 0.871 in the training set and 0.803 in the verification set. In conclusion, the radiomics model based on the tumor per se and peritumoral information predicted the TNFRSF4 expression and its association with overall survival in HCC patients.