CT-based radiomics for predicting pathological grade in hepatocellular carcinoma - Huang, Yue; Chen, Lingfeng; Ding, Qingzhu; Zhang, Han; Zhong, Yun; Zhang, Xiang; Weng, Shangeng (Frontiers in Oncology, 2024)
Objective To construct and validate radiomics models for hepatocellular carcinoma (HCC) grade prediction based on contrast-enhanced CT (CECT).Patients with pathologically confirmed HCC after surgery who underwent CECT at our institution between January 2016 and December 2020 were enrolled and randomly divided into training and validation groups. With tumor segmentation and feature extraction, radiomic models were constructed using univariate analysis, followed by least absolute shrinkage and selection operator (LASSO) regression. In addition, combined models with clinical factors and radiomics scores (Radscore) were constructed using logistic regression. Finally, all models were evaluated using the receiver operating characteristic (ROC) curve with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).Results 242 patients were enrolled in this study, of whom 170 and 72 formed the training and validation groups, respectively. The arterial phase and portal venous phase (AP+VP) radiomics model was evaluated as the best model for predicting HCC pathological grade among all the models built in our study (AUC = 0.981 in the training group; AUC = 0.842 in the validation group) and was used to build a nomogram. Furthermore, the calibration curve and DCA indicated that the AP+VP radiomics model had a satisfactory prediction efficiency.Conclusions Low-and high-grade HCC can be distinguished with good diagnostic performance using a CECT-based radiomics model.