Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma - Wu, Cuiyun; Chen, Junfa; Fan, Yuqian; Zhao, Ming; He, Xiaodong; Wei, Yuguo; Ge, Weidong; Liu, Yang (Frontiers in Oncology, 2022)
Objectives: To develop and validate a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors, and to explore the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC). Methods: First-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomics signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomics signature and clinical factors that showed independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results: Sixteen higher-order radiomics features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). On multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grade were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grade to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of nomogram. Conclusion: The radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.