Development and validation of a model for predicting who can benefit from multiple TACE in HCC patients

This study was to develop and validate a model for predicting who can benefit from multiple transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients. 228 and 98 patients were included in the development and validation sets, respectively. The primary clinical endpoint was benefiting from consecutive multiple TACE treatments. Logistic regression analysis was used to screen the independent risk factors for the clinical endpoint. The independent risk factors were then used to construct the predictive model. The area under receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curves were used to evaluate the predictive ability of the model. Multivariate Logistic regression analysis showed that complete envelope, hepatic lopes, tumor number, and alpha-fetoprotein (AFP) were independent risk factors for benefiting from multiple TACE in HCC patients. The area under the curve (AUC) of the model constructed by using independent risk factors in the development and validation sets was 0.843 (95% confidence interval [CI]: 0.784–0.902) and 0.828 (95%CI: 0.739–0.916), respectively. The calibration curves and clinical decision curves showed that the model had good predictive ability. The model established in this study has a good predictive effect on HCC patients who can benefit from multiple TACE.