摘要Background:Radiofrequency ablation(RFA)is one of the effective therapeutic modalities in patients with hepatocellular carcinoma(HCC).However,there is no proper method to evaluate the HCC response to RFA.This study aimed to establish and validate a clinical prediction model based on dual-energy com-puted tomography(DECT)quantitative-imaging parameters,clinical variables,and CT texture parameters.Methods:We enrolled 63 patients with small HCC.Two to four weeks after RFA,we performed DECT scanning to obtain DECT-quantitative parameters and to record the patients'clinical baseline variables.DECT images were manually segmented,and 56 CT texture features were extracted.We used LASSO al-gorithm for feature selection and data dimensionality reduction;logistic regression analysis was used to build a clinical model with clinical variables and DECT-quantitative parameters;we then added texture features to build a clinical-texture model based on clinical model.Results:A total of six optimal CT texture analysis(CTTA)features were selected,which were statis-tically different between patients with or without tumor progression(P＜0.05).When clinical vari-ables and DECT-quantitative parameters were included,the clinical models showed that albumin-bilirubin grade(ALBI)[odds ratio(OR)=2.77,95％confidence interval(CI):1.35-6.65,P=0.010],λAP(40-100 keV)(OR=3.21,95％CI:3.16-5.65,P=0.045)and ICAP(OR=1.25,95％CI:1.01-1.62,P=0.028)were asso-ciated with tumor progression,while the clinical-texture models showed that ALBI(OR=2.40,95％CI:1.19-5.68,P=0.024),λAP(40-100 kev)(OR=1.43,95％CI:1.10-2.07,P=0.019),and CTTA-score(OR=2.98,95％CI:1.68-6.66,P=0.001)were independent risk factors for tumor progression.The clinical model,clinical-texture model,and CTTA-score all performed well in predicting tumor progression within 12 months after RFA(AUC=0.917,0.962,and 0.906,respectively),and the C-indexes of the clinical and clinical-texture models were 0.917 and 0.957,respectively.Conclusions:DECT-quantitative parameters,CTTA,and clinical variables were helpful in predicting HCC progression after RFA.The constructed clinical prediction model can provide early warning of potential tumor progression risk for patients after RFA.