Objective To explore the feasibility of constructing a short-term therapeutic efficacy prediction model for hepatocellular carcinoma(HCC)after radiofrequency ablation(RFA)based on texture analysis of preoperative MRI arterial phase images.Methods A retrospective analysis was conducted on 169 HCC patients treated with RFA.Based on the short-term therapeutic efficacy,the patients were divided into a good prognosis group(112 cases)and a poor prognosis group(57 cases).Texture features of preoperative MRI arterial phase images were extracted using Mazda software,and dimension reduction was performed through Fisher coefficient,mutual information,classification error probability,and mean correlation coefficient.The patients were divided into a training group(n=119)and a testing group(n=50)in a 7∶3 ratio.Independent sample t-tests and the least absolute shrinkage and selection operator(LASSO)algorithm were employed for further feature selection.Subsequently,a radiomics model was established using LASSO regression and evaluated through the receiver operating characteristic(ROC)curve and area under the curve(AUC).Results The radiomics model comprised features such as S_2__2_SumOfSqs,Teta1,S_5_0_DifVarnc,S_2_0_DifEntrp,Horzl_LngREmph,and S_5_5_InvDfMom.The AUC of the model were 0.987[95％confidence interval(CI)0.965-1.000]and 0.918(95％CI 0.818-1.000)in the training and testing groups,respectively.The sensitivity was 98.7％(95％CI 92.4-100)and 93.9％(95％CI 84.8-100),and the specificity was 97.5％(95％CI 90.0-100)and 88.2％(95％CI 70.6-100),respectively.Conclusion The construction of a predictive model for short-term therapeutic efficacy of HCC after RFA based on texture analysis of preoperative MRI arterial phase images is feasible and demonstrates good predictive performance.