摘要
BACKGROUND The incidence and mortality rates of primary hepatocellular carcinoma(HCC)are high,and the conventional treatment is radiofrequency ablation(RFA)with transcatheter arterial chemoembolization(TACE);however,the 3-year survival rate is still low.Further,there are no visual methods to effectively predict their prognosis.AIM To explore the factors influencing the prognosis of HCC after RFA and TACE and develop a nomogram prediction model.METHODS Clinical and follow-up information of 150 patients with HCC treated using RFA and TACE in the Hangzhou Linping Hospital of Traditional Chinese Medicine from May 2020 to December 2022 was retrospectively collected and recorded.We examined their prognostic factors using multivariate logistic regression and created a nomogram prognosis prediction model using the R software(version 4.1.2).Internal verification was performed using the bootstrapping technique.The prognostic efficacy of the nomogram prediction model was evaluated using the concordance index(CI),calibration curve,and receiver operating characteristic RESULTS Of the 150 patients treated with RFA and TACE,92(61.33%)developed recurrence and metastasis.Logistic regression analysis identified six variables,and a predictive model was created.The internal validation results of the model showed a CI of 0.882.The correction curve trend of the prognosis prediction model was always near the diagonal,and the mean absolute error before and after internal validation was 0.021.The area under the curve of the prediction model after internal verification was 0.882[95%confidence interval(95%CI):0.820-0.945],with a specificity of 0.828 and sensitivity of 0.656.According to the Hosmer-Lemeshow test,χ^(2)=3.552 and P=0.895.The predictive model demonstrated a satisfactory calibration,and the decision curve analysis demonstrated its clinical applicability.CONCLUSION The prognosis of patients with HCC after RFA and TACE is affected by several factors.The developed prediction model based on the influencing parameters shows a good prognosis predictive efficacy.
