Development and validation of a prognostic model for patients with hepatocellular carcinoma undergoing radiofrequency ablation. (10th July 2019) - Kim, Chang Gon; Lee, Hyun Woong; Choi, Hye Jin; Lee, Jung Il; Lee, Hye Won; Kim, Seung Up; Park, Jun Yong; Kim, Do Young; Ahn, Sang Hoon; Han, Kwang‐Hyub; Kim, Han Sang; Kim, Kyung Hwan; et al.
Abstract: Background: There are large variations in prognosis among hepatocellular carcinoma (HCC) patients undergoing radiofrequency ablation (RFA). However, current staging or scoring systems hardly discriminate the outcome of HCC patients treated with RFA. Methods: A total of 757 treatment‐naïve HCC patients undergoing RFA (derivation cohort) were analyzed to establish a nomogram for disease‐free survival (DFS) based on Cox proportional hazard regression model. Accuracy of the nomogram was assessed and compared with conventional staging or scoring systems. Furthermore, external validation was performed in an independent cohort including 208 patients (validation cohort). Results: Tumor size, tumor number, alpha‐fetoprotein, prothrombin induced by vitamin K absence‐II, lymphocyte count, albumin, and presence of ascites were adopted to construct the prognostic nomogram from the derivation cohort. Calibration curves to predict probability of DFS at 3 and 5 years after RFA showed good agreements between the nomogram and actual observations. The concordance index of the present nomogram was 0.759 (95% confidence interval 0.728‐0.790), which was superior to those of conventional staging or scoring systems (range 0.505‐0.683, all P  < .001). These results were also reproduced in the validation cohort. Conclusion: Our simple‐to‐use nomogram optimized for treatment‐naïve HCC patients undergoing RFA provided better prognostic performance than conventional staging or scoring systems. Abstract : A novel prognostic model was developed and externally validated in treatment‐naïve patients with hepatocellular carcinoma undergoing radiofrequency ablation, allowing more accurate prognostication compared to the current staging or scoring systems. This model can be used for stratification of individual patients' risk, optimizing surveillance schedule, guiding treatment strategies, and designing future prospective studies.