Nomogram-based Survival and Prognosis Prediction for HCC after surgery: analysis of the multi-center patients

Abstract

Objective: Hepatocellular carcinoma(HCC),the most prevalent form of liver cancer, owns high morbidity and mortality. The radical surgery is the preference. It is of great clinical significance to predict the postoperative survival. Methods: All clinical characteristics of 1187 patients participants from multicenter were collected. We identified several indicators significantly associated with HCC survival through logistic analysis to develop the prediction model. Further analysis revealed the independent predictive capacity of the predictive model. A nomogram comprising the predictive model was established. The decision curve analysis(DCA), receiver operating characteristic (ROC) curve analysis and Kaplan-Meier analysis confirmed the good performance of the predictive model. Results: As a result, we identified several clinical indicators that were significantly associated with HCC survival through univariate analysis and multivariate analysis. The predictive model was consist of clinical features and tumor characteristics readily obtained after surgery. All the factors above were incorporated into the nomogram and the application of the nomogram gave good discrimination and good calibration. Calibration curves showed a favorable consistency between the predicted probabilities. ROC curve analysis showed that the nomogram had good discrimination both in the training group and validation group, respectively. Moreover, decision curve analysis has been implemented to evaluate and compare prediction nomogram. Kaplan-Meier analysis showed significant differences in prognosis among different risk groups. Conclusion: The study provides a novel model for predicting HCC patients undergone radical surgery.