Development of pre and post‐operative nomograms to predict individual survival for ideal liver resection candidates with hepatocellular carcinoma. (13th September 2021) - Zeng, Jinshu; Zeng, Jianxing; Liu, Jingfeng; Zeng, Jinhua
Abstract: Background: Liver resection is currently the only recommended treatment option for solitary hepatocellular carcinoma (HCC) at an early stage, with well‐preserved liver function and no clinically significant portal hypertension. However, this population is heterogeneous, rendering it crucial to develop a risk stratification tool. Therefore, this study aimed to develop preoperative and post‐operative nomograms to predict individual survival and stratify patient risk in the ideal candidates for liver resection. Methods: A total of 1405 ideal liver resection candidates were recruited. Independent risk factors were identified by Cox regression model and used to establish two ideal liver resection for overall survival (IROS) nomograms in training cohort. Model performance was assessed by discrimination, calibration, clinical usefulness. The two model also compared with six other prognostic nomograms and six other staging systems. Results: Multivariate COX analysis revealed that ALP, ln(AFP), NrLR, PNI, ln(tumor size), microvascular invasion, Edmondson‐Steiner grade and tumour capsular were the independent risk factors associated with mortality. 5 preoperative variables were incorporated to construct IROS‐pre model; All eight available variables were used to draw IROS‐post model. The C‐index, K‐index, time‐dependent AUC and DCA of the two models showed significantly better predictive performances than other models. The models could stratify patients into three different risk groups. The web‐based tools are convenient for clinical practice. Conclusions: These two nomograms were developed to estimate survival probability and stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts, as well as different subgroups. Both IROS models will help guide individualized follow‐up.