Establishment and application of a survival rate graph model based on biomarkers and imaging indexes after primary hepatocellular carcinoma resection

Abstract

Abstract Background Primary liver cancer (PLC) is a highly malignant disease. This study developed an effective and convenient tool to evaluate survival times of patients after hepatectomy, which can provide a reference point for clinical decisions. Methods Clinical and laboratory data of 243 patients with PLC after hepatectomy were collected. Univariate cox regression analysis, Lasso regression analysis and multivariate cox regression analysis were used to determine the best prediction index. Multivariate cox regression analysis was used to construct a survival prediction model. A receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to verify the model. The patients in this model were divided into was divided into high‐risk and low‐risk groups according to the optimal cut‐off value of the ROC curve for different prognostic years. Kaplan–Meier survival analysis and log‐rank test were used to analyse the survival differences between the two groups. Results Tumour size, portal vein thrombosis, distant metastasis, alpha‐fetoprotein and protein induced by vitamin K absence or antagonist‐II levels were independent risk factors for overall survival (OS) after PLC surgery. The area under the concentration‐time curve for 2‐, 3‐ and 4‐year survival of patients was 0.710, 0.825 and 0.919, respectively, with a good calibration of the Hosmer–Lemeshow test (p > 0.05) and net benefit. The mortality rates in patients with 2, 3 and 4 years of survival were 70.59%, 67.83% and 66.67% for the high‐risk group and 21.84%, 22.50% and 22.67% for the low‐risk group, respectively. The mortality rate of the high‐risk group was significantly higher than that of the low‐risk group (p < 0.05). Conclusion The OS model of prognosis after PLC surgery constructed in this study can be used to predict the 2‐, 3‐ and 4‐year survival prognoses of patients, and patients with different prognosis years can be re‐stratified so that they achieve more accurate and personalised assessment, thereby providing a reference point for clinical decision‐making