Construction and application value of nomogram predictive model for mixed-type liver cancer based on SEER database
10.3760/cma.j.cn115610-20230910-00082
- VernacularTitle:基于SEER数据库混合型肝癌列线图预测模型的构建及其应用价值
- Author:
Shui LIU
1
;
Jiyao SHENG
;
Xuewen ZHANG
Author Information
1. 吉林大学第二医院肝胆胰外科 吉林省肝胆胰疾病转化医学工程实验室，长春 130041
- Keywords:
Liver neoplasms;
Database;
Nomogram;
Prediction;
Prognosis
- From:
Chinese Journal of Digestive Surgery
2023;22(S1):44-50
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the prognostic factors of combined hepatocellular-cholangiocarcinoma (CHC) based on Surveillance, Epidemiology, and End Results (SEER), and to construct and investigate the application value of a nomogram predictive model for CHC.Methods:The retrospective cohort study was conducted. The clinicopathological data of 208 patients with CHC entered into SEER database from January 2004 to December 2019 were analyzed. There were 150 males and 58 females, 121 cases with age ≥60 years, 87 cases with age <60 years. All the 208 patients with CHC were divided into a training set and a validation set in a 7∶3 ratio based on the random table method. The COX proportional hazard model was used to construct the nomogram predictive model, which was validated and compared the predictive performance with the eighth edition of the American Joint Committee on Cancer (AJCC). Observation indicators: (1) survival of patients with CHC; (2) prognostic factors analysis of patients with CHC; (3) construction and perfor-mance evaluation of the nomogram predictive model. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers, and comparison between groups was analyzed using the chi-square test. Comparison of ordinal data was analyzed using the rank sum test. The COX regression model was used for univariate and multivariate analyses. The Kaplan-Meier method was used to calculate survival rate. The Log-Rank test was used for survival analysis. The prognostic efficacy of the predictive model was evaluated using the consistency index (C-index) and the area under curve (AUC) of receiver operating characteristic (ROC) curve. The calibration curve was used to validate the accuraly of predictive model and decision curve was used to evaluate the clinical utility of predictive model. Results:(1) Survival of patients with CHC. The overall survival time of 145 patients with CHC in the training set was 15.0(range, 1.0-166.0)months, with 1-year survival rate as 52.7%. The overall survival time of 63 patients with CHC in the validation set was 11.5(range, 1.0-176.0)months, with 1-year survival rate as 48.3%. (2) Prognostic factors analysis of patients with CHC. Results of multivariate analysis showed that T staging, surgery, degree of tumor differentiation and chemotherapy were independent factors for prognosis of patients with CHC ( P<0.05). (3) Cons-truction and performance evaluation of the nomogram model. The nomogram predictive model was constructed based on results of multiveriate analysis. The C-index of nomogram predictive model in the training set and the validation set was 0.758 and 0.742, respectively. The C-index of the AJCC predictive model in the training set and the validation set was 0.622 and 0.662, respectively. The AUC of ROC of the nomogram predictive model for 1-, 3-, 5-year overall survival rates was 0.830, 0.861, 0.881 for CHC patients in the training set and 0.798, 0.813, 0.844 for CHC patients in the validation set. The AUS of ROC of AJCC predictive model for 1-, 3-, 5-year overall survival rates was 0.668, 0.699, 0.747 for CHC patients in the training set and 0.719, 0.760, 0.796 for CHC patients in the validation set. Results of calibration curve showed that 1-year overall survival rate calibration curve fitted well with the ideal straight line with a slope of 1, and the predicted results of the two prediction models were highly consistent with the actual results. Results of decision curve showed that nomogram predictive model had a better prediction efficiency than the AJCC predictive model. Conclusion:T staging, surgery, degree of tumor differentiation, chemotherapy are independent factors for prognosis of patients with CHC, and the nomogram constructed based on SEER database can more accurately evaluate the prognosis of CHC patients.
