The progress on survival prediction model of gallbladder carcinoma
10.3760/cma.j.cn112139-20200116-00032
- VernacularTitle:胆囊癌相关生存预测模型的研究现状
- Author:
Zhimin GENG
1
;
Qi LI
;
Zhen ZHANG
;
Shubin SI
;
Zhiqiang CAI
;
Yaling ZHAO
;
Zhaohui TANG
Author Information
1. 西安交通大学第一附属医院肝胆外科 710061
- Keywords:
Gallbladder neoplasms;
Bayes theorem;
Survival prediction model;
Nomogram;
Machine learning
- From:
Chinese Journal of Surgery
2020;58(8):649-652
- CountryChina
- Language:Chinese
-
Abstract:
Gallbladder carcinoma (GBC) is the most common malignancy of the biliary tract, radical resection is the only effective treatment for GBC at present. However, the postoperative effect is still poor. Therefore, identifying the key prognostic factors and establishing an individual and accurate survival prediction model for GBC are critical to prognosis assessment, treatment options and clinical decision support in patients with GBC. The prediction value of current commonly used TNM staging system is limited. Cox regression model is the most commonly used classical survival analysis method, but it is difficult to establish the association between prognostic variables. Nomogram and machine learning techniques including Bayesian network have been used to establish survival prediction model of GBC in recent years, which representing a certain degree of advancement, however, the model precision and clinical application still need to be further verified. The establishment of more accurate survival prediction models for GBC based on machine learning algorithm from Chinese multicenter large sample database to guide the clinical decision-making is the main research direction in the future.
