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.
胆囊癌是胆道系统最常见的恶性肿瘤，根治性手术切除是目前胆囊癌唯一有效的治疗手段，但效果欠佳。识别胆囊癌患者的关键预后因素，建立一种个体化、准确的生存预测模型，对于胆囊癌患者预后评估、治疗方案选择及临床决策支持具有重要的指导意义。目前常用的肿瘤TNM分期预测价值有限；Cox回归模型是最常用、最经典的生存分析方法，但难以建立预后变量之间的关联关系。列线图及贝叶斯网络等机器学习方法近年来逐渐用于建立胆囊癌的生存预测模型，显示出了一定先进性，但模型的精确度及临床应用仍需要进一步验证。建立我国胆囊癌多中心大样本数据库，基于机器学习算法建立更精准的生存预测模型以指导胆囊癌临床决策是今后研究的主要方向。.
Keywords: Bayes theorem; Gallbladder neoplasms; Machine learning; Nomogram; Survival prediction model.