Liu Rongqiang, Zhang Chenxuan, Shen Yankun, Wang Jianguo, Ye Jing, Yu Jia, Wang Weixing
Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei Province, China.
Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200030, China.
Eur J Med Res. 2025 Apr 26;30(1):331. doi: 10.1186/s40001-025-02513-7.
Gallbladder cancer (GBC) arises from the malignant transformation of epithelial cells that line the gallbladder mucosa. The likelihood of developing GBC escalates with advancing age, and the condition generally presents a dismal prognosis. Despite this, there is a limited amount of research focusing on the prognostic determinants linked to GBC. As a result, this study sought to create a nomogram for evaluating GBC prognostic factors.
In this investigation, a total of 8,615 cases of GBC from the Surveillance, Epidemiology, and End Results (SEER) database spanning from 2000 to 2020 were collected. In a 7:3 ratio, these instances were randomly assigned to one of two groups: training or internal validation. To assess the impact of clinical variables on overall survival (OS) in patients with GBC, both univariate and multivariate Cox regression analyses were utilized. The clinical criteria established were used to develop a nomogram. The effectiveness of the nomogram was evaluated through several approaches, such as receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curves, and Kaplan-Meier (KM) analysis.
To predict the prognosis of GBC patients, a nomogram was created based on the following criteria: sex, rural-urban continuum, marital status, nodes, histology, radiation, chemotherapy, metastasis, age, surgery, and grade. The training set had an area under the curve for 1-year, 3-year, and 5-year OS of 0.79, 0.78, and 0.78, respectively. The DCA curves demonstrated that the model was clinically useful and well-corrected. Patients with GBC were categorized into high-risk and low-risk groups based on the median risk score. KM curves revealed a significantly lower survival rate for the high-risk group in comparison with the low-risk group (P < 0.001).
Our model demonstrated strong predictive capabilities for the prognosis of GBC patients, thereby aiding in the refinement of treatment strategies for these individuals.
胆囊癌（GBC）起源于胆囊黏膜上皮细胞的恶性转化。患胆囊癌的可能性随着年龄的增长而增加，并且该疾病通常预后不佳。尽管如此，针对与胆囊癌相关的预后决定因素的研究数量有限。因此，本研究旨在创建一个用于评估胆囊癌预后因素的列线图。
在本调查中，收集了2000年至2020年监测、流行病学和最终结果（SEER）数据库中的8615例胆囊癌病例。按照7:3的比例，将这些病例随机分为两组之一：训练组或内部验证组。为了评估临床变量对胆囊癌患者总生存期（OS）的影响，采用了单因素和多因素Cox回归分析。所建立的临床标准用于开发列线图。通过几种方法评估列线图的有效性，如受试者操作特征（ROC）曲线、决策曲线分析（DCA）、校准曲线和Kaplan-Meier（KM）分析。
为了预测胆囊癌患者的预后，基于以下标准创建了列线图：性别、城乡连续体、婚姻状况、淋巴结、组织学、放疗、化疗、转移、年龄、手术和分级。训练集1年、3年和5年总生存期的曲线下面积分别为0.79、0.78和0.78。DCA曲线表明该模型在临床上有用且校正良好。根据中位风险评分将胆囊癌患者分为高风险和低风险组。KM曲线显示，与低风险组相比，高风险组的生存率显著更低（P < 0.001）。
我们的模型对胆囊癌患者的预后显示出强大的预测能力，从而有助于优化这些患者的治疗策略。
