Song Wei, Zhu Zhi-Gang, Wu Qiong, Lv Chang-Guang, Wang Yong-Gang, Chen Lei, Miao Dong-Liu
Department of Intervention and Vascular Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou Cancer Medical Center, Suzhou, China.
Cancer Manag Res. 2018 Jun 14;10:1535-1541. doi: 10.2147/CMAR.S163291. eCollection 2018.
The aim of the study was to develop and validate a nomogram to predict overall survival (OS) in biliary tract cancer (BTC).
Patients diagnosed with BTC between 2004 and 2014 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to 2 sets, the training set (n = 8,869) and the validation set (n = 8,766), for the purposes of validation. The prognostic effects of each variable were examined using univariate and multivariate analyses. Cox regression models and a nomogram were developed based on significant prognostic factors. The predictive and discriminatory capacity of the nomogram was evaluated by Harrell's concordance index (C-index) and calibration plots.
Data of 17,635 patients with BTC were collected from the SEER database. Age; race; tumor site; tumor grade; T, N, and M stage; marital status; and therapy were associated with survival in the multivariate models. All these factors were integrated to construct the nomogram. The nomogram for predicting OS displayed better discrimination power than the tumor-node-metastasis (TNM) stage system 6th edition in the training set and validation set. The calibration curve indicated that the nomogram was able to accurately predict 3- and 5-year OS.
This predictive model has the potential to provide an individualized risk estimate of survival in patients with BTC.
本研究旨在开发并验证一种用于预测胆道癌（BTC）总生存期（OS）的列线图。
从监测、流行病学和最终结果（SEER）数据库中选取2004年至2014年间诊断为BTC的患者进行研究。为进行验证，所有患者被随机分为两组，训练集（n = 8869）和验证集（n = 8766）。使用单因素和多因素分析检验每个变量的预后效果。基于显著的预后因素建立Cox回归模型和列线图。通过Harrell一致性指数（C指数）和校准图评估列线图的预测能力和区分能力。
从SEER数据库收集了17635例BTC患者的数据。年龄、种族、肿瘤部位、肿瘤分级、T、N和M分期、婚姻状况以及治疗在多因素模型中与生存相关。所有这些因素被整合以构建列线图。在训练集和验证集中，预测OS的列线图显示出比肿瘤-淋巴结-转移（TNM）分期系统第6版更好的区分能力。校准曲线表明列线图能够准确预测3年和5年总生存期。
这种预测模型有可能为BTC患者提供个性化的生存风险估计。
