Zhao Kailiang, Yang Dashuai, Zhou Yu, Ding Youming
Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, No. 99 Zhangzhidong Road, Wuchang District, Wuhan, 430060, China.
J Gastrointest Surg. 2023 May;27(5):945-955. doi: 10.1007/s11605-023-05602-2. Epub 2023 Feb 2.
This study aims to construct a risk classification system and a nomogram in intrahepatic cholangiocarcinomafor patients (ICC).
Three thousand seven hundred thirty-seven patients diagnosed with ICC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results. The consistency index, time-dependent receiver operating characteristic curve, and the calibration plots were adopted to evaluate the effective performance of nomogram. Decision curve analysis (DCA), net reclassification index (NRI), and comprehensive discrimination improvement (IDI) were used to compare the advantages and disadvantages of two models. Kaplan-Meier curve showed the difference in prognosis among different groups.
Ten variables were selected to establish the nomogram for ICCA. The C-index (training cohort: 0.765, P < 0.05; validation cohort: 0.776, P < 0.05) and the time-dependent AUCs (the training cohort: the values of 1, 3, 5 years were 0.836, 0.873, and 0.888; the validation cohort: the values of 1, 3, 5 years were 0.833, 0.838, and 0.881) showed satisfactory discrimination. The calibration curves also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 3-, 5-year CSS: 0.879, 0.94, 0.771; validation cohort: 1-, 3-, 5-year CSS: 0.905, 0.945, 0.717) and IDI (training cohort: 1-, 3-, 5-year CSS: 0.24, 0.23, 0.22; validation cohort: 1-, 3-, 5-year CSS: 0.24, 0.46, 0.27) (P < 0.05) (compared with AJCC staging). DCA showed that the new model was more practical and had better recognition than AJCC staging.
A new risk stratification system for ICC patients has been developed, which can be a practical tool for patient management.
本研究旨在构建肝内胆管癌(ICC)患者的风险分类系统和列线图。
从监测、流行病学和最终结果中选取2010年至2015年间诊断为ICC的3737例患者。采用一致性指数、时间依赖性受试者工作特征曲线和校准图来评估列线图的有效性能。决策曲线分析(DCA)、净重新分类指数(NRI)和综合鉴别改善(IDI)用于比较两种模型的优缺点。Kaplan-Meier曲线显示不同组之间的预后差异。
选择10个变量建立ICC的列线图。C指数(训练队列:0.765,P<0.05;验证队列:0.776,P<0.05)和时间依赖性AUC(训练队列:1、3、5年的值分别为0.836、0.873和0.888;验证队列:1、3、5年的值分别为0.833、0.838和0.881)显示出令人满意的鉴别能力。校准曲线还显示列线图与实际观察结果一致。NRI(训练队列:1、3、5年CSS:0.879、0.94、0.771;验证队列:1、3、5年CSS:0.905、0.945、0.717)和IDI(训练队列:1、3、5年CSS:0.24、0.23、0.22;验证队列:1、3、5年CSS:0.24、0.46、0.27)(P<0.05)(与AJCC分期相比)。DCA显示新模型比AJCC分期更实用,识别能力更好。
已开发出一种新的ICC患者风险分层系统,可作为患者管理的实用工具。