Department of Radiology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, PR China; Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, PR China.
Department of Radiology, Armed Police Force Hospital of Sichuan, 614000, Leshan, Sichuan, PR China.
Eur J Radiol. 2021 May;138:109631. doi: 10.1016/j.ejrad.2021.109631. Epub 2021 Mar 6.
We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA).
A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve.
The clinical model (Model) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images. The model yield the best performance (Model) was build using three independent variables, Signature and Signature. In training and testing cohorts, the concordance indexes (C-indexes) of Model were 0.846 (95 % CI, 0.735-0.957) and 0.755 (95 % CI, 0.540-969), and Model achieved C-indexes of 0.962 (95 % CI, 0.905-1) and 0.814 (95 % CI, 0.569-1). Both Model and Model outperformed the TNM staging system. Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis for Model and Model.
Two preoperative nomograms were constructed to predict 1-, 3-, and 5-years survival for individual pCCA patients, demonstrating the potential for clinical application to assist decision-making.
我们旨在开发生存预测工具,为肝门部胆管癌(pCCA)的临床决策提供信息。
共纳入 184 例 2010 年 1 月至 2018 年 12 月间接受根治性切除术和磁共振成像(MRI)检查的 pCCA 患者。110 例患者被随机选择用于模型开发,其余 74 例患者用于模型测试。分析术前临床、实验室和影像学数据。术前临床预测因素被独立使用或与放射组学特征相结合,通过多变量 Cox 比例风险方法构建不同的术前模型。构建了预测总生存(OS)的列线图,并通过判别能力、时间依赖性接收者操作特征曲线(ROC)、校准曲线和决策曲线评估其性能。
基于术前 CEA、cN 分期和影像学中肝动脉侵犯这三个独立变量构建了临床模型(模型)。使用三个独立变量构建的模型表现最佳(模型),包括 Signature 和 Signature。在训练和测试队列中,模型的一致性指数(C-index)分别为 0.846(95 % CI,0.735-0.957)和 0.755(95 % CI,0.540-969),模型的 C-index 分别为 0.962(95 % CI,0.905-1)和 0.814(95 % CI,0.569-1)。模型和模型均优于 TNM 分期系统。校准曲线显示出良好的一致性,通过决策曲线分析验证了模型和模型的临床应用价值。
构建了两个术前列线图来预测个体 pCCA 患者的 1 年、3 年和 5 年生存率,为临床决策提供了潜在的应用价值。