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A novel inflammation‐based nomogram system to predict survival of patients with hepatocellular carcinoma

机译:一种新颖的基于炎症的诺模图系统,可预测肝细胞癌患者的生存

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Background and Aim The existed staging systems were limited in the accuracy of prediction for overall survival (OS) of hepatocellular carcinoma (HCC) patients. The aim of this study is to establish a novel inflammation‐based prognostic system with nomogram for HCC patients. Methods A prospective cohort of patients was recruited and assigned to the training cohort (n?=?659) and validation cohort (n?=?320) randomly. Different inflammation‐based score systems were evaluated to select the best one predicting overall survival (OS). The inflammation‐based score system with the highest predicting value and the parameters best reflecting tumor burden identified by multivariate analysis were selected to construct a novel predicting nomogram system. The predictive accuracy and discriminative ability of the nomogram were evaluated by concordance index (C‐index) and calibration curve and compared with conventional staging systems. Results With a highest C‐index and areas under the receiver operating characteristic curve (AUC), C‐reactive protein/albumin ratio (CAR) was selected to construct the novel system, along with tumor number, tumor size, macrovascular invasion and extra‐hepatic metastases. The C‐index of the nomogram was 0.813 (95% CI, 0.789‐0.837) in the training cohort and 0.794 (95% CI, 0.756‐0.832) in the validation cohort. The calibration curve for predicting probability of survival showed that the nomogram had a high consistency with follow‐up data. The C‐index of the novel system was higher than other conventional staging systems ( P ??0.001). Conclusions The novel inflammation‐based nomogram, developed from prospectively collected data in the present study, predicted the OS of HCC patients.
机译:背景与目的现有的分期系统在预测肝细胞癌(HCC)患者的总生存期(OS)的准确性方面受到限制。这项研究的目的是建立一种新的基于诺曼图的基于炎症的预后系统,用于HCC患者。方法招募预期患者队列,随机分为训练队列(n == 659)和验证队列(n == 320)。对不同的基于炎症的评分系统进行了评估,以选择最佳的预测整体生存率(OS)的系统。选择基于炎症的评分系统,该评分系统具有最高的预测值和通过多变量分析确定的最能反映肿瘤负荷的参数,从而构建了一个新颖的预测列线图系统。通过一致性指数(C-index)和校准曲线评估了诺模图的预测准确性和判别能力,并与常规分期系统进行了比较。结果选择C反应蛋白/白蛋白比率(CAR)来构建新系统,并获得最高的C指数和最高的受体工作特征曲线(AUC)区域,以及肿瘤数目,肿瘤大小,大血管浸润和细胞外肝转移。在训练队列中,列线图的C指数为0.813(95%CI,0.789-0.837),在验证队列中为0.794(95%CI,0.756-0.832)。预测生存概率的校准曲线表明,诺模图与随访数据具有高度一致性。新型系统的C指数高于其他常规分期系统(P <0.001)。结论从本研究的前瞻性收集数据中得出的新型基于炎症的诺模图可预测HCC患者的OS。

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