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通过S指数建立的预测原发性肝细胞癌根治性切除术后总生存期的样本模型。

A sample model established by S-index predicting overall survival after curative resection of primary hepatocellular carcinoma.

作者信息

Chen Lei, Cai Bin-Bin, Zhou Chao-Jun, Hou Xiang-Qing, Hu Si-Pin, Fang Guan, Chen Wen-Chao, Li Lin-Hui, Yang Wen-Jun

机构信息

Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, Wenzhou, China,

Department of Statistics, School of Public Health and Management, Wenzhou Medical University, Zhejiang, Wenzhou, China.

出版信息

Cancer Manag Res. 2019 Jan 14;11:693-703. doi: 10.2147/CMAR.S193593. eCollection 2019.

DOI:10.2147/CMAR.S193593
PMID:30679923
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6338126/
Abstract

PURPOSE

Prognostic prediction after curative resection of primary hepatocellular carcinoma (PHCC) remains an arduous task. The S-index calculated from γ-glutamyl transpeptidase, albumin, and platelets is reported to predict the severity of liver fibrosis. We constructed a nomogram for predicting the survival probability of PHCC based on a new indicator, the S-index, combined with other routine clinical parameters.

PATIENTS AND METHODS

We selected 490 patients with PHCC postradical surgery at the First Affiliated Hospital of Wenzhou Medical University between January 2007 and January 2014. The subjects were randomly allocated into the training cohort and the validation cohort in the ratio 7:3 by the digital method. Important variables screened by univariate analysis were included in multivariate analysis to obtain independent risk factors for predicting the prognosis of PHCC. The construction of the nomogram was based on Cox proportional hazard regression models. The concordance index (C-index) was used in the nomogram for evaluating the model performance for prognosis. We drew time-dependent receiver operating characteristic curves to compare our model with other staging systems.

RESULTS

The nomogram based on six independent risk factors after multivariate analyses had good predictive power after radical surgery of PHCC. In the training and validation groups, the C-index of the nomogram was highly consistent for evaluating survival from PHCC. Compared with the traditional scoring system, the areas under time-dependent receiver operating characteristic curves were 0.7382, 0.7293, and 0.7520 for 1-, 3-, and 5-year overall survival, respectively. In summary, the nomogram showed excellent results in terms of prognosis of PHCC.

CONCLUSION

Based on the S-index and the other clinical indicators, we developed a precise nomogram that predicts the survival probability of patients with PHCC after radical surgery. This tool can provide effective information for surgeons and patients.

摘要

目的

原发性肝细胞癌(PHCC)根治性切除术后的预后预测仍然是一项艰巨的任务。据报道,由γ-谷氨酰转肽酶、白蛋白和血小板计算得出的S指数可预测肝纤维化的严重程度。我们基于新指标S指数并结合其他常规临床参数构建了一个预测PHCC生存概率的列线图。

患者与方法

我们选取了2007年1月至2014年1月在温州医科大学附属第一医院接受根治性手术的490例PHCC患者。通过数字法将受试者按7:3的比例随机分为训练队列和验证队列。单因素分析筛选出的重要变量纳入多因素分析,以获得预测PHCC预后的独立危险因素。列线图的构建基于Cox比例风险回归模型。列线图中使用一致性指数(C指数)来评估模型的预后性能。我们绘制了时间依赖性受试者工作特征曲线,以将我们的模型与其他分期系统进行比较。

结果

多因素分析后基于六个独立危险因素构建的列线图在PHCC根治性手术后具有良好的预测能力。在训练组和验证组中,列线图的C指数在评估PHCC生存情况时高度一致。与传统评分系统相比,1年、3年和5年总生存的时间依赖性受试者工作特征曲线下面积分别为0.7382、0.7293和0.7520。总之,列线图在PHCC预后方面显示出优异的结果。

结论

基于S指数和其他临床指标,我们开发了一种精确的列线图,可预测PHCC患者根治性手术后的生存概率。该工具可为外科医生和患者提供有效的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/d15fba369801/cmar-11-693Fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/76266302ccff/cmar-11-693Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/8996658b98b8/cmar-11-693Fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/36ab97248fb3/cmar-11-693Fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/d15fba369801/cmar-11-693Fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/76266302ccff/cmar-11-693Fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/8996658b98b8/cmar-11-693Fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/36ab97248fb3/cmar-11-693Fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/82f4/6338126/d15fba369801/cmar-11-693Fig4.jpg

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