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一种用于鉴别肝内胆管癌与肝细胞癌的高效列线图:一项回顾性研究

An Efficient Nomogram for Discriminating Intrahepatic Cholangiocarcinoma From Hepatocellular Carcinoma: A Retrospective Study.

作者信息

Si Yuan-Quan, Wang Xiu-Qin, Pan Cui-Cui, Wang Yong, Lu Zhi-Ming

机构信息

Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

School of Basic Medicine, Shandong First Medical University, Jinan, China.

出版信息

Front Oncol. 2022 Apr 11;12:833999. doi: 10.3389/fonc.2022.833999. eCollection 2022.

Abstract

OBJECTIVE

This study aims to establish a nomogram and provide an effective method to distinguish between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC).

METHODS

A total of 1,591 patients with HCC or ICC hospitalized at Shandong Provincial Hospital between January 2016 and August 2021 were included and randomly divided into development and validation groups in a ratio of 3:1. Univariate and multivariate analyses were performed to determine the independent differential factors between HCC and ICC patients in the development cohort. By combining these independent differential factors, the nomogram was established for discriminating ICC from HCC. The accuracy of the nomogram was estimated by using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Furthermore, the predictive nomogram was assessed in the internal testing set.

RESULTS

Through multivariate analysis, independent differential factors between HCC and ICC involved hepatitis B virus (HBV), logarithm of alpha-fetoprotein (Log AFP), logarithm of protein induced by vitamin K absence or antagonist-II (Log PIVKA-II), logarithm of carbohydrate antigen 199 (Log CA199), and logarithm of carbohydrate antigen 125 (Log CA125). A nomogram was finally established by incorporating these five independent differential factors. Comparing a model of conventional tumor biomarkers including AFP and CA199, the nomogram showed a better distinction between ICC and HCC. The area under the ROC curve (AUC) of ICC diagnosis was 0.951 (95% CI, 0.938-0.964) for the nomogram. The results were consistent in the validation cohort with an AUC of 0.958 (95% CI, 0.938-0.978). After integrating patient preferences into the analysis, the DCA showed that using this nomogram to distinguish ICC and HCC increased more benefit compared with the conventional model.

CONCLUSION

An efficient nomogram has been established for the differential diagnosis between ICC and HCC, which may facilitate the detection and diagnosis of ICC. Further use of the nomogram in multicenter investigations will confirm the practicality of the tool for future clinical application.

摘要

目的

本研究旨在建立一种列线图,并提供一种区分肝内胆管癌(ICC)和肝细胞癌(HCC)的有效方法。

方法

纳入2016年1月至2021年8月在山东省立医院住院的1591例HCC或ICC患者,并按3:1的比例随机分为训练组和验证组。在训练队列中进行单因素和多因素分析,以确定HCC和ICC患者之间的独立鉴别因素。通过综合这些独立鉴别因素,建立用于区分ICC和HCC的列线图。采用受试者工作特征(ROC)曲线和决策曲线分析(DCA)评估列线图的准确性。此外,在内部测试集中评估预测列线图。

结果

通过多因素分析,HCC和ICC之间的独立鉴别因素包括乙型肝炎病毒(HBV)、甲胎蛋白对数(Log AFP)、维生素K缺乏或拮抗剂-II诱导蛋白对数(Log PIVKA-II)、糖类抗原199对数(Log CA-199)和糖类抗原125对数(Log CA-125)。最终纳入这五个独立鉴别因素建立了列线图。与包括AFP和CA199在内的传统肿瘤生物标志物模型相比,列线图在区分ICC和HCC方面表现更佳。该列线图诊断ICC时的ROC曲线下面积(AUC)为0.951(95%CI,0.938-0.964)。验证队列中的结果一致,AUC为0.958(95%CI,0.938-0.978)。将患者偏好纳入分析后,DCA显示使用该列线图区分ICC和HCC比传统模型增加了更多益处。

结论

已建立一种用于ICC和HCC鉴别诊断的有效列线图,这可能有助于ICC的检测和诊断。该列线图在多中心研究中的进一步应用将证实该工具在未来临床应用中的实用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bba5/9035637/9d385f0ae08b/fonc-12-833999-g001.jpg

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