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基于新型生物标志物的恶性嗜铬细胞瘤和副神经节瘤列线图的建立与评估

Establishment and evaluation of a novel biomarker-based nomogram for malignant phaeochromocytomas and paragangliomas.

作者信息

Zhong Xu, Ye Lei, Su TingWei, Xie Jing, Zhou Weiwei, Jiang Yiran, Jiang Lei, Ning Guang, Wang Weiqing

机构信息

Shanghai Key Laboratory for Endocrine Tumors, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of Chinese Health Ministry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Laboratory for Endocrine & Metabolic Diseases of Institute of Health Science, Shanghai Jiao Tong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

出版信息

Clin Endocrinol (Oxf). 2017 Aug;87(2):127-135. doi: 10.1111/cen.13357. Epub 2017 May 28.

Abstract

OBJECTIVE

No single histological or molecular marker is diagnostic for malignant phaeochromocytomas and paragangliomas (PPGLs). This study aimed to establish and evaluate a prognostic nomogram to improve the prediction of metastatic probability in individual PPGL patients.

METHODS

Three hundred and 47 consecutive PPGL patients from January 2002 through December 2014 were randomly divided into a training set (n=208) and a validation set (n=139). A multivariate logistic regression analysis of selected prognostic features was performed, and a nomogram to predict metastasis was constructed. Discrimination and calibration were employed to evaluate the performance of the nomogram. Clinical usefulness was calculated using decision curve analysis.

RESULTS

The overall metastatic rate was 10.6%. Primary tumour size, primary tumour location, vascular invasion, ERBB-2 overexpression, SDHB mutation and catecholamine type were associated with malignancy in the logistic analysis and were included in the nomogram. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.872 (95% confidence interval [CI], 0.819-0.914) in the training set. The validation set showed good discrimination, with an AUC of 0.870 (95% CI, 0.803-0.921). The nomogram was well calibrated, with no significant difference between the predicted and the observed probabilities (Hosmer-Lemeshow test: P=.510 for the training set; .314 for the validation set). Decision curve analysis revealed that molecular markers (ERBB-2 overexpression and SDHB mutation) could increase the clinical benefit of the nomogram.

CONCLUSION

Our results support the use of the present biomarker-based nomogram, which has good discriminative ability, to predict the metastatic probability of PPGLs.

摘要

目的

目前尚无单一的组织学或分子标志物可用于诊断恶性嗜铬细胞瘤和副神经节瘤(PPGLs)。本研究旨在建立并评估一种预后列线图,以改善对个体PPGL患者转移概率的预测。

方法

选取2002年1月至2014年12月期间连续收治的347例PPGL患者,随机分为训练集(n = 208)和验证集(n = 139)。对选定的预后特征进行多因素逻辑回归分析,并构建预测转移的列线图。采用区分度和校准度评估列线图的性能。使用决策曲线分析计算临床实用性。

结果

总体转移率为10.6%。在逻辑分析中,原发肿瘤大小、原发肿瘤位置、血管侵犯、ERBB-2过表达、SDHB突变和儿茶酚胺类型与恶性肿瘤相关,并纳入列线图。该列线图在训练集中的受试者操作特征曲线(AUC)下面积为0.872(95%置信区间[CI],0.819 - 0.914)。验证集显示出良好的区分度,AUC为0.870(95% CI,0.80

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