Establishment and evaluation of a novel biomarker-based nomogram for malignant phaeochromocytomas and paragangliomas
Establishment and evaluation of a novel biomarker-based nomogram for malignant phaeochromocytomas and paragangliomas
复制标题
恶性嗜铬细胞瘤和副神经节瘤新型基于生物标志物列线图的建立和评估
DOI：
10.1111/cen.13357
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发表时间：
2017
影响因子：
3.2
通讯作者：
Wang Weiqing
中科院分区：
文献类型：
--
作者：
Zhong Xu;Ye Lei;Su TingWei;Xie Jing;Zhou Weiwei;Jiang Yiran;Jiang Lei;Ning Guang;Wang Weiqing
目的没有单一的组织学或分子标志物可以诊断恶性嗜铬细胞瘤和副神经节瘤（PPGL）。本研究旨在建立和评估预后列线图，以提高个体PPGL患者转移概率的预测。方法将2002年1月至2014年12月连续的30047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变量逻辑回归分析，并构建了预测转移的列线图。采用辨别和校准来评估列线图的性能。使用决策曲线分析计算临床有用性。结果总体转移率为10.6%。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在逻辑分析中与恶性肿瘤相关，并包含在列线图中。列线图显示训练集中受试者工作特征曲线 (AUC) 下的面积为 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，0.803‐0.921）。列线图经过良好校准，预测概率和观测概率之间没有显着差异（Hosmer-Lemeshow 检验：训练集 P=.510；验证集 P=.314）。决策曲线分析显示，分子标志物（ERBB-2过表达和SDHB突变）可以增加列线图的临床益处。结论我们的结果支持使用现有的基于生物标志物的列线图来预测PPGL的转移概率，该列线图具有良好的辨别能力。
ObjectiveNo 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.MethodsThree 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.ResultsThe 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.ConclusionOur results support the use of the present biomarker‐based nomogram, which has good discriminative ability, to predict the metastatic probability of PPGLs.