Development and validation of a model to predict tyrosine kinase inhibitor-sensitive EGFR mutations of non-small cell lung cancer based on multi-institutional data.
Development and validation of a model to predict tyrosine kinase inhibitor-sensitive EGFR mutations of non-small cell lung cancer based on multi-institutional data.
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DOI：
10.1111/1759-7714.12881
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发表时间：
2018-12
期刊：
影响因子：
2.9
通讯作者：
Zhang JX
中科院分区：
文献类型：
--
作者：
Chang H;Liu YB;Yi W;Lu JB;Zhang JX
具有不同EGFR突变类型的非小细胞肺癌(NSCLC)对酪氨酸激酶抑制剂(TKIs)表现出明显的敏感性。这项研究开发了一个基于病理-临床特征的TKI敏感的EGFR突变预测模型。本文回顾了2008年11月至2014年10月1121例中国非小细胞肺癌患者的临床资料。多因素Logistic回归分析确定潜在预测因素与经典的敏感EGFR突变(外显子19缺失和外显子21 L858R点突变)之间的关联。通过将加权分数分配给与回归系数成比例的每个因素来创建预测指数。验证在由864名患者组成的独立队列中进行，这些患者在2014年11月至2017年1月期间连续入选(验证集)。确定了7个独立的预测因素：性别(女性与男性)、腺癌(是与否)、吸烟史(否与是)、N分期(N+与N0)、M期(M1与M0)、脑转移(是与否)以及Cyfra 21-1升高(否与是)。每个人都被分配了一些分数。在验证集中，预测指标的曲线下面积为0.698(95%可信区间0.663-0.733)。其敏感性为95.0%，特异性为32.3%，阳性预测值为61.4%，阴性预测值为85.1%，符合率为65.6%。我们开发了一个基于病理-临床特征的模型来预测TKI敏感的EGFR突变。我们的模型可能代表了临床医生告知TKI治疗的一种非侵入性、经济的选择。
Non‐small cell lung cancer (NSCLC) with different EGFR mutation types shows distinct sensitivity to tyrosine kinase inhibitors (TKIs). This study developed a patho‐clinical profile‐based prediction model of TKI‐sensitive EGFR mutations. The records of 1121 Chinese patients diagnosed with NSCLC from November 2008 to October 2014 (the development set) were reviewed. Multivariate logistic regression was conducted to identify any association between potential predictors and the classic sensitive EGFR mutations (exon 19 deletion and exon 21 L858R point mutation). A prediction index was created by assigning weighted scores to each factor proportional to a regression coefficient. Validation was made in an independent cohort consisting of 864 patients who were consecutively enrolled between November 2014 and January 2017 (the validation set). Seven independent predictors were identified: gender (female vs. male), adenocarcinoma (yes vs. no), smoking history (no vs. yes), N stage (N+ vs. N0), M stage (M1 vs. M0), brain metastasis (yes vs. no), and elevated Cyfra 21‐1 (no vs. yes). Each was assigned a number of points. In the validation set, the area under curve of the prediction index appeared as 0.698 (95% confidence interval 0.663–0.733). The sensitivity, specificity, positive and negative predictive values, and concordance were 95.0%, 32.3%, 61.4%, 85.1%, and 65.6%, respectively. We developed a patho‐clinical profile‐based model for predicting TKI‐sensitive EGFR mutations. Our model may represent a noninvasive, economical choice for clinicians to inform TKI therapy.
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影响因子：
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作者：
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影响因子：
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