Nomogram to predict the presence of EGFR activating mutation in lung adenocarcinoma
Nomogram to predict the presence of EGFR activating mutation in lung adenocarcinoma
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DOI：
10.1183/09031936.00010111
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发表时间：
2012-02-01
影响因子：
24.3
通讯作者：
Pao, W.
中科院分区：
文献类型：
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作者：
Girard, N.;Sima, C. S.;Pao, W.
表皮生长因子受体（EGFR）肿瘤基因分型对于指导非小细胞肺癌（NSCLC）中EGFR酪氨酸激酶抑制剂的使用至关重要。然而，一些患者可能无法获得肿瘤检测，因为组织有限和/或测试不定期提供。在这里，我们的目的是建立一个基于模型的诺模图，以预测EGFR突变在NSCLC中的存在。我们回顾性地收集了3,006例NSCLC患者的临床和病理数据，这些患者在全球5家机构进行了EGFR突变的肿瘤基因分型。在2,392例非亚裔肺腺癌患者中，携带EGFR突变的最重要预测因子为：降低吸烟暴露（OR 0.41，95% CI 0.37-0.46），戒烟与诊断之间的时间间隔较长（OR 2.19，95% CI 1.71-2.80）、晚期（OR 1.58，95% CI 1.18-2.13）和乳头状（OR 4.57，95% CI 3.14-6.66）或细支气管肺泡（OR 2.84，95% CI 1.98-4.06）组织学上占优势的亚型。建立了一个列线图，并显示出良好的区分准确性：独立验证数据集的一致性指数为0.84。随着临床实践过渡到将基因分型作为常规护理的一部分，当突变谱不可用或不可能时，该列线图可能非常有用地预测非亚洲肺腺癌患者中EGFR突变的存在。
Epidermal growth factor receptor (EGFR) tumour genotyping is crucial to guide treatment decisions regarding the use of EGFR tyrosine kinase inhibitors in nonsmall cell lung cancer (NSCLC). However, some patients may not be able to obtain tumour testing, either because tissue is limited and/or tests are not routinely offered. Here, we aimed to build a model-based nomogram to allow for prediction of the presence of EGFR mutations in NSCLC.We retrospectively collected clinical and pathological data on 3,006 patients with NSCLC who had their tumours genotyped for EGFR mutations at five institutions worldwide. Variables of interest were integrated in a multivariate logistic regression model.In the 2,392 non-Asian patients with lung adenocarcinomas, the most important predictors of harbouring EGFR mutation were: lower tobacco smoking exposure (OR 0.41, 95% CI 0.37-0.46), longer time interval between smoking cessation and diagnosis (OR 2.19, 95% CI 1.71-2.80), advanced stage (OR 1.58, 95% CI 1.18-2.13), and papillary (OR 4.57, 95% CI 3.14-6.66) or bronchioloalveolar (OR 2.84, 95% CI 1.98-4.06) histologically predominant subtype. A nomogram was established and showed excellent discriminating accuracy: the concordance index on an independent validation dataset was 0.84.As clinical practices transition to incorporating genotyping as part of routine care, this nomogram could be highly useful to predict the presence of EGFR mutations in lung adenocarcinoma in non-Asian patients when mutational profiling is not available or possible.