Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer.
Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer.
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用于预测非小细胞肺癌表皮生长因子受体突变的定量生物标志物
DOI：
10.1016/j.tranon.2017.10.012
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发表时间：
2018-03
影响因子：
5
通讯作者：
Tian J
中科院分区：
文献类型：
--
作者：
Zhang L;Chen B;Liu X;Song J;Fang M;Hu C;Dong D;Li W;Tian J
目的：应用定量放射性生物标志物和具有代表性的临床变量预测表皮生长因子受体(EGFR)突变状态。方法：对180例非小细胞肺癌(NSCLC)患者进行治疗前CT扫描。使用放射组学方法，提取了485个反映肿瘤异质性和表型的特征。然后，将这些放射组学特征用基于多变量Logistic回归的最小绝对收缩和选择算子(LASSO)用于预测表皮生长因子受体(EGFR)突变状态。因此，我们发现放射组学特征在预测EGFR突变状态方面具有预测能力。此外，我们还使用放射学诺模图和校准曲线对模型的性能进行了检验。结果：多因素分析显示，放射组学特征有可能建立EGFR突变的预测模型。训练队列的受试者工作特征曲线下面积为0.8618，验证队列的受试者工作特征曲线下面积为0.8725，均优于单纯使用临床变量的预测模型。结论：与传统的语义CT图像特征或临床变量相比，放射学特征能更好地预测EGFR突变状态，以帮助医生决定谁需要EGFR酪氨酸激酶抑制剂(TKI)治疗。
OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.
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影响因子：
2
作者：
Paul, Prabasaj;Pennell, Michael L.;Lemeshow, Stanley
通讯作者：
Lemeshow, Stanley
影响因子：
51.1
作者：
Park, Keunchil;Tan, Eng-Huat;Paz-Ares, Luis
通讯作者：
Paz-Ares, Luis
DOI：
10.1007/s00432-007-0320-z
发表时间：
2008-05-01
影响因子：
3.6
作者：
Sasaki, Hidefumi;Endo, Katsuhiko;Fujii, Yoshitaka
通讯作者：
Fujii, Yoshitaka
影响因子：
20.4
作者：
Russell, Prudence A.;Barnett, Stephen A.;John, Thomas
通讯作者：
John, Thomas
影响因子：
4.6
作者：
Villa, Celina;Cagle, Philip T.;Raparia, Kirtee
通讯作者：
Raparia, Kirtee