Title: Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer
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| Authors: Zhang, LW; Chen, BJ; Liu, X; Song, JD; Fang, MJ; Hu, CE; Dong, D; Li, WM; Tian, J
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| Author Full Names: Zhang, Liwen; Chen, Bojiang; Liu, Xia; Song, Jiangdian; Fang, Mengjie; Hu, Chaoen; Dong, Di; Li, Weimin; Tian, Jie
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| Source: TRANSLATIONAL ONCOLOGY, 11 (1):94-101; 10.1016/j.tranon.2017.10.012 FEB 2018
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| Language: English
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| Abstract: 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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| ISSN: 1936-5233
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| IDS Number: FT9EM
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| Unique ID: WOS:000423454900012
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