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Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer
Zhang, Liwen1,2; Chen, Bojiang3; Liu, Xia1; Song, Jiangdian4; Fang, Mengjie2; Hu, Chaoen2; Dong, Di2,5; Li, Weimin3; Tian, Jie2,5
AbstractOBJECTIVES: 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.
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationNational Key R&D Program of China(2017YFC1308700 ; National Natural Science Foundation of China(81227901 ; Natural Science Foundation of Heilongjiang Province(F201311 ; special program for science and technology development from the Ministry of science and technology, China(2016CZYD0001) ; Science and Technology Service Network Initiative of the Chinese Academy of Sciences(KFJ-SW-STS-160) ; Instrument Developing Project(YZ201502) ; Beijing Municipal Science and Technology Commission(Z161100002616022) ; Key Program from the Department of Science and Technology, Sichuan Province, China(2017SZ0052) ; Youth Innovation Promotion Association CAS ; 2017YFA0205200 ; 81771924 ; 12541105) ; 2017YFC1308701 ; 81671851 ; 2017YFC1309100) ; 81527805 ; 61231004 ; 61672197 ; 81501616)
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000423454900012
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Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Heilongjiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Sichuan Univ, West China Hosp, Dept Resp & Crit Care Med, Chengdu 610041, Sichuan, Peoples R China
4.China Med Univ, Sch Med Informat, Shenyang 110122, Liaoning, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Liwen,Chen, Bojiang,Liu, Xia,et al. Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer[J]. TRANSLATIONAL ONCOLOGY,2018,11(1):94-101.
APA Zhang, Liwen.,Chen, Bojiang.,Liu, Xia.,Song, Jiangdian.,Fang, Mengjie.,...&Tian, Jie.(2018).Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer.TRANSLATIONAL ONCOLOGY,11(1),94-101.
MLA Zhang, Liwen,et al."Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer".TRANSLATIONAL ONCOLOGY 11.1(2018):94-101.
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