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Quantitative Biomarkers for Prediction of Epidermal Growth Factor Receptor Mutation in Non-Small Cell Lung Cancer

文献类型：期刊论文

作者Zhang, Liwen1,2; Chen, Bojiang3; Liu, Xia1; Song, Jiangdian4; Fang, Mengjie2; Hu, Chaoen2; Dong, Di2,5; Li, Weimin3; Tian, Jie2,5
刊名TRANSLATIONAL ONCOLOGY
出版日期2018-02-01
卷号11期号:1页码:94-101
关键词Radiomics
DOI10.1016/j.tranon.2017.10.012
文献子类Article
英文摘要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.
WOS关键词PREDOMINANT HISTOLOGIC SUBTYPE ; EGFR MUTATIONS ; ADENOCARCINOMA CLASSIFICATION ; 1ST-LINE TREATMENT ; ASIAN PATIENTS ; OPEN-LABEL ; GEFITINIB ; AFATINIB ; FEATURES ; TRIAL
WOS研究方向Oncology
语种英语
WOS记录号WOS:000423454900012
资助机构National 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)
源URL[http://ir.ia.ac.cn/handle/173211/20307]  
专题自动化研究所_中国科学院分子影像重点实验室
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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