Application of radiomics captured from CT to predict the EGFR mutation status and TKIs therapeutic sensitivity of advanced lung adenocarcinoma
10.3760/cma.j.issn.0253-3766.2019.04.007
- VernacularTitle: CT影像组学特征预测晚期肺腺癌表皮生长因子受体突变状态及表皮生长因子受体酪氨酸激酶抑制剂治疗敏感性的效能
- Author:
Chunsheng YANG
1
;
Weidong CHEN
1
;
Guanzhong GONG
2
;
Zhenjiang LI
2
;
Qingtao QIU
2
;
Yong YIN
2
Author Information
1. Department of Oncology, Jining First People′s Hospital, Jining 272000, China
2. Department of Radiophysical Technology, Shandong Cancer Hospital, Jinan 250117, China
- Publication Type:Clinical Trail
- Keywords:
Lung neoplasms;
Radiomics;
Epidermal growth factor receptor;
Epidermal growth factor receptor-tyrosine kinase inhibitors
- From:
Chinese Journal of Oncology
2019;41(4):282-287
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the ability of computed-tomography (CT) radiomic features to predict the Epidermal growth factor receptor (EGFR) mutation status and the therapeutic response of advanced lung adenocarcinoma to EGFR- Tyrosine kinase inhibitors (TKIs) treatment.
Methods:A retrospective analysis was performed on 253 patients diagnosed as advanced lung adenocarcinoma, who underwent EGFR mutation detection, and those with EGFR sensitive mutation were treated with TKIs. Using the Lasso regression model and the 10 fold cross-validation method, the radiomic features of predicted EGFR mutation status and the screening of TKIs for sensitive populations were obtained. 715 radiomic features were extracted from unenhanced, arterial phase and venous phase, respectively.
Results:The area under curve (AUC) values of the multi-phases including unenhanced, arterial phase and venous phase of the EGFR mutation status validation group were 0.763, 0.807 and 0.808, respectively. The number of radiomic features extracted from the multi-phases were 5, 18 and 23, respectively, which could distinguish the EGFR mutation status. The AUC values of the multi-phases of the EGFR-TKIs sensitive validation group were 0.730, 0.833 and 0.895, respectively. The number of radiomic features extracted from the multi-phases were 3, 7 and 22, respectively, which can be used to screen the superior population for TKIs treatment. The efficiency of radiomic features extracted from venous phase in predicting EGFR mutant status and EGFR-TKIs sensitivity was significantly superior than those of unenhanced and arterial phase.
Conclusions:The radiomic features of CT scanning can be used as the radiomics biomarker to predict the EGFR mutation status of lung adenocarcinoma and to further screen the dominant population in TKIs therapy, which provides the basis for targeted therapy.
