CT radiomics signatures for prediction of epidermal growth factor receptor sensitive mutation in lung adenocarcinoma
10.13929/j.1672-8475.201809026
- Author:
Lei XIAO
1
Author Information
1. Department of Radiology, First Affiliated Hospital of Hunan University of Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Adenocarcinoma;
Lung neoplasms;
Radiomics;
Receptor, epidermal growth factor;
Tomography, X-ray computed
- From:
Chinese Journal of Interventional Imaging and Therapy
2019;16(4):220-224
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish radiomics signatures based on non-enhanced CT image features, and to evaluate their feasibility for prediction epidermal growth factor receptor (EGFR) sensitive mutation of lung adenocarcinoma. Methods Eighty lung adenocarcinoma patients were divided into EGFR sensitive group (n=37) and EGFR insensitive (n=43) group according to EGFR mutation status. Radiomics features and subjective image features were collected from non-enhanced CT images. LASSO regression model was used to select radiomics features. Subjective image features model, radiomics model and combined diagnostic model were developed with multiple factors Logistic models, respectively. The predictive performance of EGFR sensitive mutation of each model was evaluated with ROC curve. Results There was no significant difference of subjective CT image features between EGFR sensitive and insensitive group (all P>0.05). Through feature selection, 4 radiomics features were enrolled. Subjective CT image features model (AUC=0.66), radiomics model (AUC=0.77) and combined diagnostic model (AUC=0.83) had statistically significant differences in the performance of predicting EGFR sensitive mutation (all P<0.05). The combined diagnostic model had the best predictive efficiency. Conclusion Radiomics signatures based on non-enhanced CT images can be used to predict EGFR sensitive mutation in lung adenocarcinoma.
