PREDICTION OF EGFR MUTATION STATUS IN LUNG ADENOCARCINOMA USING MULTI-SOURCE FEATURE REPRESENTATIONS

被引:5
|
作者
Cheng, Jianhong [1 ,3 ]
Liu, Jin [1 ]
Jiang, Meilin [2 ]
Yue, Hailin [1 ]
Wu, Lin [2 ]
Wang, Jianxin [1 ]
机构
[1] Cent South Univ, Sch Comp Sci & Engn, Hunan Prov Key Lab Bioinformat, Changsha 410083, Peoples R China
[2] Cent South Univ, Xiangya Sch Med, Affiliated Canc Hosp, Hunan Canc Hosp,Dept Thorac Oncol 2, Changsha 410083, Peoples R China
[3] Inst Guizhou Aerosp Measuring & Testing Technol, Guiyang 550009, Peoples R China
基金
中国国家自然科学基金;
关键词
Lung adenocarcinoma; EGFR mutation; Deep learning; Feature representations;
D O I
10.1109/ICASSP39728.2021.9414064
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Epidermal growth factor receptor (EGFR) genotyping is essential to treatment guidelines for the use of tyrosine kinase inhibitors in lung adenocarcinoma. However, accurate and noninvasive methods to detect the EGFR gene are ongoing challenges. In this study, we propose a hybrid framework, namely HC-DLR, to noninvasively predict EGFR mutation status by fusing multi-source features including low-level handcrafted radiomics (HCR) features, high-level deep learning-based radiomics (DLR) features, and demographics features. The HCR features first are selected from massive handcrafted features extracted from CT images. The DLR features are also extracted from CT images using the pre-trained 3D DenseNet. Then, multi-source feature representations are refined and fused to build an HC-DLR model for improving the predictive performance of EGFR mutations. The proposed method is evaluated on a newly collected dataset with 670 patients. Experimental results show that the HC-DLR model achieves an encouraging predictive performance with an AUC of 0.76, an accuracy of 72.47%, and an F1-score of 71.35%, which may have potential clinical value for predicting EGFR mutations in lung adenocarcinoma.
引用
收藏
页码:1350 / 1354
页数:5
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