CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses

被引:69
作者
Mei, Dongdong [1 ]
Luo, Yan [1 ]
Wang, Yan [2 ]
Gong, Jingshan [1 ]
机构
[1] Jinan Univ, Shenzhen Peoples Hosp, Dept Radiol, Clin Med Coll 2, Shenzhen 518020, Guangdong, Peoples R China
[2] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, 185 Berry St,Suite 350, San Francisco, CA 94107 USA
关键词
Lung adenocarcinoma; Computed tomography; Radiomics; Epidermal growth factor receptor; GROWTH-FACTOR-RECEPTOR; EXON-21; MUTATIONS; CANCER PATIENTS; GEFITINIB; SURVIVAL; IMAGES;
D O I
10.1186/s40644-018-0184-2
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
ObjectiveTo investigate whether radiomic features can be surrogate biomarkers for epidermal growth factor receptor (EGFR) mutation statuses.Materials and methodsTwo hundred ninety six consecutive patients, who underwent CT examinations before operation within 3months and had EGFR mutations tested, were enrolled in this retrospective study. CT texture features were extracted using an open-source software with whole volume segmentation. The association between CT texture features and EGFR mutation statuses were analyzed.ResultsIn the 296 patients, there were 151 patients with EGFR mutations (51%). Logistic analysis identified that lower age (Odds Ratio[OR]: 0.968,95% confidence interval [CI]:0.946 similar to 0.990, p=0.005) and a radiomic feature named GreyLevelNonuniformityNormalized (OR: 0.012, 95% CI:0.000 similar to 0.352, p=0.01) were predictors for exon 19 mutation; higher age (OR: 1.027, 95%CI:1.003 similar to 1.052,p=0.025), female sex (OR: 2.189, 95%CI:1.264 similar to 3.791, p=0.005) and a radiomic feature named Maximum2DDiameterColumn (OR: 0.968, 95%CI:0.946 similar to 0.990], p=0.005) for exon 21 mutation; and female sex (OR: 1.883,95%CI:1.064 similar to 3.329, p=0.030), non-smoking status (OR: 2.070, 95%CI:1.090 similar to 3.929, p=0.026) and a radiomic feature termed SizeZone NonUniformityNormalized (OR: 0.010, 95% CI:0.0001 similar to 0.852, p=0.042) for EGFR mutations. Areas under the curve (AUCs) of combination with clinical and radiomic features to predict exon 19 mutation, exon 21 mutation and EGFR mutations were 0.655, 0.675 and 0.664, respectively.ConclusionSeveral radiomic features are associated with EGFR mutation statuses of lung adenocarcinoma. Combination with clinical files, moderate diagnostic performance can be obtained to predict EGFR mutation status of lung adenocarcinoma. Radiomic features might harbor potential surrogate biomarkers for identification of EGRF mutation statuses.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features
    Yi, Li
    Peng, Zhiwei
    Chen, Zhiyong
    Tao, Yahong
    Lin, Ze
    He, Anjing
    Jin, Mengni
    Peng, Yun
    Zhong, Yufeng
    Yan, Huifeng
    Zuo, Minjing
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [32] Predicting PD-L1 in Lung Adenocarcinoma Using 18F-FDG PET/CT Radiomic Features
    Zhang, Huiyuan
    Meng, Xiangxi
    Wang, Zhe
    Zhou, Xin
    Liu, Yang
    Li, Nan
    [J]. DIAGNOSTICS, 2025, 15 (05)
  • [33] The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma
    Gao, Jianxiong
    Niu, Rong
    Shi, Yunmei
    Shao, Xiaoliang
    Jiang, Zhenxing
    Ge, Xinyu
    Wang, Yuetao
    Shao, Xiaonan
    [J]. EJNMMI RESEARCH, 2023, 13 (01)
  • [34] The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma
    Jianxiong Gao
    Rong Niu
    Yunmei Shi
    Xiaoliang Shao
    Zhenxing Jiang
    Xinyu Ge
    Yuetao Wang
    Xiaonan Shao
    [J]. EJNMMI Research, 13
  • [35] Can texture features improve the differentiation of infiltrative lung adenocarcinoma appearing as ground glass nodules in contrast-enhanced CT?
    Gao, Chen
    Xiang, Ping
    Ye, Jianfeng
    Pang, Peipei
    Wang, Shiwei
    Xu, Maosheng
    [J]. EUROPEAN JOURNAL OF RADIOLOGY, 2019, 117 : 126 - 131
  • [36] The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors
    Kim, Hyungjin
    Park, Chang Min
    Keam, Bhumsuk
    Park, Sang Joon
    Kim, Miso
    Kim, Tae Min
    Kim, Dong-Wan
    Heo, Dae Seog
    Goo, Jin Mo
    [J]. PLOS ONE, 2017, 12 (11):
  • [37] CT-Imaging Based Analysis of Invasive Lung Adenocarcinoma Presenting as Ground Glass Nodules Using Peri- and Intra-nodular Radiomic Features
    Wu, Linyu
    Gao, Chen
    Xiang, Ping
    Zheng, Sisi
    Pang, Peipei
    Xu, Maosheng
    [J]. FRONTIERS IN ONCOLOGY, 2020, 10
  • [38] In Non-small Cell Lung Cancer, Can Radiomic Features Predict EGFR Mutations?
    Liu, Jiayan
    Liu, Lin
    Ma, Yue
    Xue, Kaiming
    Zhou, Zhe
    Zhang, Mengchao
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA), 2019, : 2180 - 2184
  • [39] CT Texture Analysis for Differentiating Bronchiolar Adenoma, Adenocarcinoma In Situ, and Minimally Invasive Adenocarcinoma of the Lung
    Sun, Jinju
    Liu, Kaijun
    Tong, Haipeng
    Liu, Huan
    Li, Xiaoguang
    Luo, Yi
    Li, Yang
    Yao, Yun
    Jin, Rongbing
    Fang, Jingqin
    Chen, Xiao
    [J]. FRONTIERS IN ONCOLOGY, 2021, 11
  • [40] Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features
    Huang, Zhiwei
    Lyu, Mo
    Ai, Zhu
    Chen, Yirong
    Liang, Yuying
    Xiang, Zhiming
    [J]. FRONTIERS IN SURGERY, 2021, 8