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

被引:71
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
[11]   Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients [J].
Tunali, Ilke ;
Stringfield, Olya ;
Guvenis, Albert ;
Wang, Hua ;
Liu, Ying ;
Balagurunathan, Yoganand ;
Lambin, Philippe ;
Gillies, Robert J. ;
Schabath, Matthew B. .
ONCOTARGET, 2017, 8 (56) :96013-96026
[12]   Using Multi-phase CT Radiomics Features to Predict EGFR Mutation Status in Lung Adenocarcinoma Patients [J].
Zhang, Guojin ;
Man, Qiong ;
Shang, Lan ;
Zhang, Jing ;
Cao, Yuntai ;
Li, Shenglin ;
Qian, Rong ;
Ren, Jialiang ;
Pu, Hong ;
Zhou, Junlin ;
Zhang, Zhuoli ;
Kong, Weifang .
ACADEMIC RADIOLOGY, 2024, 31 (06) :2591-2600
[13]   Contrast-Enhanced CT Parameters of Gastric Adenocarcinoma: Can Radiomic Features Be Surrogate Biomarkers for HER2 Over-Expression Status? [J].
Wang, Na ;
Wang, Xinxin ;
Li, Wenya ;
Ye, Huajun ;
Bai, Hongzhao ;
Wu, Jiansheng ;
Chen, Mengjun .
CANCER MANAGEMENT AND RESEARCH, 2020, 12 :1211-1219
[14]   Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma [J].
Xiumei Li ;
Jianwei Chen ;
Chengxiu Zhang ;
Zewen Han ;
Xiuying zheng ;
Dairong Cao .
BMC Pulmonary Medicine, 23
[15]   Application value of CT radiomic nomogram in predicting T790M mutation of lung adenocarcinoma [J].
Li, Xiumei ;
Chen, Jianwei ;
Zhang, Chengxiu ;
Han, Zewen ;
Zheng, Xiuying ;
Cao, Dairong .
BMC PULMONARY MEDICINE, 2023, 23 (01)
[16]   Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT [J].
Yoon, Hyun Jung ;
Choi, Jieun ;
Kim, Eunjin ;
Um, Sang-Won ;
Kang, Noeul ;
Kim, Wook ;
Kim, Geena ;
Park, Hyunjin ;
Lee, Ho Yun .
FRONTIERS IN ONCOLOGY, 2022, 12
[17]   Value of multi-center 18F-FDG PET/CT radiomics in predicting EGFR mutation status in lung adenocarcinoma [J].
Zuo, Yan ;
Liu, Liu ;
Chang, Cheng ;
Yan, Hui ;
Wang, Lihua ;
Sun, Dazhen ;
Ruan, Maomei ;
Lei, Bei ;
Xia, Xunpeng ;
Xie, Wenhui ;
Song, Shaoli ;
Huang, Gang .
MEDICAL PHYSICS, 2024, 51 (07) :4872-4887
[18]   A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma [J].
Xing, Xiaowei ;
Li, Liangping ;
Sun, Mingxia ;
Zhu, Xinhai ;
Feng, Yue .
THERAPEUTIC ADVANCES IN RESPIRATORY DISEASE, 2024, 18
[19]   Relationship between EGFR gene mutation and serum tumor biomarkers in advanced lung adenocarcinoma [J].
Feng, Yuan ;
Zhou, Jihong ;
Chen, Sining ;
Jiang, Ying ;
Zhou, Ying .
INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY, 2016, 9 (01) :250-255
[20]   Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis [J].
Yao, Guangyu ;
Zhou, Yiyi ;
Gu, Yifeng ;
Wang, Zhiyu ;
Yang, Mengdi ;
Sun, Jing ;
Luo, Quanyong ;
Zhao, Hui .
JOURNAL OF CANCER, 2020, 11 (18) :5511-5517