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
  • [1] CT texture analysis of lung adenocarcinoma: can Radiomic features be surrogate biomarkers for EGFR mutation statuses
    Dongdong Mei
    Yan Luo
    Yan Wang
    Jingshan Gong
    Cancer Imaging, 18
  • [2] TEXTURE ANALYSIS PREDICTING EGFR MUTATION AND RECURRENCE IN LUNG ADENOCARCINOMA
    Kang, Lae Hyung
    Kim, Tae Hwa
    Yoon, Seong Hoon
    Kim, Yun Seong
    Kim, Seong-Jang
    RESPIROLOGY, 2018, 23 : 168 - 169
  • [3] EGFR Mutation Status and Subtypes Predicted by CT-Based 3D Radiomic Features in Lung Adenocarcinoma
    Chen, Quan
    Li, Yan
    Cheng, Qiguang
    Van Valkenburgh, Juno
    Sun, Xiaotian
    Zheng, Chuansheng
    Zhang, Ruiguang
    Yuan, Rong
    ONCOTARGETS AND THERAPY, 2022, 15 : 597 - 608
  • [4] Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features
    Liu, Qiufang
    Sun, Dazhen
    Li, Nan
    Kim, Jinman
    Feng, Dagan
    Huang, Gang
    Wang, Lisheng
    Song, Shaoli
    TRANSLATIONAL LUNG CANCER RESEARCH, 2020, 9 (03) : 549 - +
  • [5] Prediction of EGFR mutation status in lung adenocarcinoma based on 18F-FDG PET/CT radiomic features
    Tan, Jian-Ling
    Xia, Liang
    Sun, Su-Guang
    Zeng, Hui
    Lu, Di-Yu
    Cheng, Xiao-Jie
    AMERICAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2023, 13 (05): : 230 - +
  • [6] CT texture features of lung adenocarcinoma with HER2 mutation
    Chen, Wufei
    Gao, Pan
    Lu, Fang
    Wang, Ernuo
    Liu, Haiquan
    Li, Ming
    BMC CANCER, 2025, 25 (01)
  • [7] Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status?
    Digumarthy, Subba R.
    Padole, Atul M.
    Lo Gullo, Roberto
    Sequist, Lecia V.
    Kalra, Mannudeep K.
    MEDICINE, 2019, 98 (01) : E13963
  • [8] Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas
    Liu, Ying
    Kim, Jongphil
    Balagurunathan, Yoganand
    Li, Qian
    Garcia, Alberto L.
    Stringfield, Olya
    Ye, Zhaoxiang
    Gillies, Robert J.
    CLINICAL LUNG CANCER, 2016, 17 (05) : 441 - 448
  • [9] Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates
    Sacconi, B.
    Anzidei, M.
    Leonardi, A.
    Boni, F.
    Saba, L.
    Scipione, R.
    Anile, M.
    Rengo, M.
    Longo, F.
    Bezzi, M.
    Venuta, F.
    Napoli, A.
    Laghi, A.
    Catalano, C.
    CLINICAL RADIOLOGY, 2017, 72 (06) : 443 - 450
  • [10] Contrast-Enhanced CT Parameters of Gastric Adenocarcinoma: Can Radiomic Features Be Surrogate Biomarkers for HER2 Over-Expression Status?
    Wang, Na
    Wang, Xinxin
    Li, Wenya
    Ye, Huajun
    Bai, Hongzhao
    Wu, Jiansheng
    Chen, Mengjun
    CANCER MANAGEMENT AND RESEARCH, 2020, 12 : 1211 - 1219