International Journal of Clinical Oncology

, Volume 24, Issue 6, pp 649–659 | Cite as

CT and clinical characteristics that predict risk of EGFR mutation in non-small cell lung cancer: a systematic review and meta-analysis

  • Hanfei Zhang
  • Weiguo Cai
  • Yanfan Wang
  • Meiyan LiaoEmail author
  • Sufang Tian
Original Article



To systematically analyze CT and clinical characteristics to find out the risk factors of epidermal growth factor receptor (EGFR) mutation in non-small cell lung cancer (NSCLC). Then the significant characteristics were used to set up a mathematic model to predict EGFR mutation in NSCLC.

Materials and methods

PubMed, Web of Knowledge and EMBASE up to August 17, 2018 were systematically searched for relevant studies that investigated the evidence of association between CT and clinical characteristics and EGFR mutation in NSCLC. After study selection, data extraction, and quality assessment, the pooled odds ratios (ORs) were calculated. Then from May 2017 to August 2018, all NSCLC received EGFR mutation examination and CT examination in our hospital were chosen to test the prediction model by receiver operating characteristic (ROC) curves.


Seventeen original studies met the inclusion criteria. The results showed that the ORs of ground-glass opacity (GGO), air bronchogram, pleural retraction, vascular convergence, smoking history, female gender were, respectively, 1.93 (P = 0.003), 2.09 (P = 0.03), 1.59 (P < 0.01), 1.61 (P = 0.001), 0.28 (P < 0.01), 0.35 (P < 0.01). The result of speculation, cavitation/bubble-like lucency, lesion shape, margin, pathological stage were, respectively, 1.19 (P = 0.32), 0.99 (P = 0.97), 0.82 (P = 0.42), 1.02 (P = 0.90), 0.77 (P = 0.30). 121 NSCLC received EGFR mutation test were included to test the prediction model. The mathematical model based on the results of meta-analysis was: 0.74 × air bronchogram + 0.46 × pleural retraction + 0.48 × vascular convergence − 1.27 × non-smoking history − 1.05 × female. The area under the ROC curve was 0.68.


Based on the current evidence, GGO presence, air bronchogram, pleural retraction, vascular convergence were significant risk factors of EGFR mutation in NSCLC. And the prediction model can help to predict EGFR mutation status.


Spiral computed tomography Epidermal growth factor receptor Non-small cell lung carcinoma Meta-analysis 



This work was supported by the Key Foundation of Hubei Natural Science Funds (no. 2015CFB649).

Compliance with ethical standards

Conflict of interest

No author has any conflict of interest.

Supplementary material

10147_2019_1403_MOESM1_ESM.docx (82 kb)
Supplementary material 1 (DOCX 82 KB)


  1. 1.
    Ettinger DS, Akerley W, Borghaei H et al (2012) Non-small cell lung cancer. J Natl Compr Cancer Netw 10(10):1236–1271Google Scholar
  2. 2.
    Detterbeck FC, Boffa DJ, Tanoue LT (2009) The new lung cancer staging system. Chest 136(1):260–271Google Scholar
  3. 3.
    Goldstraw P, Chansky K, Crowley J et al (2016) The IASLC lung cancer staging project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 11(1):39–51Google Scholar
  4. 4.
    Buettner R, Wolf J, Thomas RK (2013) Lessons learned from lung cancer genomics: the emerging concept of individualized diagnostics and treatment. J Clin Oncol Off J Am Soc Clin Oncol 31(15):1858–1865Google Scholar
  5. 5.
    Abdallah SM, Hirsh V (2018) Irreversible tyrosine kinase inhibition of epidermal growth factor receptor with afatinib in EGFR activating mutation-positive advanced non-small-cell lung cancer. Curr Oncol 25(Suppl 1):S9–S17Google Scholar
  6. 6.
    Diaz-Serrano A, Gella P, Jimenez E et al (2018) Targeting EGFR in lung cancer: current standards and developments. Drugs 78(9):893–911Google Scholar
  7. 7.
    Rizzo S, Petrella F, Buscarino V et al (2016) CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol 26(1):32–42Google Scholar
  8. 8.
    Taniguchi K, Okami J, Kodama K et al (2008) Intratumor heterogeneity of epidermal growth factor receptor mutations in lung cancer and its correlation to the response to gefitinib. Cancer Sci 99(5):929–935Google Scholar
  9. 9.
    Tomonaga N, Nakamura Y, Yamaguchi H et al (2013) Analysis of intratumor heterogeneity of EGFR mutations in mixed type lung adenocarcinoma. Clin Lung Cancer 14(5):521–526Google Scholar
  10. 10.
    Tang ER, Schreiner AM, Pua BB (2014) Advances in lung adenocarcinoma classification: a summary of the new international multidisciplinary classification system (IASLC/ATS/ERS). J Thorac Dis 6(Suppl 5):S489–S501Google Scholar
  11. 11.
    Cheng Z, Shan F, Yang Y et al (2017) CT characteristics of non-small cell lung cancer with epidermal growth factor receptor mutation: a systematic review and meta-analysis. BMC Med Imaging 17(1):1–10Google Scholar
  12. 12.
    Liu Y, Kim J, Balagurunathan Y et al (2016) Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer 17(5):441–448e6Google Scholar
  13. 13.
    Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1–e7Google Scholar
  14. 14.
    Wells GA, Shea B, O’Connell D et al (2014) The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analysis. Ottawa Hospital Research Institute. http://www.ohrica/programs/clinical_epidemiology/oxfordasp. Accessed 9 June 2014
  15. 15.
    Higgins JPT, Green S (eds) (2011) Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The Cochrane Collaboration 2011.
  16. 16.
    Usuda K, Sagawa M, Motono N et al (2014) Relationships between EGFR mutation status of lung cancer and preoperative factors—are they predictive? Asian Pac J Cancer Prev 15(2):657–662Google Scholar
  17. 17.
    Sabri A, Batool M, Xu Z et al (2016) Predicting EGFR mutation status in lung cancer: proposal for a scoring model using imaging and demographic characteristics. Eur Radiol 26(11):4141–4147Google Scholar
  18. 18.
    Kim T, Lee C, Jheon S et al (2016) Radiologic characteristics of surgically resected non-small cell lung cancer with ALK rearrangement or EGFR mutations. Ann Thorac Surg 101(2):473–480Google Scholar
  19. 19.
    Yano M, Sasaki H, Kobayashi Y et al (2006) Epidermal growth factor receptor gene mutation and computed tomographic findings in peripheral pulmonary adenocarcinoma. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 1(5):413–416Google Scholar
  20. 20.
    Sugano M, Shimizu K, Nakano T et al (2011) Correlation between computed tomography findings and epidermal growth factor receptor and Kras gene mutations in patients with pulmonary adenocarcinoma. Oncol Rep 26:1205–1211Google Scholar
  21. 21.
    Cao Y, Xu H, Liao M et al (2018) Associations between clinical data and computed tomography features in patients with epidermal growth factor receptor mutations in lung adenocarcinoma. Int J Clin Oncol 23(2):249–257Google Scholar
  22. 22.
    Hsu KH, Chen KC, Yang TY et al (2011) Epidermal growth factor receptor mutation status in stage I lung adenocarcinoma with different image patterns. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 6(6):1066–1072Google Scholar
  23. 23.
    Hsu JS, Huang MS, Chen CY et al (2014) Correlation between EGFR mutation status and computed tomography features in patients with advanced pulmonary adenocarcinoma. J Thorac Imaging 29(6):357–363Google Scholar
  24. 24.
    Sacconi B, Anzidei M, Leonardi A et al (2017) Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol 72(6):443–450Google Scholar
  25. 25.
    Yang Y, Yang Y, Zhou X et al (2015) EGFR L858R mutation is associated with lung adenocarcinoma patients with dominant ground-glass opacity. Lung Cancer 87(3):272–277Google Scholar
  26. 26.
    Zou J, Lv T, Zhu S et al (2017) Computed tomography and clinical features associated with epidermal growth factor receptor mutation status in stage I/II lung adenocarcinoma. Thorac Cancer 8(3):260–270Google Scholar
  27. 27.
    Hasegawa M, Sakai F, Ishikawa R et al (2016) CT features of epidermal growth factor receptor-mutated adenocarcinoma of the lung: comparison with nonmutated adenocarcinoma. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 11(6):819–826Google Scholar
  28. 28.
    Liu Y, Kim J, Qu F et al (2016) CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology 280(1):271–280Google Scholar
  29. 29.
    Zhou JY, Zheng J, Yu ZF et al (2015) Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations. Eur Radiol 25(5):1257–1266Google Scholar
  30. 30.
    Dai J, Shi J, Soodeen-Lalloo AK et al (2016) Air bronchogram: a potential indicator of epidermal growth factor receptor mutation in pulmonary subsolid nodules. Lung Cancer 98:22–28Google Scholar
  31. 31.
    Zhao J, Dinkel J, Warth A et al (2017) CT characteristics in pulmonary adenocarcinoma with epidermal growth factor receptor mutation. PLoS One 12(9):e0182741Google Scholar
  32. 32.
    Suzuki S, Sakurai H, Yotsukura M et al (2018) Clinical features of ground glass opacity-dominant lung cancer exceeding 3.0 cm in the whole tumor size. Ann Thorac Surg 105(5):1499–1506Google Scholar
  33. 33.
    Lee HJ, Kim YT, Kang CH et al (2013) Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes. Radiology 268(1):254–264Google Scholar
  34. 34.
    Haneda H, Sasaki H, Shimizu S et al (2006) Epidermal growth factor receptor gene mutation defines distinct subsets among. Lung Cancer 52(1):47–52Google Scholar
  35. 35.
    Lederlin M, Puderbach M, Muley T et al (2013) Correlation of radio- and histomorphological pattern of pulmonary adenocarcinoma. Eur Respir J 41(4):943–951Google Scholar

Copyright information

© Japan Society of Clinical Oncology 2019

Authors and Affiliations

  1. 1.Department of RadiologyZhongnan Hospital of Wuhan UniversityWuhanChina
  2. 2.Department of PathologyZhongnan Hospital of Wuhan UniversityWuhanChina

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