Skip to main content


Log in

Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript



The tyrosine kinase inhibitor (TKI)-sensitive mutations of the epidermal growth factor receptor (EGFR) gene is essential in the treatment of lung adenocarcinoma. To overcome the difficulty of EGFR gene test in situations where surgery and biopsy samples are too risky to obtain, we tried a noninvasive imaging method using radiomics features and random forest models.


Five hundred three lung adenocarcinoma patients who received surgery-based treatment were included in this study. The diagnosis and EGFR gene test were based on resections. TKI-sensitive mutations were found in 60.8% of the patients. CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types. Clinical features (sex and smoking history) were added to the image-based model. The model was trained on a set of 345 patients and validated on an independent test group (n = 158) using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.


The performance of the random forest model with 94 radiomics features reached an AUC of 0.802. Its AUC was further improved to 0.828 by adding sex and smoking history. The sensitivity and specificity are 60.6% and 85.1% at the best diagnostic decision point.


Our results showed that radiomics could not only reflect the genetic differences among tumors but also have diagnostic value and the potential to be a diagnostic tool.

Key Points

Radiomics provides a potential noninvasive method for the prediction of EGFR mutation status.

In situations where surgeries and biopsy are not available, CT image-based radiomics models could help to make treatment decisions.

The accuracy, sensitivity, and specificity still need to be improved before the image-based EGFR identifier could be used in clinics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others


19 del:

Exon 19 deletions


Artificial intelligence


Anaplastic lymphoma kinase


Area under the receiver operating characteristic curve


Computed tomography


Circulating tumor cells


Circulating tumor DNA


Epidermal growth factor receptor

exon 21 L858R:

Exon 21 amino acid substitution at position 858 in EGFR, from a leucine (L) to an arginine (R)


Ground glass components


The International Association for the Study of Lung Cancer


Non-small cell lung cancer


Polymerase chain reaction


Random forest


Receiver operating characteristic


Region of interest


Tyrosine kinase inhibitors


Treatment planning system


  1. Society, A.C(2015) Cancer Facts & Figures 2015. Atlanta: American Cancer Society. Available via

  2. National Cancer Institute (2015), SEER Cancer Statistics Review, 1975–2012, National Cancer Institute, available via http://, based on November 2014 SEER data submission, posted to the SEER web site

  3. Lynch TJ, Bell DW, Sordella R et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med 350:2129–2139

    Article  CAS  PubMed  Google Scholar 

  4. Paez JG, Jänne PA, Lee JC et al (2004) EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304:1497–1500

  5. Fukuoka M, Yano S, Giaccone G et al (2003) Multi-institutional randomized phase II trial of gefitinib for previously treated patients with advanced non-small-cell lung cancer (The IDEAL 1 Trial). J Clin Oncol 21:2237–2246

  6. Kris MG, Natale RB, Herbst RS et al (2003) Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. JAMA 290:2149–2158

    Article  CAS  PubMed  Google Scholar 

  7. Mitsudomi T, Yatabe Y (2007) Mutations of the epidermal growth factor receptor gene and related genes as determinants of epidermal growth factor receptor tyrosine kinase inhibitors sensitivity in lung cancer. Cancer Sci 98:1817–1824

    Article  CAS  PubMed  Google Scholar 

  8. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 5:4006. doi:

  9. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577

    Article  Google Scholar 

  10. Rios Velazquez E, Liu Y, Parmar C, Narayan V, Gillies R, Aerts H (2016) Radiomic CT features complement semantic annotations to predict EGFR mutations in lung adenocarcinomas. Med Phys 43:3706–3706

    Article  Google Scholar 

  11. 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):441–448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gevaert O, Echegaray S, Khuong A et al (2017) Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep 7:41674

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 26:32–42.

    Article  PubMed  Google Scholar 

  14. Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6(1):23428–23428

Download references


The abstract of this manuscript was published in the meeting of IASLC 18th World Conference on Lung Cancer with a smaller patient set.


Tian-Ying Jia, et al, Detecting Epidermal Growth factor receptor mutation status in patients with lung adenocarcinoma using radiomics and random forest. IASLC 18th World Conference on Lung Cancer, MA 14.12.


This study has received funding by National Key Research and Development Program of China (2016YFC0905502 and 2016YFC0104608), National Natural Science Foundation of China (No. 81371634), Shanghai Jiao Tong University Medical Engineering Cross Research Funds (YG2017ZD10, YG2013MS30, and YG2014ZD05), and the project of multi-center clinical research, Shanghai Jiao Tong University School of Medicine (DLY201619).

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Jun Zhao or Xiao-Long Fu.

Ethics declarations


The scientific guarantor of this publication is Xiao-Long Fu.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study because this study used CT images and clinical information and did not involve treatment decisions.

Ethical approval

Institutional Review Board approval was not required because this is a retrospective study and did not influence the treatment of the patients.


• retrospective

• diagnostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(DOC 1305 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, TY., Xiong, JF., Li, XY. et al. Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. Eur Radiol 29, 4742–4750 (2019).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: