Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer



To assess the predictive power of pre-therapy 18F-FDG PET/CT-based radiomic features for epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer.


Two hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and were tested for genetic mutations. The LIFEx package was used to extract 47 PET and 45 CT radiomic features reflecting tumor heterogeneity and phenotype. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomics signature. We compared the predictive performance of models established by radiomics signature, clinical variables, and their combinations using receiver operating curves (ROCs). In addition, a nomogram based on the radiomics signature score (rad-score) and clinical variables was developed.


The patients were divided into a training set (n = 175) and a validation set (n = 73). Ten radiomic features were selected to build the radiomics signature model. The model showed a significant ability to discriminate between EGFR mutation and EGFR wild type, with area under the ROC curve (AUC) equal to 0.79 in the training set, and 0.85 in the validation set, compared with 0.75 and 0.69 for the clinical model. When clinical variables and radiomics signature were combined, the AUC increased to 0.86 (95% CI [0.80–0.91]) in the training set and 0.87 (95% CI [0.79–0.95]) in the validation set, thus showing better performance in the prediction of EGFR mutations.


The PET/CT-based radiomic features showed good performance in predicting EGFR mutation in non-small cell lung cancer, providing a useful method for the choice of targeted therapy in a clinical setting.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Hsu WH, Yang JC, Mok TS, Loong HH. Overview of current systemic management of EGFR-mutant NSCLC. Ann Oncol. 2018;29:i3–9.

    Article  Google Scholar 

  2. 2.

    An N, Zhang Y, Niu H, Li Z, Cai J, Zhao Q, et al. EGFR-TKIs versus taxanes agents in therapy for nonsmall-cell lung cancer patients: a PRISMA-compliant systematic review with meta-analysis and meta-regression. Medicine (Baltimore). 2016;95:e5601.

    CAS  Article  Google Scholar 

  3. 3.

    Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M, et al. Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J. 2019;53:1800986.

    Article  Google Scholar 

  4. 4.

    Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics─guiding principles and technical considerations. Radiology. 2014;270(2):320–5.

    Article  Google Scholar 

  5. 5.

    Ozkan E, West A, Dedelow JA, Chu BF, Zhao W, Yildiz VO, et al. CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung. AJR Am J Roentgenol. 2015;205:1016–25.

    Article  Google Scholar 

  6. 6.

    Bianconi F, Fravolini ML, Bello-Cerezo R, Minestrini M, Scialpi M, Palumbo B. Evaluation of shape and textural features from CT as prognostic biomarkers in non-small cell lung cancer. Anticancer Res. 2018;38:2155–60.

    PubMed  Google Scholar 

  7. 7.

    Digumarthy SR, Padole AM, Gullo RL, Sequist LV, Kalra MK. Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine (Baltimore). 2019;98:e13963.

    CAS  Article  Google Scholar 

  8. 8.

    Cook GJR, Azad G, Owczarczyk K, Siddique M, Goh V. Challenges and promises of PET radiomics. Int J Radiat Oncol Biol Phys. 2018;102:1083–9.

    Article  Google Scholar 

  9. 9.

    Lee SM, Bae SK, Jung SJ, Kim CK. FDG uptake in non-small cell lung cancer is not an independent predictor of EGFR or KRAS mutation status: a retrospective analysis of 206 patients. Clin Nucl Med. 2015;40:950–8.

    Article  Google Scholar 

  10. 10.

    Takamochi K, Mogushi K, Kawaji H, Imashimizu K, Fukui M, Oh S, et al. Correlation of EGFR or KRAS mutation status with 18F-FDG uptake on PET-CT scan in lung adenocarcinoma. PLoS One. 2017;12:e0175622.

    Article  Google Scholar 

  11. 11.

    Zwanenburg A, Leger S, Vallières M, Löck S, for the Image Biomarker Standardisation Initiative (IBSI). Image biomarker standardisation initiative — feature definitions. 2018 [Current version V10 2019].

  12. 12.

    Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

    CAS  Article  Google Scholar 

  13. 13.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9.

    CAS  Article  Google Scholar 

  14. 14.

    Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, et al. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018;45:1649–60.

    Article  Google Scholar 

  15. 15.

    She Y, Zhang L, Zhu H, Dai C, Xie D, Xie H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28:5121–8.

    Article  Google Scholar 

  16. 16.

    Goldman JW, Noor ZS, Remon J, Besse B, Rosenfeld N. Are liquid biopsies a surrogate for tissue EGFR testing? Ann Oncol. 2018;29:i38–46.

    CAS  Article  Google Scholar 

  17. 17.

    Kim TO, Oh IJ, Kho BG, Park HY, Chang JS, Park CK, et al. Feasibility of re-biopsy and EGFR mutation analysis in patients with non-small cell lung cancer. Thorac Cancer. 2018;9:856–64.

    Article  Google Scholar 

  18. 18.

    Li W, Qiu T, Ling Y, Gao S, Ying J. Subjecting appropriate lung adenocarcinoma samples to next-generation sequencing-based molecular testing: challenges and possible solutions. Mol Oncol. 2018;12:677–89.

    CAS  Article  Google Scholar 

  19. 19.

    Kim YI, Paeng JC, Park YS, Cheon GJ, Lee DS, Chung JK, et al. Relation of EGFR mutation status to metabolic activity in localized lung adenocarcinoma and its influence on the use of FDG PET/CT parameters in prognosis. AJR Am J Roentgenol. 2018;210:1346–51.

    Article  Google Scholar 

  20. 20.

    Weihua Z, Tsan R, Huang WC, Wu Q, Chiu CH, Fidler IJ, et al. Survival of cancer cells is maintained by EGFR independent of its kinase activity. Cancer Cell. 2008;13:385–93.

    Article  Google Scholar 

  21. 21.

    Rizzo S, Petrella F, Buscarino V, De Maria F, Raimondi S, Barberis M, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol. 2016;26:32–42.

    Article  Google Scholar 

  22. 22.

    Liu Y, Kim J, Qu F, Liu S, Wang H, Balagurunathan Y, et al. CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology. 2016;280:271–80.

    Article  Google Scholar 

  23. 23.

    Shi Z, Zheng X, Shi R, Song C, Yang R, Zhang Q, et al. Radiological and clinical features associated with epidermal growth factor receptor mutation status of Exon 19 and 21 in lung adenocarcinoma. Sci Rep. 2017;7:364.

    Article  Google Scholar 

  24. 24.

    Sacconi B, Anzidei M, Leonardi A, Boni F, Saba L, Scipione R, et al. Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol. 2017;72:443–50.

    CAS  Article  Google Scholar 

  25. 25.

    Desseroit MC. Tixier F, Weber WA, Siegel BA, Cheze Le Rest C, Visvikis D, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med. 2017;58:406–11.

    CAS  Article  Google Scholar 

  26. 26.

    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    CAS  Article  Google Scholar 

  27. 27.

    Yip SS, Kim J, Coroller TP, Parmar C, Velazquez ER, Huynh E, et al. Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med. 2017;58:569–76.

    CAS  Article  Google Scholar 

  28. 28.

    Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 2017;77:3922–30.

    CAS  Article  Google Scholar 

  29. 29.

    Dogan S, Shen R, Ang DC, Johnson ML, D’Angelo SP, Paik PK, et al. Molecular epidemiology of EGFR and KRAS mutations in 3,026 lung adenocarcinomas: higher susceptibility of women to smoking-related KRAS-mutant cancers. Clin Cancer Res. 2012;18:6169–77.

    CAS  Article  Google Scholar 

  30. 30.

    Shi Y, Au JS, Thongprasert S, Srinivasan S, Tsai CM, Khoa MT, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J Thorac Oncol. 2014;9:154–62.

    CAS  Article  Google Scholar 

  31. 31.

    Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia AL, Stringfield O, et al. Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer. 2016;17:441–8.e6.

    CAS  Article  Google Scholar 

  32. 32.

    Zhang L, Chen B, Liu X, Song J, Fang M, Hu C, et al. Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol. 2018;11:94–101.

    Article  Google Scholar 

Download references


The authors thank all their coworkers involved in the study for their support and assistance.


This study was financially supported by the Foundation of Science and Technology Department of Hebei Province, China (grant No.15277776D).

Author information



Corresponding author

Correspondence to Xinming Zhao.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the study and involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

This retrospective analysis was approved by the Institutional Review Board of the Fourth Hospital of Hebei Medical University (Approval No. 2019MEC031), and the requirement of informed consent was waived.

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

Electronic supplementary material


(DOCX 20 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Zhao, X., Zhao, Y. et al. Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 47, 1137–1146 (2020).

Download citation


  • Radiomics
  • Epidermal growth factor receptor
  • Non-small cell lung cancer
  • 18F-FDG
  • PET/CT