European Radiology

, Volume 29, Issue 2, pp 889–897 | Cite as

Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule

  • Li Fan
  • MengJie Fang
  • ZhaoBin Li
  • WenTing Tu
  • ShengPing Wang
  • WuFei Chen
  • Jie Tian
  • Di DongEmail author
  • ShiYuan LiuEmail author



To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules.


This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared.


355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value.


The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma.

Key Points

• The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma.

• The radiomics signature outweighed CT morphological and quantitative indices.

• A three-centre study showed that radiomics signature had good predictive performance.


Lung Adenocarcinoma Tomography, x-ray computed Computational biology Solitary pulmonary nodule 



Artificial neural networks


Area under the curve


Computed tomography


Disease-free survival


Ground-glass nodule


Grey-level co-occurrence matrix


Grey-level run-length matrix


Invasive adenocarcinoma


International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society


The least absolute shrinkage and selection operator


Minimally invasive adenocarcinoma


Receiver-operating characteristic



This study has received funding by the National Natural Science Foundation of China (grant numbers 81370035, 81230030, and 81771924), The National Key R&D Program of China (grant number 2016YFE0103000, 2017YFC1308703, 2017YFA0205200, 2017YFC1309100 and 2017YFC1308700), Shanghai Pujiang Talent Program (grant number 15PJD002).

Compliance with ethical standards


The scientific guarantor of this publication is Prof. Shiyuan Liu.

Conflict of interest

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

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• multicentre study

Supplementary material

330_2018_5530_MOESM1_ESM.docx (356 kb)
ESM 1 (DOCX 356 kb)


  1. 1.
    Goo JM, Park CM, Lee HJ (2011) Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma. AJR Am J Roentgenol 196:533–543CrossRefGoogle Scholar
  2. 2.
    Siegel R, Naishadham D, Jemal A (2013) Cancer statistics. CA Cancer J Clin 63:11–30CrossRefGoogle Scholar
  3. 3.
    de Groot P, Munden RF (2012) Lung cancer epidemiology, risk factors, and prevention. Radiol Clin North Am 50:863–876CrossRefGoogle Scholar
  4. 4.
    Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 6:244–285CrossRefGoogle Scholar
  5. 5.
    Russell PA, Wainer Z, Wright GM et al (2011) Does lung adenocarcinoma subtype predict patient survival? A clinicopathologic study based on the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol 6:1496–1504CrossRefGoogle Scholar
  6. 6.
    Luo J, Huang Q, Wang R et al (2016) Prognostic and predictive value of the novel classification of lung adenocarcinoma in patients with stage IB. J Cancer Res Clin Oncol 142:2031–2040CrossRefGoogle Scholar
  7. 7.
    Yanagawa N, Shiono S, Abiko M et al (2013) New IASLC/ATS/ERS classification and invasive tumor size are predictive of disease recurrence in stage I lung adenocarcinoma. J Thorac Oncol 8:612–618CrossRefGoogle Scholar
  8. 8.
    Yoshiya T, Mimae T, Tsutani Y et al (2016) Prognostic role of subtype classification in small-sized pathologic N0 invasive lung adenocarcinoma. Ann Thorac Surg 102:1668–1673CrossRefGoogle Scholar
  9. 9.
    Yoshizawa A, Motoi N, Riely GJ et al (2011) Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol 24:653–664CrossRefGoogle Scholar
  10. 10.
    Lee HY, Choi YL, Lee KS et al (2014) Pure ground-glass opacity neoplastic lung nodules: histopathology, imaging, and management. AJR Am J Roentgenol 202:W224–W233CrossRefGoogle Scholar
  11. 11.
    Ding H, Shi J, Zhou X et al (2017) Value of CT characteristics in predicting invasiveness of adenocarcinoma presented as pulmonary ground-glass nodules. Thorac Cardiovasc Surg 65:136–141PubMedPubMedCentralGoogle Scholar
  12. 12.
    Fan L, Liu SY, Li QC et al (2012) Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant. Br J Radiol 85:897–904CrossRefGoogle Scholar
  13. 13.
    Zhang Y, Qiang JW, Shen Y et al (2016) Using air bronchograms on multi-detector CT to predict the invasiveness of small lung adenocarcinoma. Eur J Radiol 85:571–577CrossRefGoogle Scholar
  14. 14.
    Shikuma K, Menju T, Chen F et al (2016) Is volumetric 3-dimensional computed tomography useful to predict histological tumour invasiveness? Analysis of 211 lesions of cT1N0M0 lung adenocarcinoma. Interact Cardiovasc Thorac Surg 22:831–838CrossRefGoogle Scholar
  15. 15.
    Ikeda K, Awai K, Mori T et al (2007) Differential diagnosis of ground-glass opacity nodules: CT number analysis by three-dimensional computerized quantification. Chest 132:984–990CrossRefGoogle Scholar
  16. 16.
    Yu WS, Hong SR, Lee JG et al (2016) Three-dimensional ground glass opacity ratio in C T images can predict tumor invasiveness of stage Ia lung cancer. Yonsei Med J 57:1131–1138Google Scholar
  17. 17.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  18. 18.
    Parmar C, Leijenaar RT, Grossmann P et al (2015) Radiomic feature clusters and prognostic signatures specific for lung and head and neck cancer. Sci Rep 5:11044CrossRefGoogle Scholar
  19. 19.
    Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefGoogle Scholar
  20. 20.
    Sakakura N, Inaba Y, Yatabe Y et al (2016) Estimation of the pathological invasive size of pulmonary adenocarcinoma using high-resolution computed tomography of the chest: a consideration based on lung and mediastinal window settings. Lung Cancer 95:51–56CrossRefGoogle Scholar
  21. 21.
    Son JY, Lee HY, Kim JH et al (2016) Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non–invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol 26:43–54CrossRefGoogle Scholar
  22. 22.
    Kitami A, Sano F, Hayashi S et al (2016) Correlation between histological invasiveness and the computed tomography value in pure ground-glass nodules. Surg Today 46:593–598CrossRefGoogle Scholar
  23. 23.
    Lim HJ, Ahn S, Lee KS et al (2013) Persistent pure ground glass opacity lung nodules ≥10 mm in diameter at CT scan: histopathologic comparisons and prognostic implications. Chest 144:1291–1299CrossRefGoogle Scholar
  24. 24.
    Nomori H, Ohtsuka T, Naruke T et al (2003) Differentiating between atypical adenomatous hyperplasia and bronchioloalveolar carcinoma using the computed tomography number histogram. Ann Thorac Surg 76:867–871CrossRefGoogle Scholar
  25. 25.
    Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248CrossRefGoogle Scholar
  26. 26.
    Zhao B, Tan Y, Tsai WY et al (2016) Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 6:23428CrossRefGoogle Scholar
  27. 27.
    Coroller TP, Grossmann P, Hou Y et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350CrossRefGoogle Scholar
  28. 28.
    Chae HD, Park CM, Park SJ et al (2014) Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 273:285–293CrossRefGoogle Scholar
  29. 29.
    Song J, Yang C, Fan L et al (2016) Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE Trans Med Imaging 35:337–353CrossRefGoogle Scholar
  30. 30.
    Wang S, Zhou M, Liu Z et al (2017) Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Li Fan
    • 1
  • MengJie Fang
    • 2
    • 3
  • ZhaoBin Li
    • 4
  • WenTing Tu
    • 1
  • ShengPing Wang
    • 5
  • WuFei Chen
    • 6
  • Jie Tian
    • 2
    • 3
  • Di Dong
    • 2
    • 3
    Email author
  • ShiYuan Liu
    • 1
    Email author
  1. 1.Department of RadiologyChangzheng Hospital, Second Military Medical UniversityShanghaiChina
  2. 2.CAS Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Department of Radiation OncologyThe Sixth People’s Hospital, Shanghai Jiaotong UniversityShanghaiChina
  5. 5.Department of RadiologyFudan University Shanghai Cancer CenterShanghaiChina
  6. 6.Department of RadiologyHuadong Hospital Affiliated with Fudan UniversityShanghaiChina

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