European Radiology

, Volume 28, Issue 7, pp 2772–2778 | Cite as

Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer

  • Xinzhong ZhuEmail author
  • Di DongEmail author
  • Zhendong Chen
  • Mengjie Fang
  • Liwen Zhang
  • Jiangdian Song
  • Dongdong Yu
  • Yali Zang
  • Zhenyu Liu
  • Jingyun ShiEmail author
  • Jie Tian
Computed Tomography



To distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature


This study involved 129 patients with non-small cell lung cancer (NSCLC) (81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts.


Five features were selected to construct the radiomic signature for histologic subtype classification. The performance of the radiomic signature to distinguish between lung ADC and SCC in both training and validation cohorts was good, with an AUC of 0.905 (95% confidence interval [CI]: 0.838 to 0.971), sensitivity of 0.830, and specificity of 0.929. In the validation cohort, the radiomic signature showed an AUC of 0.893 (95% CI: 0.789 to 0.996), sensitivity of 0.828, and specificity of 0.900.


A unique radiomic signature was constructed for use as a diagnostic factor for discriminating lung ADC from SCC. Patients with NSCLC will benefit from the proposed radiomic signature.

Key points

• Machine learning can be used for auxiliary distinguish in lung cancer.

• Radiomic signature can discriminate lung ADC from SCC.

• Radiomics can help to achieve precision medical treatment.


Aenocarcinoma Diagnostic imaging Regression analysis ROC curve Squamous cell carcinoma 





Area Under the Curve


Confidence Interval


Computed Tomography


Least Absolute Shrinkage and Selection Operator


Non-small Cell Lung Cancer


Positron Emission Tomography


Receiver Operating Characteristic


Region of Interest


Squamous Cell Carcinoma



This work was supported by the National Natural Science Foundation of China (81227901, 81771924, 81501616, 61231004, 81671851, and 81527805), National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1308701, 2017YFC1309100), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201502), the Beijing Municipal Science and Technology Commission (Z161100002616022), and the Youth Innovation Promotion Association CAS.

Compliance with ethical standards


The scientific guarantor of this publication is Jie Tian.

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 obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2017_5221_MOESM1_ESM.doc (82 kb)
ESM 1 (DOC 82 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.School of Life Science and TechnologyXIDIAN UniversityXi’anChina
  2. 2.CAS Key Lab of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.College of Mathematics, Physics and Information EngineeringZhejiang Normal UniversityJinhuaChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Department of Radiology, Shanghai Pulmonary HospitalTongji University School of MedicineTongjiChina

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