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

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

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

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

ADC:

Adenocarcinoma

AUC:

Area Under the Curve

CI:

Confidence Interval

CT:

Computed Tomography

LASSO:

Least Absolute Shrinkage and Selection Operator

NSCLC:

Non-small Cell Lung Cancer

PET:

Positron Emission Tomography

ROC:

Receiver Operating Characteristic

ROI:

Region of Interest

SCC:

Squamous Cell Carcinoma

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Funding

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.

Author information

Author notes

  1. Xinzhong Zhu and Zhendong Chen contributed equally to this work.

Authors and Affiliations

  1. School of Life Science and Technology, XIDIAN University, Xi’an, Shanxi, China

    Xinzhong Zhu & Jie Tian

  2. CAS Key Lab of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Xinzhong Zhu, Di Dong, Zhendong Chen, Mengjie Fang, Liwen Zhang, Jiangdian Song, Dongdong Yu, Yali Zang, Zhenyu Liu & Jie Tian

  3. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhengjiang, China

    Xinzhong Zhu & Zhendong Chen

  4. University of Chinese Academy of Sciences, Beijing, China

    Di Dong, Mengjie Fang, Dongdong Yu, Yali Zang, Zhenyu Liu & Jie Tian

  5. Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Tongji, China

    Jingyun Shi

Authors
  1. Xinzhong Zhu
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  2. Di Dong
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  10. Jingyun Shi
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  11. Jie Tian
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Corresponding authors

Correspondence to Xinzhong Zhu, Di Dong or Jingyun Shi.

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Guarantor

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.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cite this article

Zhu, X., Dong, D., Chen, Z. et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28, 2772–2778 (2018). https://doi.org/10.1007/s00330-017-5221-1

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  • DOI: https://doi.org/10.1007/s00330-017-5221-1

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