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

  • Chest
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Abstract

Objectives

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

Methods

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.

Results

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.

Conclusions

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.

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Abbreviations

ANNs:

Artificial neural networks

AUC:

Area under the curve

CT:

Computed tomography

DFS:

Disease-free survival

GGN:

Ground-glass nodule

GLCM:

Grey-level co-occurrence matrix

GLRLM:

Grey-level run-length matrix

IAC:

Invasive adenocarcinoma

IASLC/ATS/ERS:

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

LASSO:

The least absolute shrinkage and selection operator

MIA:

Minimally invasive adenocarcinoma

ROC:

Receiver-operating characteristic

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Funding

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).

Author information

Author notes

  1. Li Fan and MengJie Fang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Changzheng Hospital, Second Military Medical University, No. 415 Fengyang Road, Shanghai, 200003, China

    Li Fan, WenTing Tu & ShiYuan Liu

  2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No.95 Zhongguancun East Road, Beijing, 100190, China

    MengJie Fang, Jie Tian & Di Dong

  3. University of Chinese Academy of Sciences, Beijing, 100190, China

    MengJie Fang, Jie Tian & Di Dong

  4. Department of Radiation Oncology, The Sixth People’s Hospital, Shanghai Jiaotong University, Shanghai, 200233, China

    ZhaoBin Li

  5. Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China

    ShengPing Wang

  6. Department of Radiology, Huadong Hospital Affiliated with Fudan University, Shanghai, 200040, China

    WuFei Chen

Authors
  1. Li Fan
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  2. MengJie Fang
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  3. ZhaoBin Li
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  7. Jie Tian
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  8. Di Dong
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  9. ShiYuan Liu
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Corresponding authors

Correspondence to Di Dong or ShiYuan Liu.

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Guarantor

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.

Methodology

• retrospective

• diagnostic or prognostic study

• multicentre study

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Fan, L., Fang, M., Li, Z. et al. Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur Radiol 29, 889–897 (2019). https://doi.org/10.1007/s00330-018-5530-z

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  • DOI: https://doi.org/10.1007/s00330-018-5530-z

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