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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 Dong
  • ShiYuan Liu
Chest
  • 488 Downloads

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.

Keywords

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

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

Notes

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).

Compliance with ethical standards

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

Supplementary material

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

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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
  • ShiYuan Liu
    • 1
  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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