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European Radiology

, Volume 28, Issue 12, pp 5121–5128 | Cite as

The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules

  • Yunlang She
  • Lei Zhang
  • Huiyuan Zhu
  • Chenyang Dai
  • Dong Xie
  • Huikang Xie
  • Wei Zhang
  • Lilan Zhao
  • Liling Zou
  • Ke Fei
  • Xiwen SunEmail author
  • Chang ChenEmail author
Chest

Abstract

Objectives

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are assumed to be indolent lung adenocarcinoma with excellent prognosis. We aim to identify these lesions from invasive adenocarcinoma (IA) by a radiomics approach.

Methods

This retrospective study was approved by institutional review board with a waiver of informed consent. Pathologically confirmed lung adenocarcinomas manifested as lung nodules less than 3 cm were retrospectively identified. In-house software was used to quantitatively extract 60 CT-based radiomics features quantifying nodule’s volume, intensity and texture property through manual segmentation. In order to differentiate AIS/MIA from IA, least absolute shrinkage and selection operator (LASSO) logistic regression was used for feature selection and developing radiomics signatures. The predictive performance of the signature was evaluated via receiver operating curve (ROC) and calibration curve, and validated using an independent cohort.

Results

402 eligible patients were included and divided into the primary cohort (n = 207) and the validation cohort (n = 195). Using the primary cohort, we developed a radiomics signature based on five radiomics features. The signature showed good discrimination between MIA/AIS and IA in both the primary and validation cohort, with AUCs of 0.95 (95% CI, 0.91–0.98) and 0.89 (95% CI, 0.84–0.93), respectively. Multivariate logistic analysis revealed that the signature (OR, 13.3; 95% CI, 6.2–28.5; p < 0.001) and gender (OR, 3.5; 95% CI, 1.2–10.9; p = 0.03) were independent predictors of indolent lung adenocarcinoma.

Conclusion

The signature based on radiomics features helps to differentiate indolent from invasive lung adenocarcinoma, which might be useful in guiding the intervention choice for patients with pulmonary nodules.

Key points

• Based on radiomics features, a signature is established to differentiate adenocarcinoma in situ and minimally invasive adenocarcinoma from invasive lung adenocarcinoma.

Keywords

Lung neoplasms Tomography, spiral computed Radiomics Multivariate analysis Forecasting 

Abbreviations

AIS

Adenocarcinoma in situ

AUC

Area under the curve

CT

Computed tomography

IA

Invasive adenocarcinoma

LASSO

Least absolute shrinkage and selection operator

MIA

Minimally invasive adenocarcinoma

Notes

Funding

This study has received funding by Shanghai Hospital Development Center (16CR3116B).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Chang Chen.

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

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

• observational

• performed at one institution

Supplementary material

330_2018_5509_MOESM1_ESM.docx (433 kb)
ESM 1 (DOCX 432 kb)

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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Yunlang She
    • 1
  • Lei Zhang
    • 1
  • Huiyuan Zhu
    • 1
  • Chenyang Dai
    • 1
  • Dong Xie
    • 1
  • Huikang Xie
    • 2
  • Wei Zhang
    • 2
  • Lilan Zhao
    • 3
  • Liling Zou
    • 4
    • 5
  • Ke Fei
    • 1
  • Xiwen Sun
    • 6
    Email author
  • Chang Chen
    • 1
    Email author
  1. 1.Department of Thoracic Surgery, Shanghai Pulmonary HospitalTongji University School of MedicineShanghaiPeople’s Republic of China
  2. 2.Department of Pathology, Shanghai Pulmonary HospitalTongji University School of MedicineShanghaiPeople’s Republic of China
  3. 3.Department of General Visceral and Thoracic SurgeryUniversity Medical Center Hamburg-EppendorfHamburgGermany
  4. 4.Department of Medical StatisticsTongji university School of MedicineShanghaiPeople’s Republic of China
  5. 5.Clinical and Translational Science InstituteUniversity of Rochester Medical CenterRochesterUSA
  6. 6.Department of Radiology, Shanghai Pulmonary HospitalTongji University School of MedicineShanghaiPeople’s Republic of China

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