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European Radiology

, Volume 29, Issue 11, pp 6049–6058 | Cite as

Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?

  • Xiang Wang
  • Xingyu Zhao
  • Qiong Li
  • Wei Xia
  • Zhaohui Peng
  • Rui Zhang
  • Qingchu Li
  • Junming Jian
  • Wei Wang
  • Yuguo Tang
  • Shiyuan LiuEmail author
  • Xin GaoEmail author
Chest

Abstract

Objectives

To evaluate the efficiency of radiomics model on CT images of intratumoral and peritumoral lung parenchyma for preoperative prediction of lymph node (LN) metastasis in clinical stage T1 peripheral lung adenocarcinoma patients.

Methods

Three hundred sixty-six peripheral lung adenocarcinoma patients with clinical stage T1 were evaluated using five CT scanners. For each patient, two volumes of interest (VOIs) on CT were defined as the gross tumor volume (GTV) and the peritumoral volume (PTV, 1.5 cm around the tumor). One thousand nine hundred forty-six radiomic features were obtained from each VOI, and then refined for reproducibility and redundancy. The refined features were investigated for usefulness in building radiomic signatures by mRMR feature ranking method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic nomogram incorporating the radiomic signature and clinical parameters. The prediction performance was evaluated on the validation cohort.

Results

The radiomic signatures using the features of GTV and PTV showed a good ability in predicting LN metastasis with an AUC of 0.829 (95% CI, 0.745–0.913) and 0.825 (95% CI, 0.733–0.918), respectively. By incorporating the features of GTV and PTV, the AUC of radiomic signature increased to 0.843 (95% CI, 0.770–0.916). The AUC of radiomic nomogram was 0.869 (95% CI, 0.800–0.938).

Conclusions

Radiomic signatures of GTV and PTV both had a good prediction ability in the prediction of LN metastasis, and there is no significant difference of AUC between the two groups. The proposed nomogram can be conveniently used to facilitate the preoperative prediction of LN metastasis in T1 peripheral lung adenocarcinomas.

Key Points

• Radiomics from peritumoral lung parenchyma increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT.

• A radiomic nomogram was developed and validated to predict LN metastasis.

• Different scan parameters on CT showed that radiomics signature had good predictive performance.

Keywords

Lung Adenocarcinoma Radiomics Lymph node Metastasis 

Abbreviations

GPTV

Gross and peritumoral volume

GTV

Gross tumor volume

LASSO

Least absolute shrinkage and selection operator

LN

Lymph node

mRMR

Minimum redundancy maximum relevance

PTV

Peritumoral volume

Notes

Funding

The National Key Research and Development Program of China for Intergovernmental Cooperation (2016YFE0103000), Shanghai Municipal Commission of Health and Family Planning Program (grant numbers 20184Y0037 and 2018ZHYL0101).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Prof. Xin Gao.

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

This retrospective analysis was approved by the ethical review board of our hospital (No. 2018SL049).

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6084_MOESM1_ESM.docx (50 kb)
ESM 1 (DOCX 50.3 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyChangzheng Hospital, Second Military Medical University ShanghaiChina
  2. 2.University of Science and Technology of ChinaHefeiChina
  3. 3.Suzhou Institute of Biomedical Engineering and TechnologyChinese Academy of SciencesSuzhouChina

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