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

European Radiology volume 29, pages 6049–6058 (2019)Cite this article

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.

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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

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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).

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Author notes

  1. Xiang Wang and Xingyu Zhao are primary authors and contributed equally.

Authors and Affiliations

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

    Xiang Wang, Qiong Li, Zhaohui Peng, Qingchu Li, Wei Wang & Shiyuan Liu

  2. University of Science and Technology of China, Hefei, China

    Xingyu Zhao & Junming Jian

  3. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China

    Xingyu Zhao, Wei Xia, Rui Zhang, Junming Jian, Yuguo Tang & Xin Gao

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  1. Xiang Wang
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Correspondence to Shiyuan Liu or Xin Gao.

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

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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.

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

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Wang, X., Zhao, X., Li, Q. et al. Can peritumoral radiomics increase the efficiency of the prediction for lymph node metastasis in clinical stage T1 lung adenocarcinoma on CT?. Eur Radiol 29, 6049–6058 (2019). https://doi.org/10.1007/s00330-019-06084-0

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Keywords

  • Lung
  • Adenocarcinoma
  • Radiomics
  • Lymph node
  • Metastasis