European Radiology

, Volume 29, Issue 2, pp 906–914 | Cite as

The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer

  • Jinrong Qu
  • Chen Shen
  • Jianjun Qin
  • Zhaoqi Wang
  • Zhenyu Liu
  • Jia Guo
  • Hongkai Zhang
  • Pengrui Gao
  • Tianxia Bei
  • Yingshu Wang
  • Hui Liu
  • Ihab R. Kamel
  • Jie TianEmail author
  • Hailiang LiEmail author



To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC).

Patients and methods

A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts.


Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes.


The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients.

Key Points

• The role of MRI in preoperative staging of esophageal cancer patients is increasing.

• MRI radiomic features showed the ability to predict LN metastasis in EC patients.

• ICCs showed excellent interreader agreement of the extracted MR features.


Magnetic resonance imaging Esophageal cancer Lymph nodes Metastasis 



Area under receiver operating characteristic curve


Computed tomography


Esophageal cancer


Interclass correlation coefficient


Least absolute shrinkage selection operator


Lymph node


Magnetic resonance imaging


Regions of interest


T2-weighted imaging



This study has received funding from the National Natural Science Foundation of China (nos. 81501549, 81772012), the National Key Research and Development Plan of China under grant nos. 2017YFA0205200 and 2016YFC0103001, Beijing Municipal Science & Technology Commission (no. Z171100000117023) and special funding from the Henan Health Science and Technology Innovation Talent Project (no. 201004057).

Compliance with ethical standards


The scientific guarantor of this publication is Hailiang Li.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Siemens. Three authors from Siemens provided the prototype sequence and reviewed the paper without any conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• case-control study/observational/experimental

• performed at one institution

Supplementary material

330_2018_5583_MOESM1_ESM.docx (29 kb)
ESM 1 (DOCX 28 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Jinrong Qu
    • 1
    • 2
  • Chen Shen
    • 2
    • 3
  • Jianjun Qin
    • 4
  • Zhaoqi Wang
    • 1
  • Zhenyu Liu
    • 3
  • Jia Guo
    • 1
  • Hongkai Zhang
    • 1
  • Pengrui Gao
    • 1
  • Tianxia Bei
    • 1
  • Yingshu Wang
    • 1
  • Hui Liu
    • 1
  • Ihab R. Kamel
    • 5
  • Jie Tian
    • 2
    • 3
  • Hailiang Li
    • 1
  1. 1.Department of Radiology, Affiliated Cancer Hospital of Zhengzhou UniversityHenan Cancer HospitalZhengzhouChina
  2. 2.School of Life Science and TechnologyXIDIAN UniversityXi’anChina
  3. 3.Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  4. 4.Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou UniversityHenan Cancer HospitalZhengzhouChina
  5. 5.Department of RadiologyJohns Hopkins University School of MedicineBaltimoreUSA

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