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

  • Qu, Jinrong1, 2
  • Shen, Chen2, 3
  • Qin, Jianjun4
  • Wang, Zhaoqi1
  • Liu, Zhenyu3
  • Guo, Jia1
  • Zhang, Hongkai1
  • Gao, Pengrui1
  • Bei, Tianxia1
  • Wang, Yingshu1
  • Liu, Hui1
  • Kamel, Ihab R.5
  • Tian, Jie2, 3
  • Li, Hailiang1
  • 1 Henan Cancer Hospital, Department of Radiology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China , Zhengzhou (China)
  • 2 XIDIAN University, School of Life Science and Technology, Xi’an, Shaanxi, 710126, China , Xi’an (China)
  • 3 Chinese Academy of Sciences, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China , Beijing (China)
  • 4 Henan Cancer Hospital, Department of Thoracic Surgery, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China , Zhengzhou (China)
  • 5 Johns Hopkins University School of Medicine, Department of Radiology, Baltimore, MD, 21205-2196, USA , Baltimore (United States)
Published Article
European Radiology
Publication Date
Jul 23, 2018
DOI: 10.1007/s00330-018-5583-z
Springer Nature


PurposeTo assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC).Patients and methodsA 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.ResultsNine 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.ConclusionThe 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.

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