Annals of Surgical Oncology

, Volume 23, Supplement 5, pp 722–729 | Cite as

Shear-Wave Elastography for Papillary Thyroid Carcinoma can Improve Prediction of Cervical Lymph Node Metastasis

  • Ah Young Park
  • Jeong-Ah KimEmail author
  • Eun Ju Son
  • Ji Hyun Youk
Endocrine Tumors



This study aimed to investigate whether the elasticity index of shear-wave elastography (SWE) can predict cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC).


This retrospective study included 363 patients with a surgical diagnosis of PTC who underwent preoperative SWE evaluation. The elasticity indices of PTC (E mean, E max, E min, E ratio-p, and E ratio-m) and gray-scale ultrasound (US) parameters (extrathyroidal extension, multifocality, and cervical LN metastasis) were correlated with the pathologic staging parameters. The optimal cutoff values for the elasticity indices were determined for the prediction of cervical LN metastasis, and diagnostic performance was compared between gray-scale US and the combined application of gray-scale US and SWE.


The findings showed E mean and E max to be associated with central LN metastasis (P = 0.037) and E min to be associated with lateral LN metastasis (P = 0.015). An E mean value higher than 124 kPa or an E max value higher than 138 kPa with suspicious gray-scale US findings improved the sensitivity and area under the curve (AUC) for predicting central LN metastasis (sensitivity, 45.4 and 44.6 % vs. 28 %, P < 0.001; AUC, 0.659 and 0.667 vs. 0.615, P = 0.011 and 0.019), whereas an E min value higher than 63 kPa with suspicious gray-scale US findings improved the sensitivity and AUC for predicting lateral LN metastasis (sensitivity, 95.8 vs. 75 %, P = 0.025; AUC, 0.924 vs. 0.871, P = 0.047).


The quantitative elasticity index of PTC on preoperative SWE could be useful for predicting cervical LN metastasis.


Lymph Node Metastasis Thyroid Cancer Papillary Thyroid Carcinoma Cervical Lymph Node Metastasis Lateral Lymph Node Metastasis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Nothing to disclose.


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Copyright information

© Society of Surgical Oncology 2016

Authors and Affiliations

  • Ah Young Park
    • 1
  • Jeong-Ah Kim
    • 2
    Email author
  • Eun Ju Son
    • 2
  • Ji Hyun Youk
    • 2
  1. 1.Department of Radiology, CHA Bundang Medical CenterCHA UniversityGyeonggi-doRepublic of Korea
  2. 2.Department of Radiology, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea

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