J Vet Sci.  2019 Nov;20(6):e62. 10.4142/jvs.2019.20.e62.

Semi-quantitative strain elastography may facilitate pre-surgical prediction of mandibular lymph nodes malignancy in dogs

• Choi M
• Yoon J
• Choi M

• KMID: 2464078
• DOI: http://doi.org/10.4142/jvs.2019.20.e62

Abstract

Evaluation of mandibular lymph nodes in a patient with head and neck cancer is important for stage determination and prognosis development, and, in human medicine, the use of sonoelastography for differentiating between non-metastatic and metastatic lymph nodes has been reported. Our prospective, cross-sectional study aimed to evaluate the diagnostic performance of strain elastography and to determine elastographic cut-off values for predicting malignancy. Sixty-six mandibular lymph nodes were included (clinical healthy, n = 45; non-metastatic, n = 8; and metastatic, n = 13). Elastographic images were evaluated qualitatively (elastographic pattern) and semi-quantitatively (mean hue histogram and stiffness area ratios). Elastographic patterns were classified as grades 1 to 4, according to the percentage of high elasticity determined by visualization. The mean hue histogram was defined based on as the mean pixel color values within the lymph node. Stiffness area ratios were determined by computer program analysis of the stiff area. Among the criteria, receiver operating characteristic curve analyses revealed cut-off values for the prediction of malignancy of 92.26 for mean hue histogram (sensitivity: 100%, specificity: 92%), and 0.17 for stiffness area ratios (sensitivity: 86%, specificity of 100%). Reproducibility and repeatability were excellent. In conclusion, semi-quantitative evaluation via strain elastography holds potential for predicting lymph node malignancy.

Keyword

Lymph node; predicting malignancy; stiffness area ratio; strain histogram; ultrasound elastography

MeSH Terms

Animals
Cross-Sectional Studies
Dogs*
Elasticity
Elasticity Imaging Techniques*
Head and Neck Neoplasms
Humans
Lymph Nodes*
Prognosis
Prospective Studies
ROC Curve
Sensitivity and Specificity