Prediction of cervical lymph node metastasis in patients with papillary thyroid cancer using combined conventional ultrasound, strain elastography, and acoustic radiation force impulse (ARFI) elastography.

ABSTRACT

OBJECTIVES: To investigate the value of combined conventional ultrasound (US), strain elastography (SE) and acoustic radiation force impulse (ARFI) elastography for prediction of cervical lymph node metastasis (CLNM) in papillary thyroid cancer (PTC). METHODS: A consecutive series of 203 patients with 222 PTCs were preoperatively evaluated by US, SE, and ARFI including virtual touch tissue imaging (VTI) and virtual touch tissue quantification (VTQ). A multivariate analysis was performed to predict CLNM by 22 independent variables. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance. RESULTS: Multivariate analysis demonstrated that VTI area ratio (VAR) > 1 was the best predictor for CLNM, followed by abnormal cervical lymph node (ACLN), capsule contact, microcalcification, capsule involvement, and multiple nodules (all P < 0.05). ROC analyses of these characteristics showed the areas under the curve (Az), sensitivity, and specificity were 0.600-0.630, 47.7 %-93.2 %, and 26.9 %-78.4 % for US, respectively; and they were 0.784, 83.0 %, and 73.9 %, respectively, for VAR > 1. As combination of US characteristics with and without VAR, the Az, sensitivity, and specificity were 0.803 and 0.556, 83.0 % and 100.0 %, and 77.6 % and 11.2 %, respectively (P < 0.001). CONCLUSIONS: ARFI elastography shows superior performance over conventional US, particularly when combined with US, in predicting CLNM in PTC patients. KEY POINTS • Conventional ultrasound is useful in predicting cervical lymph node metastasis preoperatively. • Virtual touch tissue imaging area ratio is the strongest predicting factor. • Predictive performance is markedly improved by combining ultrasound characteristics with VAR. • Acoustic radiation force impulse elastography may be a promising complementary tool.