The application study of the virtual touch tissue image quantification technique in the benign or malignancy diagnosis of cervical lymph nodes

Abstract

Objective To investigate the application value of the virtual touch tissue image quantification (VTIQ) technique in the differential diagnosis of malignant and benign lymph nodes of the neck. Methods The imaging data of 107 lymph nodes in 84 patients proven by biopsy pathology or operating on conventional ultrasound and VTIQ were retrospectively analyzed. The lymph nodes were examined by conventional ultrasound firstly. And then, the maximum, minimum, median and average of shear wave velocity (SWV) values were obtained from multiple SWV measurement under the VTIQ speed mode. According to pathology results, ROC curve were plotted to determine the most accurate SWV value and the cut-off value for differential diagnosis. Simultaneously, univariate and multivariate logistic regression analysis were used to analyze risk factors. Results According to the pathology results, there were 52 malignant lymph nodes and 55 benign lymph nodes. Microcalcification, SWVmean and color distributtion of internal blood were significantly correlated to abnormal lymph node (P<0.05), especially in patients with microcalcification and the high of the SWVmean. The SWVmax, SWVmin, SWVmedian and SWVmean on VTIQ of malignant and benign lymph nodes were (2.98±0.62), (2.38±0.44), (2.60±0.51), (2.63±0.49)m/s, and (3.58±0.62), (2.97±0.48), (3.26±0.50), (3.26±0.51)m/s, respectively, there were significant differences between malignant and benign lymph nodes in SWVmax, SWVmin, SWVmedian, and SWVmean on VTIQ (t=-5.03, -6.60, -6.74, -6.45, P<0.01). Based on ROC curve analysis, the SWVmean value in the lymph nodes was the best value for the diagnosis efficacy in comparison with other SWV values. The cut-off value of VTIQ mean was 2.68 m/s. According to ROC curve analysis, the sensitivity, specificity and Youden index for VTIQ were 86.53%, 78.18%, 0.65, respectively. Conclusions VTIQ technique plays an important role in differential diagnosis of lymph nodes and the SWVmean is the best parameter for differential diagnosis. Key words: Elasticity imaging techniques; Lymph nodes; Diagnosis, differential; Virtual touch tissue image quantification