The Value of Contrast-Enhanced Ultrasound in the Detection of Sentinel Lymph Nodes in Malignant Melanoma.

OBJECTIVE: To evaluate the location and characterization value of contrast-enhanced ultrasound (CEUS) in the detection of sentinel lymph nodes (SLNs) in malignant melanoma. METHODS: SLNs and the lymph node network were tracked by subcutaneous injection of ultrasonic contrast agent around the tumor and preoperative localization, and qualitative analyses were performed. The SLNs were also detected by the intraoperative subcutaneous injection of carbon nanoparticles, and the findings were compared with lymph nodes located by CEUS. The accuracy of the preoperative lymph node identification was evaluated by the results of postoperative pathology, which served as the gold standard of detection. RESULTS: In 47 patients with malignant melanoma, the mean number of SLNs detected by CEUS was 1.72 ± 0.10, while that by carbon nanoparticle administration it was 1.79 ± 1.07 (P = .371 > .05). Seven cases of lymph node metastasis were detected by CEUS, with a sensitivity of 70.0%, specificity of 97.3%, positive predictive value of 87.5%, negative predictive value of 92.3%, and accuracy of 91.5%. There was high consistency between the findings of CEUS and pathology in differentiating benign and malignant lymph nodes (kappa = 0.726, χ2  = 25.243, P < .001). CONCLUSIONS: CEUS can localize and differentiate SLNs in malignant melanoma, and thus, may potentially guide clinical treatment in the future.

A Combination of Contrast-Enhanced Ultrasound and Thyroglobulin Level in Fine-Needle Aspirates Improves Diagnostic Accuracy for Metastatic Lymph Nodes of Papillary Thyroid Carcinoma.

OBJECTIVE: To evaluate the diagnostic performance of contrast-enhanced ultrasound (CEUS) combined with thyroglobulin (Tg) levels in fine-needle aspirates (FNA) washout fluid (FNA-Tg) in diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC) patients. METHODS: Data from 190 LNs in 167 patients suspected of metastasis from the US between November 2018 and September 2020 were included. All subjects underwent FNA, CEUS, and FNA-Tg examinations. The final outcomes were confirmed by histopathological or cytological examination or follow-up imaging. Data were analyzed using the Wilcoxon rank-sum or chi-squared test. The diagnostic efficacy of FNA, CEUS, and FNA-Tg in diagnosing LNs was compared. RESULTS: A cutoff value of 6.15 ng/ml (AUC 0.925, 95% confidence interval (CI) 0.885-0.966) successfully identified metastatic LNs. FNA missed 58 LN metastases, of these, 94.8% (55/58) were correctly diagnosed using the combination of CEUS and FNA-Tg. FNA-Tg showed higher sensitivity (90.2%), NPV (86.1%) and accuracy (88.9%) than either FNA (48.2, 57.4 and 69.5%, respectively) or CEUS (82.1, 67.7 and 70.5%, respectively) alone. The combination of CEUS, FNA and FNA-Tg resulted in maximal sensitivity (100%) and NPV (100%) but reduced specificity (51.3%) and overall diagnostic accuracy (80.0%). After adding FNA-Tg to discordant samples between CEUS and FNA, 81.9% of LNs (77/94) were correctly diagnosed. CONCLUSIONS: The combination of FNA, FNA-Tg and CEUS was found to be a promising imaging tool in detecting metastatic LNs in PTC patients.

A combination of ultrasound and contrast-enhanced ultrasound improves diagnostic accuracy for the differentiation of cervical tuberculous lymphadenitis from primary lymphoma.

OBJECTIVES: To present a method combining ultrasound (US) and contrast-enhanced ultrasound (CEUS) features for differential diagnosis of cervical tuberculous lymphadenitis (CTL) and primary lymphoma. METHODS: A total of 155 patients with CTL (n = 49) and lymphoma (n = 106) who underwent US and CEUS were retrospectively included. The features extracted from US and CEUS and the significant clinical data were created three models using the least absolute shrinkage and selection operator and logistic regression analysis. The diagnostic performance of the models was assessed using the area under the curve (AUC). RESULTS: The combined model outperformed US model and CEUS model in distinguish CTL from lymphoma achieved favorable performances in training set and validation set with AUCs of 0.958 and 0.946 as well as high accuracies (91.7% and 87.2%), sensitivities (95.9% and 84.4%) and specificities (82.4% and 93.3%). Delong's test showed that among the three models, combined model was significantly different from the other two models in training set (p = 0.011 and 0.029, respectively) and validation set (p = 0.018 and 0.001, respectively). CONCLUSIONS: A combination of US and CEUS achieved good diagnostic performance in differentiating lymphoma and CTL, which might aid in clinical decision-making.