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.

[Diagnostic value of Kimura's disease with ultrasonography].

OBJECTIVE: To explore the diagnostic value of ultrasonography for Kimura's disease (KD). METHODS: Retrospectively analyzed the results of two dimension and color Doppler of 21 Kimura's disease carried by 19 patients who were resected and confirmed by pathologhy. RESULTS: Twenty-one KD cases were detected by the ultrasound while none of them was diagnosed by ultrasonography. 21 cases were showed in the lymph nodes (42.9%, 9/21), parotid gland (4.8%, 1/21) and soft tissues (52.4%, 11/21). The masses both in the lymph and in the parotid gland marked hypoechogenicity and round or like-round. Cases in the soft tissues marked hypoechogencity or hyperechogenicity. Color Doppler flow pattern II to III were showed in 19 cases (90.5%, 19/21). CONCLUSION: Ultrasonography enables a confident preoperative diagnosis of Kimura's disease.

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.

Identification of Metastasis-Associated Genes in Triple-Negative Breast Cancer Using Weighted Gene Co-expression Network Analysis.

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST, and TSHZ2, which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2, and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2, and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.