[The perfusion pattern of lymphadenopathy in contrast-enhanced ultrasonograph. under different reference conditions ].

OBJECTIVE: To determine the perfusion pattern of lymphadenopathy in contrast-enhanced ultrasonography (CEUS) under different reference conditions. METHODS: The CEUS perfusion patterns of 78 superficial lymph node lesions were compared with their pathology results. Time-intensity curves were used for comparison between benign and malignant lymph nodes. RESULTS: Inhomogeneous hyperenhancement was the main perfusion pattern (7/17, 41. 2%) in metastatic lymph nodes; compared with homogeneous hyperenhancement (2/4, 50. 0%) in lymphoma, homogeneous hyperenhancement and isoenhancement (6/52, 11. 5%) in reactive lymph nodes, and circle enhancement (2/4,50. 0%) in tuberculosis. Benign lymph nodes showed different mean value, peak intensity and area under the curve compared with their surrounding arteries (P<0. 05). But the differences in mean value, rise time, time to peak, peak intensity and the area under the curve between benign lymphadenopathy and their surrounding tissues were not statistically significant (P>0. 05). Malignant lymph nodes showed different mean value and peak intensity compared with their surrounding arteries and tissues (P<0. 05). The differences in time to peak between malignant lymph nodes and their surrounding tissues were also statistically significant (P< 0. 05). CONCLUSION: Different CEUS perfusion patterns are associated with different types of lymph node lesions. Time intensity curves with surrounding tissues as reference condition offer great values for the differential diagnosis of superficial lymphadenopathy.

Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.

Background: Thyroid cancer (TC) prone to cervical lymph node (CLN) metastasis both before and after surgery. Ultrasonography (US) is the first-line imaging method for evaluating the thyroid gland and CLNs. However, this assessment relies mainly on the subjective judgment of the sonographer and is very much dependent on the sonographer's experience. This prospective study was designed to construct a machine learning model based on contrast-enhanced ultrasound (CEUS) videos of CLNs to predict the risk of CLN metastasis in patients with TC. Methods: Patients who were proposed for surgical treatment due to TC from August 2019 to May 2020 were prospectively included. All patients underwent US of CLNs suspected of metastasis, and a 2-minute imaging video was recorded. After target tracking, feature extraction, and feature selection through the lymph node imaging video, three machine learning models, namely, support vector machine, linear discriminant analysis (LDA), and decision tree (DT), were constructed, and the sensitivity, specificity, and accuracy of each model for diagnosing lymph nodes were calculated by leave-one-out cross-validation (LOOCV). Results: A total of 75 lymph nodes were included in the study, with 42 benign cases and 33 malignant cases. Among the machine learning models constructed, the support vector machine had the best diagnostic efficacy, with a sensitivity of 93.0%, a specificity of 93.8%, and an accuracy of 93.3%. Conclusions: The machine learning model based on US video is helpful for the diagnosis of whether metastasis occurs in the CLNs of TC patients.

Partially cystic thyroid cancer on conventional and elastographic ultrasound: a retrospective study and a machine learning-assisted system.

BACKGROUND: Thyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning-assisted system based on ultrasound (US) and elastography. METHODS: Patients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. RESULTS: A total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model. CONCLUSIONS: US features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.