Clinical value of grayscale ultrasound combined with real-time shear wave elastography nomogram in risk prediction of thyroid cancer.

OBJECTIVES: This study constructed a nomogram based on grayscale ultrasound features and real-time shear wave elastography (SWE) parameters to predict thyroid cancer. METHODS: Clinical data of 217 thyroid nodules of 201 patients who underwent grayscale ultrasound, real-time SWE, and thyroid function laboratory examination in Ma'anshan People's Hospital from January 2019 to December 2020 were retrospectively analyzed. The subjects were divided into a benign nodule group (106 nodules) and a malignant nodule group (111 nodules). The differences in grayscale ultrasound features, quantitative parameters of real-time SWE, and laboratory results of thyroid function between benign and malignant thyroid nodules were analyzed. We used a chi-square test for categorical variables and a t-test for continuous variables. Then, the independent risk factors for thyroid cancer were analyzed using multivariate logistic regression. Based on the independent risk factors, a nomogram for predicting thyroid cancer risk was constructed using the RMS package of the R software. RESULTS: Multivariate logistic regression showed that the grayscale ultrasound features of thyroid nodules were the shape, margin, echogenicity, and echogenic foci of the nodules,the maximum Young's modulus (SWE-max) of thyroid nodules, and the ratio of thyroid nodule and peripheral gland (SWE-ratio) measured by real-time SWE were independent risk factors for thyroid cancer (all p < 0.05), and the other variables had no statistical difference (p > 0.05). Based on the shape (OR = 5.160, 95% CI: 2.252-11.825), the margin (OR = 9.647, 95% CI: 2.048-45.443), the echogenicity (OR = 6.512, 95% CI: 1.729-24.524), the echogenic foci (OR = 2.049, 95% CI: 1.118-3.756), and the maximum Young's modulus (SWE-max) (OR = 1.296, 95% CI: 1.140-1.473), the SWE-ratio (OR = 2.001, 95% CI: 1.403-2.854) of the thyroid nodule to peripheral gland was used to establish the related nomogram prediction model. The bootstrap self-sampling method was used to verify the model. The consistency index (C-index) was 0.979, ROC curve was used to analyze the nomogram scores of all patients, and the AUC of nomogram prediction of thyroid cancer was 0.976, indicating that the nomogram model had high accuracy in the risk prediction of thyroid cancer. CONCLUSIONS: The nomogram model of grayscale ultrasound features combined with SWE parameters can accurately predict thyroid cancer.

The Diagnostic Significance of the BI-RADS Classification Combined With Automated Breast Volume Scanner and Shear Wave Elastography for Breast Lesions.

OBJECTIVE: We herein compared the diagnostic accuracy of the BI-RADS, ABVS, SWE, and combined techniques for the classification of breast lesions. METHODS: Breast lesions were appraised using the BI-RADS classification system as well as the combinations of BI-RADS plus ABVS (BI-RADS + ABVS) and BI-RADS plus SWE (BI-RADS + SWE), and both methods (BI-RADS + ABVS + SWE) by two specialties Medical Ultrasound physician. The Fisher's exact and χ2 tests were performed to compare the degree of malignancy for the various methods with a pathology ground truth. Receiver operating characteristic curves (ROC) were generated and the corresponding area under the curve (AUC) values were determined to test the diagnostic efficacy of the various methods and identify the optimal SWE cut-off indicative of malignancy. RESULTS: The incidence of the retraction phenomenon on ABVS images of the malignant group was significantly higher (P < .001) than that of the benign group. The specificity, sensitivity, and positive and negative predictive values of the BI-RADS classification were 88.72, 79.38, 83.70, and 85.50%, respectively. BI-RADS plus SWE-Max exhibited enhanced specificity, sensitivity, and positive and negative predictive values of 88.72, 92.78, 85.70, and 94.40%, respectively. Similarly, when BI-RADS + ABVS was utilized, the sensitivity and negative predictive value increased to 95.88 and 96.40%, respectively. BI-RADS + ABVS + SWE possessed the highest overall sensitivity (96.91%), specificity (94.74%), and positive (93.10%) and negative (97.70%) predictive values from all four indices. CONCLUSION: ABVS and SWE can reduce the subjectivity of BI-RADS. As a result, BI-RADS + ABVS + SWE resulted in the best diagnostic accuracy.

Prediction of carotid plaque by blood biochemical indices and related factors based on Fisher discriminant analysis.

OBJECTIVE: This study aims to establish the predictive model of carotid plaque formation and carotid plaque location by retrospectively analyzing the clinical data of subjects with carotid plaque formation and normal people, and to provide technical support for screening patients with carotid plaque. METHODS: There were 4300 subjects in the ultrasound department of Maanshan People's Hospital collected from December 2013 to December 2018. We used demographic and biochemical data from 3700 subjects to establish predictive models for carotid plaque and its location. The leave-one-out cross-validated classification, 600 external data validation, and area under the receiver operating characteristic curve (AUC) were used to verify the accuracy, sensitivity, specificity, and application value of the model. RESULTS: There were significant difference of age (F = - 34.049, p < 0.01), hypertension (χ2 = 191.067, p < 0.01), smoking (χ2 = 4.762, p < 0.05) and alcohol (χ2 = 8.306, p < 0.01), Body mass index (F = 15.322, p < 0.01), High-density lipoprotein (HDL) (F = 13.840, p < 0.01), Lipoprotein a (Lp a) (F = 52.074, p < 0.01), Blood Urea Nitrogen (F = 2.679, p < 0.01) among five groups. Prediction models were built: carotid plaque prediction model (Model CP); Prediction model of left carotid plaque only (Model CP Left); Prediction model of right carotid plaque only (Model CP Right). Prediction model of bilateral carotid plaque (Model CP Both). Model CP (Wilks' lambda = 0.597, p < 0.001, accuracy = 78.50%, sensitivity = 78.07%, specificity = 79.07%, AUC = 0.917). Model CP Left (Wilks' lambda = 0.605, p < 0.001, accuracy = 79.00%, sensitivity = 86.17%, specificity = 72.70%, AUC = 0.880). Model CP Right (Wilks' lambda = 0.555, p < 0.001, accuracy = 83.00%, sensitivity = 81.82%, specificity = 84.44%, AUC = 0.880). Model CP Both (Wilks' lambda = 0.651, p < 0.001, accuracy = 82.30%, sensitivity = 89.50%, specificity = 72.70%, AUC = 0.880). CONCLUSION: Demographic characteristics and blood biochemical indexes were used to establish the carotid plaque and its location discriminant models based on Fisher discriminant analysis (FDA), which has high application value in community screening.

Triglyceride-Glucose Index for the Diagnosis of Metabolic Syndrome: A Cross-Sectional Study of 298,652 Individuals Receiving a Health Check-Up in China.

Objective: We herein aim to explore the relationship between the triglyceride-glucose (TyG) index and metabolic syndrome (MS). Methods: We enrolled 298,652 individuals with an average age of 47.08 ± 12.94 years and who underwent health check-ups at the First Affiliated Hospital of Wuhu Wannan Medical College in this cross-sectional study from 2014 to 2016. We enlisted 125,025 women (41.86%) and 173,627 men (58.14%). The survey information included a questionnaire survey, a physical examination, and a laboratory examination. Results: The prevalence of MS increased gradually in the TyG-index subgroups (Q1, TyG <8.30; Q2, 8.30≤ TyG <8.83; and Q3, TyG ≥8.83). We noted significant differences in hypertension, hyperlipidemia, hyperglycemia, sex, age, body mass index (BMI), smoking and drinking habits, and estimated glomerular filtration rate between the TyG-index subgroups. Multiclass logistic regression analysis showed that the group with TyG <8.30 was the reference group, and the 8.30≤ TyG <8.83 and the TyG ≥8.83 groups exhibited a higher TyG index with MS, and a lower TyG index without MS disease. In the linear curve analysis of the TyG index and MS components, BMI, systolic blood pressure, and diastolic blood pressure showed upward trends, while high-density lipoprotein cholesterol showed no obvious trend in the TyG index at a range of 7.8-11.0. Receiver operating characteristic analysis was used to evaluate the predictive value of the TyG index, triglycerides, and fasting blood glucose for MS, and we found that the area under the TyG index curve was the largest (AUC = 0.89). Conclusion: There were associations between the TyG index and MS and its components, and the TyG index is therefore of great value in the early diagnosis of MS.

The combined effect of serum cystatin C and dyslipidemia on hypertension in a large health check-up population in China.

OBJECTIVE: Some studies have reported that both serum cystatin C (Cys C) and dyslipidemia are independently associated with hypertension. However, the combined effect of the two factors is still unknown. The present study was aimed at investigating the effect of Cys C combined with dyslipidemia on hypertension in a large health check-up population in China. METHODS: A total of 203 233 health check-up subjects from January 2011 to July 2016 were recruited into this cross-sectional study. A multivariate logistic regression model was used to evaluate the combined effect of Cys C and dyslipidemia on hypertension.RESULTS: In univariate analysis, Cys C, high-density lipoprotein cholesterol, low-density lipoprotein, total cholesterol, and triglycerides were independently correlated with hypertension (p < 0.001). A concentration-dependent combined effect of serum Cys C and dyslipidemia on hypertension was observed in multivariate regression analysis. When compared with Cys C of <0.82 mg/L, the risk of hypertension in Cys C of <0.82 mg/L with dyslipidemia, Cys C  of 0.82-0.94 mg/L with dyslipidemia, Cys C  of 0.94-1.08 mg/L with dyslipidemia, and Cys C  of ≥1.08 mg/L with dyslipidemia was increased 1.946 (95% confidence interval [CI]: 1.827-2.074), 1.973 (95% CI: 1.864-2.088), 2.047 (95% CI: 1.941-2.158), and 2.038 (95% CI: 1.937-2.143) folds, respectively, after adjustment.CONCLUSION: There was an association between hypertension and the combined effect of Cys C with dyslipidemia.