This study was aimed to investigate the value of logistic regression analysis model of ultrasound characteristics in differentiating nodular goiter and papillary thyroid carcinoma. Methods: A total of 184 patients with nodular goiter and thyroid cancer confirmed by surgery and pathology in our hospital were collected. Among them, 102 patients with nodular goiter and 82 patients with papillary thyroid carcinoma. All patients underwent conventional ultrasonography and contrast enhanced ultrasonography before surgery. The ultrasound image features were compared and analyzed, and univariate analysis was performed by χ2 test. With statistically significant indicators as independent variables, then multiple logistic regression analysis was performed, and receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic performance of the logistic regression model. Results: Univariate analysis showed that age, lesion number, shape, margin, aspect ratio, calcification, micro calcification, enhancement mode, internal echo, internal structure, cystic degeneration and other factors were statistical significance (P<0.05).Multivariate analysis showed that irregular shape, mass effect, aspect ratio greater than 1, internal nodule echo, micro calcification and contrast-enhanced ultrasound were independent predictors for differentiating nodular goiter from thyroid cancer (P<0.05). The area under the ROC curve constructed by the independent predictors was 0.954, and the sensitivity and specificity were 95.1% and 83.3%, respectively. Conclusion: The logistic regression analysis model of ultrasound features has important value in the identification of nodular goiter and thyroid cancer.
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