not yet been well documented. This study aimed to establish a practical model for thyroid nodule discrimination.\nMethods: Records for 2984 patients who underwent thyroidectomy were analyzed. Clinical, laboratory, and US\nvariables were assessed retrospectively. Multivariate logistic regression analysis was performed and a mathematical\nmodel was established for malignancy prediction.\nResults: The results showed that the malignant group was younger and had smaller nodules than the benign group\n(43.5 Ã?± 11.6 vs. 48.5 Ã?± 11.5 y, p < 0.001; 1.96 Ã?± 1.16 vs. 2.75 Ã?± 1.70 cm, p < 0.001, respectively). The serum thyrotropin\n(TSH) level (median = 1.63 mIU/L, IQR (0.89ââ?¬â??2.66) vs. 1.19 (0.59ââ?¬â??2.10), p < 0.001) was higher in the malignant group than\nin the benign group. Patients with malignancies tested positive for anti-thyroglobulin antibody (TGAb) and anti-thyroid\nperoxidase antibody (TPOAb) more frequently than those with benign nodules (TGAb, 30.3% vs. 15.0%, p < 0.001;\nTPOAb, 25.6% vs. 18.0%, p = 0.028). The prevalence of ultrasound (US) features (irregular shape, ill-defined margin,\nsolid structure, hypoechogenicity, microcalcifications, macrocalcifications and central intranodular flow) was\nsignificantly higher in the malignant group. Multivariate logistic regression analysis confirmed that age (OR = 0.963, 95%\nCI = 0.934ââ?¬â??0.993, p = 0.017), TGAb (OR = 4.435, 95% CI = 1.902ââ?¬â??10.345, p = 0.001), hypoechogenicity (OR = 2.830, 95%\nCI = 1.113ââ?¬â??7.195, p = 0.029), microcalcifications (OR = 4.624, 95% CI = 2.008ââ?¬â??10.646, p < 0.001), and central intranodular\nflow (OR = 2.155, 95% CI = 1.011ââ?¬â??4.594, p < 0.05) were independent predictors of thyroid malignancy. A predictive\nmodel including four variables (age, TGAb, hypoechogenicity and microcalcification) showed an optimal discriminatory\naccuracy (area under the curve, AUC) of 0.808 (95% CI = 0.761ââ?¬â??0.855). The best cut-off value for prediction was 0.52,\nachieving sensitivity and specificity of 84.6% and 76.3%, respectively.\nConclusion: A predictive model of malignancy that combines clinical, laboratory and sonographic characteristics\nwould aid clinicians in avoiding unnecessary procedures and making better clinical decisions.
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