A predictive model of thyroid malignancy using clinical, biochemical and sonographic parameters for patients in a...

Thyroid nodules are highly prevalent, but a robust, feasible method for malignancy differentiation has not yet been well documented. This study aimed to establish a practical model for thyroid nodule discrimination.

Records for 2984 patients who underwent thyroidectomy were analyzed. Clinical, laboratory, and US variables were assessed retrospectively. Multivariate logistic regression analysis was performed and a mathematical model was established for malignancy prediction.

The results showed that the malignant group was younger and had smaller nodules than the benign group (43.5 ± 11.6 vs. 48.5 ± 11.5 y, p < 0.001; 1.96 ± 1.16 vs. 2.75 ± 1.70 cm, p < 0.001, respectively). The serum thyrotropin (TSH) level (median = 1.63 mIU/L, IQR (0.89-2.66) vs. 1.19 (0.59-2.10), p < 0.001) was higher in the malignant group than in the benign group. Patients with malignancies tested positive for anti-thyroglobulin antibody (TGAb) and anti-thyroid peroxidase antibody (TPOAb) more frequently than those with benign nodules (TGAb, 30.3% vs. 15.0%, p < 0.001; TPOAb, 25.6% vs. 18.0%, p = 0.028). The prevalence of ultrasound (US) features (irregular shape, ill-defined margin, solid structure, hypoechogenicity, microcalcifications, macrocalcifications and central intranodular flow) was significantly higher in the malignant group. Multivariate logistic regression analysis confirmed that age (OR = 0.963, 95% CI = 0.934-0.993, p = 0.017), TGAb (OR = 4.435, 95% CI = 1.902-10.345, p = 0.001), hypoechogenicity (OR = 2.830, 95% CI = 1.113-7.195, p = 0.029), microcalcifications (OR = 4.624, 95% CI = 2.008-10.646, p < 0.001), and central intranodular flow (OR = 2.155, 95% CI = 1.011-4.594, p < 0.05) were independent predictors of thyroid malignancy. A predictive model including four variables (age, TGAb, hypoechogenicity and microcalcification) showed an optimal discriminatory accuracy (area under the curve, AUC) of 0.808 (95% CI = 0.761-0.855). The best cut-off value for prediction was 0.52, achieving sensitivity and specificity of 84.6% and 76.3%, respectively.

A predictive model of malignancy that combines clinical, laboratory and sonographic characteristics would aid clinicians in avoiding unnecessary procedures and making better clinical decisions.