Abstract
Objectives In the present study, two mathematical models were constructed based on the characteristics of US image to discriminate the benign and malignant thyroid nodules. Methods A retrospective study was conducted in 128 patients with thyroid nodules from 2016/8 to 2016/12 in the First Affiliated Hospital of Nanjing Medical University. There were totally 170 pathologically-confirmed thyroid nodules. The gray scale image and color Doppler flow imaging (CDFI) sonograms of each thyroid nodule was reviewed. The data set was analyzed by the partial least squares-discriminant analysis (PLS-DA) and logistic regression (Logistic). Then the two methods were used after selecting statistically significant variables by stepwise regression analysis. Result The true positive and negative rates of PLS-DA were 96.95% and 97.73%, respectively, which were significantly higher than the true positive rate (89.86%) and true negative rate (93.12%) of Logistic (P<0.05). After stepwise regression analysis, seven significant variables were selected including the echogenicity of thyroid, shape, margin, internal content, calcification, orientation and vascularity. Based on the selected variables, the true positive and negative rates of PLS-DA were 98.12% and 98.49%, while the true positive and negative rates of Logistic were 95.09% and 95.31%, respectively. Compared to the values before variable selection the true rates of both methods were improved (P<0.05). Moreover, the result of PLS-DA was better than that of Logistic (P<0.05). Conclusion PLS-DA and Logistic based on the ultrasonic image are useful in the diagnosis of thyroid nodules. Based on the variables selected by stepwise regression analysis, the diagnosis models were built and the accuracy rate of PLS-DA and Logistic could be improved. Moreover, PLS-DA seems to be more powerful than Logistic. Key words: Ultrasounds; Thyroid nodule; Partial least squares-discriminant analysis; Logistic regression; Mathematical model
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