Value of nomogram model based on ultrasonographic parameters in differentiating benign and malignant thyroid nodules
Objective To investigate the value of nomogram model based on ultrasonographic parameters in differentiating benign and malignant thyroid nodules.Methods A total of 152 patients with thyroid nodules admitted in Yuhang District Third People's Hospital and Hangzhou First People's Hospital from May 2020 to December 2022 were included in the study.Adobe Photoshop CS6 software was used to measure the gray scale values of thyroid nodules,thyroid tissue and muscle tissue on ultrasonographic images,and the ultrasonic gray scale ratios were calculated.Multivariate logistic regression was used to analyze the influencing factors of benign and malignant nodules,based on which a nomogram model was developed to differentiate benign and malignant thyroid nodules,and ROC curve was used to analyze its differentiating efficiency.Results A total of 157 thyroid nodules were detected in 152 patients,including 77 benign nodules and 80 malignant ones.Multivariate logistic regression analysis showed that the maximum diameter(OR=0.823,95％CI:0.734-0.923)and nodule-muscle gray scale ratio(OR=0.207,95％CI:0.057-0.749),edge case(OR=0.298,95％CI:0.130-0.682),aspect ratio(OR=0.177,95％CI:0.071-0.441)were independent factors influencing factors of benign and malignant thyroid nodules(all P＜0.05).Incorporating these four ultrasonic features,a nomogram model was generated to differentiate benign and malignant thyroid nodules.The area under ROC curve(AUC)of the model in differentiating benign and malignant thyroid nodules was 0.845(95％CI:0.783-0.907),and the sensitivity and specificity were 0.787 and 0.844,respectively,and the accuracy was 0.815,when the Yodon index was the highest.Conclusion The nomogram model based on the ratio of ultrasonic nodule to muscle gray scale,the maximum diameter of the nodule,the aspect ratio and the edge of the nodule has a good prediction effect in differentiating the benign and malignant thyroid nodules.
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