The analysis of differential diagnosis of benign and malignant thyroid nodules based on ultrasound reports.

BACKGROUND

Thyroid cancer is a common endocrine tumor, the incidence of which is increasing each year. Early diagnosis and treatment can effectively prevent thyroid cancer. This article uses Chinese's ultrasound reports to determine the value of early diagnosis.

METHODS

The clinical data center of the First Affiliated Hospital of Nanjing Medical University was screened for patients diagnosed with a thyroid nodule, who had undergone a thyroid function test, ultrasound records and pathological assessment. A total of 811 patients with a total of 1,290 pathologically confirmed nodules (506 benign and 784 malignant) were enrolled. Logistic regression was used to analyze the variables that significantly affected malignant nodules. The sensitivity and specificity of ultrasound thyroid imaging-reporting and data system (TI-RADS) classification results for benign and malignant tumors were calculated.

RESULTS

The age of the patients had a very significant difference in the classification of benign and malignant nodules (P<0.001), and the marital status was significantly different (P<0.05). Gender and medical insurance had no significant effect (P>0.05). Thyroglobulin (TG), free thyroxine (FT4), and free triiodothyronine (FT3) had significant effects (P=0.003) on the incidence of malignant nodules in patients, while thyroid-stimulating hormone (TSH) had no significant effect (P>0.05). Ultrasound analysis showed a Youden's index of 78.97%, a positive predictive value of 93.20%, and a negative predicted value of 84.10% at the most excellent classification effect. The sensitivity was 89.0%, the specificity was 89.9%; much greater than the classification model based on the thyroid function test (sensitivity =80.6%, specificity =55.8%).

CONCLUSIONS

The present study verifies the effectiveness of using TI-RADS classification for diagnosis of benign and malignant thyroid nodules, and explores the use of new analysis methods for clinical data. To reduce dependence on the doctors, ultrasound image data and clinical phenotypic data can be further used to assist clinical decision making.