Clinical Value of Ultrasound in Diagnosing Thyroid Nodules
Objective To explore clinical value of ultrasound in diagnosing thyroid nodules.Methods The paper chose 600 patients with thyroid nodules from May 2017 to May 2023,and divided into an observation group(treated with ultrasound diagnosis)based on different diagnostic methods,control group(with gold standard of pathological examination).Cccuracy of benign and malignant nodules,ultrasound imaging manifestations,pathological and calcification ultrasound types,and pathological relationship were compared and analyzed between thyroid nodules and different ultrasound subtypes of calcification.Results with diagnostic gold standard,accuracy rate of benign and malignant tumors in control group was 100.00％.Observation group had 450 cases of benign tumors(93.75％)and 105 cases of malignant tumors(87.50％).Results confirmed that diagnostic accuracy in control group was higher than study group,difference was not statistically significant(P>0.05).There was statistically significant difference in morphology of benign and malignant nodules,microcalcifications,clear boundaries,halos,and cystic solid ultrasound effects in observation group(P<0.05),there was no significant difference in low echo contrast(P>0.05).Among 105 malignant patients,there were 2 cases of undifferentiated cancer,2 cases of lymphoma,70 cases of papillary carcinoma,26 cases of follicular carcinoma,and 5 cases of myeloid carcinoma.Among 450 benign patients,there were 300 cases of thyroid adenoma and 150 cases of thyroid enlargement.Comparative analysis of ultrasound classification between benign and malignant calcifications found,there was statistically significant difference between type I and type Ⅱ(P<0.05),while type Ⅲ had no statistically significant difference(P>0.05).Conclusions Ultrasound diagnosis can achieve accuracy close to pathological diagnosis for patients with thyroid nodules,with advantages of cost-effectiveness,simple operation,non-invasive and high accuracy,which is suitable for clinical promotion.
万方数据
维普
