Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

Abstract

Thyroid cancer affects 1.3 percent of the population, with rates of occurrence rising in recent years (approximately 2 percent per year). Thyroid cancer is a common endocrine cancer with an annual increase in occurrence. Although the general prognosis for differentiated subtypes is favorable, the rate of mortality linked with thyroid cancer has been steadily progressing. The presence of suspicious thyroid nodules necessitates more diagnostic testing, including laboratory evaluation, additional imaging, and biopsy. For clinical staging and appropriate patient therapy design, accurate diagnosis is necessary. In this paper, we examined the application value of ultrasound imaging diagnosis in the clinical staging of thyroid tumor in this research. The benefit of early diagnosis is determined in this article using ultrasonography reports from Chinese patients. Images of benign and malignant thyroid nodules were collected and annotated in this work, and deep learning-based image recognition and diagnostic system was built utilizing the adaptive wavelet transform-based AdaBoost algorithm (AWT-AA). The system’s efficacy in diagnosing thyroid nodules was assessed, and the use of ultrasound imaging in clinical practice was studied. The variables that had a significant impact on malignant nodules were studied using logistic multiple regression analysis. The sensitivity and specificity of ultrasonography thyroid imaging reporting and data system (TI-RADS) categorization outcomes for benign and malignant tumors were also calculated.