Non-contrast-enhanced ultrasound microvasculature imaging and quantification for differentiation of thyroid nodules

Abstract

Observation of thyroid nodules in ultrasound examinations is a common occurrence. However, accurate diagnosis of the malignancy status of these nodules is a challenging task relying only on conventional ultrasound imaging. Therefore, increasing the sensitivity and specificity of non-invasive screening methods can be helpful in reducing the probability of missed diagnosis and decreasing the rate of unnecessary biopsies. In this feasibility study, a quantitative ultrasound microvasculature imaging technique is applied to ultrasound data collected from a cohort of 92 patients. This technique consists of first enabling the visualization of vascular networks inside the scanned nodules, followed by a morphometric analysis framework with a number of biomarkers to distinguish between benign and malignant thyroid nodules. Statistical tests are performed to find significant differences between the distribution of the values of the biomarkers. Subsequently, these biomarkers are utilized to train classification models that solely benefit from morphological biomarkers and those trained with additional clinical features. The receiver operating characteristic (ROC) curve analysis of these models, which helps us evaluate their predictive power, is presented. In our preliminary results, the model trained with combined morphological and clinical features achieves an AUC of 0.90, a sensitivity of 0.81, and a specificity of 0.79. [Work supported by R01CA239548.]