Abstract
Thyroid nodules are common incidental findings. Most of them are benign, but many unnecessary fine-needle aspiration procedures, core biopsies, and even thyroidectomies or non-invasive treatments have been performed. To improve thyroid nodule characterization, the use of multiparametric ultrasound evaluation has been encouraged by most experts and several societies. In particular, US elastography for assessing tissue stiffness and CEUS for providing insight into vascularization contribute to improved characterization. Moreover, the application of AI, particularly machine learning and deep learning, enhances diagnostic accuracy. Furthermore, AI-based computer-aided diagnosis (CAD) systems, integrated into the diagnostic process, aid in risk stratification and minimize unnecessary interventions. Despite these advancements, challenges persist, including the need for standardized TIRADS, the role of US elastography in routine practice, and the integration of AI into clinical protocols. However, the integration of clinical information, laboratory information, and multiparametric ultrasound features remains crucial for minimizing unnecessary interventions and guiding appropriate treatments. In conclusion, ultrasound plays a pivotal role in thyroid nodule management. Open questions regarding TIRADS selection, consistent use of US elastography, and the role of AI-based techniques underscore the need for ongoing research. Nonetheless, a comprehensive approach combining clinical, laboratory, and ultrasound data is recommended to minimize unnecessary interventions and treatments.
Keywords Thyroid nodule characterization - US-Elastography - TIRADS - CEUS - AI
