David Emanuele, Grazhdani Hektor, Tattaresu Giuliana, Pittari Alessandra, Foti Pietro Valerio, Palmucci Stefano, Spatola Corrado, Lo Greco Maria Chiara, Inì Corrado, Tiralongo Francesco, Castiglione Davide, Mastroeni Giampiero, Gigli Silvia, Basile Antonio
Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University Hospital Policlinic "G. Rodolico-San Marco", 95123 Catania, Italy.
Department of Translational and Precision Medicine, "Sapienza" University of Rome, 00185 Rome, Italy.
Biomedicines. 2024 Jul 26;12(8):1676. doi: 10.3390/biomedicines12081676.
Ultrasound (US) is the primary tool for evaluating patients with thyroid nodules, and the risk of malignancy assessed is based on US features. These features help determine which patients require fine-needle aspiration (FNA) biopsy. Classification systems for US features have been developed to facilitate efficient interpretation, reporting, and communication of thyroid US findings. These systems have been validated by numerous studies and are reviewed in this article. Additionally, this overview provides a comprehensive description of the clinical and laboratory evaluation of patients with thyroid nodules, various imaging modalities, grayscale US features, color Doppler US, contrast-enhanced US (CEUS), US elastography, FNA biopsy assessment, and the recent introduction of molecular testing. The potential of artificial intelligence in thyroid US is also discussed.
超声(US)是评估甲状腺结节患者的主要工具,评估的恶性风险基于超声特征。这些特征有助于确定哪些患者需要细针穿刺(FNA)活检。已开发出超声特征分类系统,以促进甲状腺超声检查结果的有效解读、报告和交流。这些系统已得到大量研究的验证,本文将对其进行综述。此外,本综述全面描述了甲状腺结节患者的临床和实验室评估、各种成像方式、灰阶超声特征、彩色多普勒超声、对比增强超声(CEUS)、超声弹性成像、FNA活检评估以及最近引入的分子检测。还讨论了人工智能在甲状腺超声中的潜力。