Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Puerto Rico, Medical Sciences Campus, Paseo Dr. Jose Celso Barbosa, San Juan 00921, Puerto Rico.
Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Florida, 1600 SW Archer Road, Gainesville, FL 32610, USA.
Endocrinol Metab Clin North Am. 2022 Jun;51(2):305-321. doi: 10.1016/j.ecl.2021.12.002. Epub 2022 May 4.
Clinical evidence supports the association of ultrasound features with benign or malignant thyroid nodules and serves as the basis for sonographic stratification of thyroid nodules, according to an estimated thyroid cancer risk. Contemporary guidelines recommend management strategies according to thyroid cancer risk, thyroid nodule size, and the clinical scenario. Yet, reproducible and accurate thyroid nodule risk stratification requires expertise, time, and understanding of the weight different ultrasound features have on thyroid cancer risk. The application of artificial intelligence to overcome these limitations is promising and has the potential to improve the care of patients with thyroid nodules.
临床证据支持超声特征与甲状腺结节良恶性之间的关联，并为基于甲状腺癌风险的甲状腺结节超声分层提供了依据。根据甲状腺癌风险、甲状腺结节大小和临床情况，当代指南推荐了相应的管理策略。然而，甲状腺结节风险分层的可重复性和准确性需要专业知识、时间以及对不同超声特征对甲状腺癌风险影响的理解。人工智能的应用有望克服这些限制，并有可能改善甲状腺结节患者的护理。
