基于人工智能的影像组学在甲状腺结节临床应用的研究进展

Research progress of clinical application of AI-based radiomics in thyroid nodule

对于甲状腺结节,传统影像学诊断通常基于医生肉眼观察影像图像,结果判读受观察者主观影响,且无法识别图像中隐藏的深层信息。影像组学是一种新兴的无创技术,通过高通量的特征提取算法将普通影像图像转变为定量数据特征,从而反映病变潜在的生理及病理学特征。人工智能(AI)的加入,使影像组学成为临床应用研究的热点,在甲状腺结节良恶性鉴别、颈部淋巴结转移预测等方面取得了一定成果,可为甲状腺结节术前决策提供重要信息。该文综述了超声、CT、MRI以及PET/CT多领域影像组学结合AI技术在甲状腺结节临床应用中的研究进展。

For thyroid nodules,traditional imaging diagnosis is usually based on the image observed by the doctor's naked eyes,and the result interpretation is subject to the subjective influence of the observer,and the deep information hidden in the image cannot be recognized.Radiomics is a new non-invasive technology,which transforms ordinary image into quantitative data features through high-throughput feature extraction algorithms,so as to reflect the underlying physiological and pathological features of lesions.With the application of artificial intelligence(AI)technology,radiomics has become a hot spot of clinical application research,and certain achievements have been made in the identification of benign and malignant thyroid nodules and the prediction of cervical lymph node metastasis,which can provide important information for the preoperative decision-making of thyroid nodules.This paper reviews the research progress of ultrasound,CT,MRI and PET/CT multi-domain imaging integrated with AI technology in the clinical application of thyroid nodules.