超声影像组学预测甲状腺乳头状癌颈部淋巴结转移的研究进展
Research Progress in Ultrasound Radiomics for Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Carcinoma
DOI: 10.12677/jcpm.2026.54266, PDF,   
作者: 王世杰：济宁医学院临床医学院，山东 济宁；孔庆锋*：济宁市第一人民医院超声医学科，山东 济宁
关键词: 超声影像组学；甲状腺乳头状癌；颈部淋巴结转移；Ultrasound Radiomics； Papillary Thyroid Carcinoma； Cervical Lymph Node Metastasis
摘要: 甲状腺乳头状癌(PTC)占全部甲状腺癌的80%~90%，尽管其预后相对较好，但其颈部淋巴结转移(CLNM)概率较高，而传统超声(含B型超声)检查诊断PTC CLNM存在局限性，且易受医师临床经验影响。随着计算机与人工智能技术发展，超声影像组学(USR)逐渐广泛应用于甲状腺领域，该技术通过勾画感兴趣区域(ROI)、提取特征量化疾病信息，结合机器学习、深度学习等方法构建模型，可用于预测PTC CLNM及甲状腺疾病相关诊疗评估。本文综述了USR的工作流程、在预测PTC CLNM方面的研究进展，同时探讨了该技术的局限性及未来改进方向，旨在为临床精准诊疗提供参考。
Abstract: Papillary thyroid carcinoma (PTC) accounts for 80%~90% of all thyroid cancers. Although it has a relatively favorable prognosis, it carries a high risk of cervical lymph node metastasis (CLNM). However, conventional ultrasound (including B-mode ultrasound) has limitations in diagnosing CLNM in PTC and is susceptible to the influence of physicians’ clinical experience. With the advancement of computer and artificial intelligence technologies, ultrasound radiomics (USR) has been increasingly applied in the field of thyroid diseases. This technique quantifies disease information by delineating regions of interest (ROI) and extracting features, and constructs models combined with machine learning, deep learning and other methods, which can be used to predict CLNM in PTC and evaluate the diagnosis and treatment of thyroid-related diseases. This article reviews the workflow of USR and its research progress in predicting CLNM in PTC, discusses its limitations and future directions for improvement, and aims to provide a reference for precise clinical diagnosis and treatment.
文章引用：王世杰, 孔庆锋. 超声影像组学预测甲状腺乳头状癌颈部淋巴结转移的研究进展[J]. 临床个性化医学, 2026, 5(4): 417-426. https://doi.org/10.12677/jcpm.2026.54266

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