超声是甲状腺结节检测和分类的首选成像方法。最近，超声可观察到的某些特征被等同于潜在的恶性。本次回顾性队列研究旨在验证假设：峨嵋山乳头状癌（MTC）、乳头状癌（PTC）、滤泡状癌（FTC）和滤泡甲状腺腺瘤（FTA）的放射组学特征显示出明显的超声特征。利用人工神经网络模型证明概念，这些超声特征被用作输入数据。共纳入了148名患者进行研究，所有患者都确认为四个命名类别中的一个的甲状腺病理。通过标准化方案获得了术前超声配置文件。神经网络由七个输入神经元、三个隐藏层，分别具有50、250和100个神经元，以及一个输出层组成。框线轮廓、结构和钙化的放射组学特征根据结节类型差异显著（p = 0.025、p = 0.032和p = 0.0002）。人工神经网络分析在区别这些类别时的准确度范围从0.59到0.98（95%置信区间为0.57-0.99），正、负预测范围分别为0.41-0.99和0.78-0.97。我们的数据表明，一些MTC、PTC、FTC和FTA具有独特的超声特征。然而，这些特征的显著重叠可能会妨碍明确的分类。进一步的前瞻性研究应包括更多的患者和结节数，以及多中心方面的访问，以确定这种神经网络是否有益于甲状腺肿瘤的分类。©2023。作者。

Ultrasound is the first-line imaging modality for detection and classification of thyroid nodules. Certain features observable by ultrasound have recently been equated with potential malignancy. This retrospective cohort study was conducted to test the hypothesis that radiomics of the four categorical divisions (medullary [MTC], papillary [PTC], or follicular [FTC] carcinoma and follicular thyroid adenoma [FTA]) demonstrate distinctive sonographic characteristics. Using an artificial neural network model for proof of concept, these sonographic features served as input.A total of 148 patients were enrolled for study, all with confirmed thyroid pathology in one of the four named categories. Preoperative ultrasound profiles were obtained via standardized protocols. The neural network consisted of seven input neurons; three hidden layers with 50, 250, and 100 neurons, respectively; and one output layer.Radiomics of contour, structure, and calcifications differed significantly according to nodule type (p = 0.025, p = 0.032, and p = 0.0002, respectively). Levels of accuracy shown by artificial neural network analysis in discriminating among categories ranged from 0.59 to 0.98 (95% confidence interval [CI]: 0.57-0.99), with positive and negative predictive ranges of 0.41-0.99 and 0.78-0.97, respectively.Our data indicate that some MTCs, PTCs, FTCs, and FTAs have distinctive sonographic characteristics. However, a significant overlap of these characteristics may impede an explicit classification. Further prospective investigations involving larger patient and nodule numbers and multicenter access should be pursued to determine if neural networks of this sort are beneficial, helping to classify neoplasms of the thyroid gland.© 2023. The Author(s).