确定桥本甲状腺炎 (HT) 甲状腺结节 (TN) 的性质仍然是一个挑战。我们的目的是研究从 B 型超声 (B-US) 和对比增强超声 (CEUS) 图像中获得的多区域超声特征，以预测 HT 患者 TN 的恶性程度。B-US 和 193 个结节的 CEUS 图像（110 个恶性结节）这项单中心研究回顾性收集了 110 名患者的甲状腺瘤内 (In) 和瘤周 (Peri) 区域的超声特征。构建了In-B-US、Peri-B-US、In-CEUS、Peri-CEUS超声模型和叠加回归模型，通过比较受试者工作特征曲线下面积（ ROC）。训练和测试数据集中的 In-B-US、Peri-B-US、In-CEUS、Peri-CEUS 和堆叠回归模型的 AUC（95% CI）为 0.872（0.812，0.932）， 0.815(0.747, 0.882), 0.739(0.659, 0.819), 0.890(0.836, 0.943), 0.997(0.992, 1.000) 和 0.799(0.650, 0.948), 0.851(0.727, 0.97) 4), 0.622(0.440, 0.805), 0.742 (0.573, 0.911), 0.867(0.741, 0.992);灵敏度为82.8%、89.7%、71.3%、74.7%、96.6%和69.6%、78.3%、43.5%、78.3%、91.3%；特异性分别为 80.6%、58.2%、67.2%、91.0%、98.5% 和 93.8%、87.5%、93.3%、75.0%、81.2%。基于超声特征的叠加回归模型显示出良好的校准和判别能力。与堆叠回归模型相比，In-B-US 和 Peri-B-US 模型的 AUC 差异无统计学意义（P > 0.05）。然而，In-CEUS和Peri-CEUS模型之间的AUC差异显着（P < 0.05）。超声组学方法的应用可以有效预测伴有HT的TN的良恶性。通过甲状腺双区域、双模式的结合，提高超声模型的诊断性能。版权所有©2024大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

It remains a challenge to determine the nature of thyroid nodules (TNs) with Hashimoto's thyroiditis (HT). We aim to investigate the multiregional ultrasomics signatures obtained from B-mode ultrasound (B-US) and contrast-enhanced ultrasound (CEUS) images for predicting malignancy in TNs of patients with HT.B-US and CEUS images of 193 nodules (110 malignant and 83 benign nodules) from 110 patients were retrospectively collected in the single-center study, extracting ultrasomics signatures from the intratumoral (In) and peritumoral (Peri) regions of the thyroid. In-B-US, Peri-B-US, In-CEUS, and Peri-CEUS ultrasomics models and a stacking regression model were constructed, and the diagnostic performance of the models was evaluated by comparing the area under the receiver operating characteristic curve (ROC).The In-B-US, Peri-B-US, In-CEUS, Peri-CEUS, and stacking regression model in the training and testing datasets which attained AUC (95% CI) of 0.872(0.812, 0.932), 0.815(0.747, 0.882), 0.739(0.659, 0.819), 0.890(0.836, 0.943), 0.997(0.992, 1.000) and 0.799(0.650, 0.948), 0.851(0.727, 0.974), 0.622(0.440, 0.805), 0.742(0.573, 0.911), 0.867(0.741, 0.992); sensitivity of 82.8%, 89.7%, 71.3%, 74.7%, 96.6% and 69.6%, 78.3%, 43.5%, 78.3%, 91.3%; specificity of 80.6%, 58.2%, 67.2%, 91.0%, 98.5% and 93.8%, 87.5%, 93.3%, 75.0%, 81.2%, respectively. The stacking regression model based on ultrasomics signatures showed favorable calibration and discriminative capabilities. Compared to the stacking regression model, the difference in AUC between the In-B-US and Peri-B-US models was not statistically significant (P > 0.05). However, the difference in AUC between the In-CEUS and Peri-CEUS models was significant (P < 0.05).The application of an ultrasomics approach can effectively predict the benign or malignant nature of TNs accompanied by HT. The diagnostic performance of the ultrasomics model was improved by combining the dual-region and dual-mode of thyroid.Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.