评估从甲状腺灰阶超声图像、常规超声造影（CEUS）及高帧频超声造影（H-CEUS）图像中提取的影像组学特征对于甲状腺结节良恶性诊断的价值及其诊断效能。

回顾性收集并最终纳入2021年1月至2022年4月中国医科大学附属盛京医院的186例患者的186个结节的灰阶超声图像、常规CEUS及H-CEUS视频数据，分别选择灰阶超声图像中结节最大长轴的横向切面、纵向切面以及常规CEUS和H-CEUS视频中造影剂到达时间、达峰时间、平均消退时间对应的3个关键帧图像这8张图像进行分析并提取相应的影像组学特征，采用逻辑回归和随机森林建立3种影像组学模型（灰阶超声、灰阶超声+常规CEUS和灰阶超声+H-CEUS模型），最后，评估这些影像组学特征的诊断效能，比较3种模型在测试集（38个结节）中的曲线下面积（AUC）、敏感度、特异度、阳性预测值（PPV）、阴性预测值（NPV）、准确性、F1评分。

共提取186例患者186个结节的9000个影像组学特征，灰阶超声模型在测试集中的AUC、准确性、敏感度、特异度、PPV、NPV和F1评分分别为0.81、81.6%、68.8%、90.9%、84.6%、80.0%和0.76。灰阶超声+常规CEUS模型在测试集中的AUC、准确性、敏感度、特异度、PPV、NPV和F1评分分别为0.87、84.2%、68.8%、95.5%、91.7%、80.8%和0.79。灰阶超声+H-CEUS模型在测试集中的AUC、准确性、敏感度、特异度、PPV、NPV和F1评分分别为0.91、86.8%、87.5%、86.4%、82.4%、90.5%和0.85。

基于灰阶超声图像、常规CEUS和H-CEUS关键帧图像的影像组学特征对于鉴别良恶性甲状腺结节具有一定价值，灰阶超声+H-CEUS图像的影像组学模型的诊断效能优于灰阶超声模型和灰阶超声+常规CEUS影像组学模型。

To evaluate the clinical application and diagnostic efficiency of the radiomics features extracted from thyroid B-mode ultrasound (B-US) images, contrast-enhanced ultrasound (CEUS) images, and high-frame-rate contrast-enhanced ultrasound (H-CEUS) images in classifying and predicting benign and malignant thyroid nodules.

We retrospectively collected and ultimately included B-US images, CEUS videos, and H-CEUS videos of 186 nodules from 186 patients. Next, we selected the following eight images to manually select the regions of interest: nodule in transverse section, nodule in longitudinal section, and the "rise time" frame, "time to peak" frame, and "mean transit time" frame of both CEUS and H-CEUS videos, and then extracted the corresponding radiomics features from these images. We established three radiomics models (B-US radiomics model, B-US+CEUS radiomics model, and B-US+H-CEUS radiomics model) based on these radiomics features by Logistic regression and random forest, and assessed the diagnostic performance of these radiomics features. Finally, the area under curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), accuracy (ACC), and F1-score in the test set (n=38) were calculated and compared.

A total of 9000 significant radiomics features of 186 nodules from 186 patients were extracted. The AUC in the differential diagnosis of the nature of thyroid nodules was 0.81 for the B-US radiomics model, 0.87 for the B-US+CEUS radiomics model, and 0.91 for the B-US+H-CEUS radiomics model. The accuracy, sensitivity, specificity, PPV, NPV, and F1 scores of the B-US radiomics model in the test set were 81.6%, 68.8%, 90.9%, 84.6%, 80.0%, and 0.76, respectively; the corresponding values were 84.2%, 68.8%, 95.5%, 91.7%, 80.8%, and 0.79 for the B-US+CEUS radiomics model, and 86.8%, 87.5%, 86.4%, 82.4%, 90.5%, and 0.85 for the B-US+H-CEUS radiomics model.

Radiomics features based on B-US images, CEUS, and H-CEUS key frame images have appreciated clinical value in differentiating benign and malignant thyroid nodules. The diagnostic efficacy of the B-US+H-CEUS radiomics model is better than that of other radiomics models.