1 Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China .
2 Clinical Trial Unit, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, People's Republic of China .
Thyroid. 2018 Aug;28(8):1024-1033. doi: 10.1089/thy.2017.0525. Epub 2018 Jul 30.
BACKGROUND: Visual interpretation of ultrasound (US) images alone may not be sensitive enough to detect important features of potentially malignant thyroid nodules. The aim of this study was to develop a radiomics score using US imaging to predict the probability for malignancy of thyroid nodules as compared with the Thyroid Imaging, Reporting, and Data System (TI-RADS) scoring criteria proposed by the American College of Radiology (ACR). METHODS: One hundred thirty-seven pathologically proven thyroid nodules from hospital 1 were enrolled as a training cohort, while 95 nodules from hospital 2 served as the validation cohort. A radiomics score using US images was developed from the training cohort. Two junior and two senior radiologists reviewed all images and scored each nodule according to the 2017 updated ACR TI-RADS scoring criteria. Univariate logistic regression analysis was used to develop the prediction models based on the radiomics score and ACR scores. The performance of the models was evaluated and compared with respect to discrimination, calibration, and clinical application in the validation cohort. RESULTS: Univariate regression indicated that the radiomics score and ACR scores were predictors for thyroid nodule malignancy (all p < 0.001). Five prediction models were built based on the above scores. The radiomics score showed good discrimination with an AUC of 0.921 in the training cohort and 0.931 in the validation cohort, which was significantly better than the ACR scores of junior radiologists in both cohorts. Although five models showed good calibration (all p > 0.05), the model based on the radiomics score presented the lowest errors (E max = 0.073 or E aver = 0.028) in predicting and calibrating probabilities. Decision curve analysis demonstrated that the model using the radiomics score added more benefit than using the ACR scores of junior radiologists. CONCLUSION: Compared with ACR TI-RADS evaluation by junior radiologists, the radiomics score showed good performance in predicting malignancy of thyroid nodules in our set of histologically verified thyroid nodules from two tertiary hospitals.
背景：仅凭超声（US）图像的视觉解读可能不够敏感，无法检测出潜在恶性甲状腺结节的重要特征。本研究旨在开发一种基于 US 成像的放射组学评分，以预测甲状腺结节的恶性概率，与美国放射学院（ACR）提出的甲状腺成像、报告和数据系统（TI-RADS）评分标准进行比较。
方法：从医院 1 招募了 137 个经病理证实的甲状腺结节作为训练队列，而医院 2 的 95 个结节作为验证队列。从训练队列中开发了一种基于 US 图像的放射组学评分。两位初级和两位高级放射科医生审查了所有图像，并根据 2017 年更新的 ACR TI-RADS 评分标准对每个结节进行评分。使用单变量逻辑回归分析基于放射组学评分和 ACR 评分开发预测模型。在验证队列中评估和比较模型的性能，包括区分度、校准度和临床应用。
结果：单变量回归表明，放射组学评分和 ACR 评分是甲状腺结节恶性的预测因素（均 p<0.001）。基于以上评分建立了五个预测模型。放射组学评分在训练队列中的 AUC 为 0.921，在验证队列中的 AUC 为 0.931，在两个队列中均显著优于初级放射科医生的 ACR 评分。虽然五个模型均具有良好的校准度（均 p>0.05），但基于放射组学评分的模型在预测和校准概率方面的误差最小（E max=0.073 或 E aver=0.028）。决策曲线分析表明，使用放射组学评分的模型比使用初级放射科医生的 ACR 评分能带来更多的获益。
结论：与初级放射科医生的 ACR TI-RADS 评估相比，在我们的两个三级医院的经组织学验证的甲状腺结节中，放射组学评分在预测甲状腺结节恶性方面表现出良好的性能。
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