Guo Shi Yan, Zhou Ping, Zhang Yan, Jiang Li Qing, Zhao Yong Feng
The Department of Ultrasound, Xiangya Third Hospital, Central South University, Changsha, China.
Front Oncol. 2021 Oct 14;11:738909. doi: 10.3389/fonc.2021.738909. eCollection 2021.
With the improvement of ultrasound imaging resolution and the application of various new technologies, the detection rate of thyroid nodules has increased greatly in recent years. However, there are still challenges in accurately diagnosing the nature of thyroid nodules. This study aimed to evaluate the clinical application value of the radiomics features extracted from B-mode ultrasound (B-US) images combined with contrast-enhanced ultrasound (CEUS) images in the differentiation of benign and malignant thyroid nodules by comparing the diagnostic performance of four logistic models.
We retrospectively collected and ultimately included B-US images and CEUS images of 123 nodules from 123 patients, and then extracted the corresponding radiomics features from these images respectively. Meanwhile, a senior radiologist combined the thyroid imaging reporting and data system (TI-RADS) and the enhancement pattern of the ultrasonography to make a graded diagnosis of the malignancy of these nodules. Next, based on these radiomics features and grades, logistic regression was used to help build the models (B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model). Finally, the study assessed the diagnostic performance of these radiomics features with a comparison of the area under the curve (AUC) of the receiver operating characteristic curve of four logistic models for predicting the benignity or malignancy of thyroid nodules.
The AUC in the differential diagnosis of the nature of thyroid nodules was 0.791 for the B-US radiomics model, 0.766 for the CEUS radiomics model, 0.861 for the B-US+CEUS radiomics model, and 0.785 for the TI-RADS+CEUS model. Compared to the TI-RADS+CEUS model, there was no statistical significance observed in AUC between the B-US radiomics model, CEUS radiomics model, B-US+CEUS radiomics model, and TI-RADS+CEUS model (>0.05). However, a significant difference was observed between the single B-US radiomics model or CEUS radiomics model and B-US+CEUS radiomics model (<0.05).
In our study, the B-US radiomics model, CEUS radiomics model, and B-US+CEUS radiomics model demonstrated similar performance with the TI-RADS+CEUS model of senior radiologists in diagnosing the benignity or malignancy of thyroid nodules, while the B-US+CEUS radiomics model showed better diagnostic performance than single B-US radiomics model or CEUS radiomics model. It was proved that B-US radiomics features and CEUS radiomics features are of high clinical value as the combination of the two had better diagnostic performance.
随着超声成像分辨率的提高和各种新技术的应用,近年来甲状腺结节的检出率大幅上升。然而,准确诊断甲状腺结节的性质仍存在挑战。本研究旨在通过比较四种逻辑模型的诊断性能,评估从B型超声(B-US)图像和对比增强超声(CEUS)图像中提取的放射组学特征在鉴别甲状腺良恶性结节中的临床应用价值。
我们回顾性收集并最终纳入了123例患者的123个结节的B-US图像和CEUS图像,然后分别从这些图像中提取相应的放射组学特征。同时,一位资深放射科医生结合甲状腺影像报告和数据系统(TI-RADS)及超声检查的增强模式,对这些结节的恶性程度进行分级诊断。接下来,基于这些放射组学特征和分级,使用逻辑回归来构建模型(B-US放射组学模型、CEUS放射组学模型、B-US+CEUS放射组学模型和TI-RADS+CEUS模型)。最后,通过比较四个逻辑模型预测甲状腺结节良恶性的受试者操作特征曲线的曲线下面积(AUC),评估这些放射组学特征的诊断性能。
B-US放射组学模型在鉴别甲状腺结节性质的诊断中,AUC为0.791;CEUS放射组学模型为0.766;B-US+CEUS放射组学模型为0.861;TI-RADS+CEUS模型为0.785。与TI-RADS+CEUS模型相比,B-US放射组学模型、CEUS放射组学模型、B-US+CEUS放射组学模型和TI-RADS+CEUS模型之间的AUC无统计学意义(>0.05)。然而,单一的B-US放射组学模型或CEUS放射组学模型与B-US+CEUS放射组学模型之间观察到显著差异(<0.05)。
在我们的研究中,B-US放射组学模型、CEUS放射组学模型和B-US+CEUS放射组学模型在诊断甲状腺结节的良恶性方面与资深放射科医生的TI-RADS+CEUS模型表现相似,而B-US+CEUS放射组学模型的诊断性能优于单一的B-US放射组学模型或CEUS放射组学模型。证明了B-US放射组学特征和CEUS放射组学特征具有较高的临床价值,两者结合具有更好的诊断性能。