Department of Radiological, Oncological, and Pathological Sciences, "Sapienza" University of Rome, Rome, Italy.
Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy.
J Ultrasound. 2020 Jun;23(2):169-174. doi: 10.1007/s40477-020-00453-y. Epub 2020 Apr 3.
Computer-aided diagnosis (CAD) may improve interobserver agreement in the risk stratification of thyroid nodules. This study aims to evaluate the performance of the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification as estimated by an expert radiologist, a senior resident, a medical student, and a CAD system, as well as the interobserver agreement among them.
Between July 2016 and 2018, 107 nodules (size 5-40 mm, 27 malignant) were classified according to the K-TIRADS by an expert radiologist and CAD software. A third-year resident and a medical student with basic imaging training, both blinded to previous findings, retrospectively estimated the K-TIRADS classification. The diagnostic performance was calculated, including sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve.
The CAD system and the expert achieved a sensitivity of 70.37% (95% CI 49.82-86.25%) and 81.48% (61.92-93.7%) and a specificity of 87.50% (78.21-93.84%) and 88.75% (79.72-94.72%), respectively. The specificity of the student was significantly lower (76.25% [65.42-85.05%], p = 0.02).
In our opinion, the CAD evaluation of thyroid nodules stratification risk has a potential role in a didactic field and does not play a real and effective role in the clinical field, where not only images but also specialistic medical practice is fundamental to achieve a diagnosis based on family history, genetics, lab tests, and so on. The CAD system may be useful for less experienced operators as its specificity was significantly higher.
计算机辅助诊断(CAD)可提高甲状腺结节风险分层的观察者间一致性。本研究旨在评估专家放射科医师、高年住院医师、医学生和 CAD 系统对韩国甲状腺影像报告和数据系统(K-TIRADS)分类的评估性能,以及它们之间的观察者间一致性。
在 2016 年 7 月至 2018 年期间,根据 K-TIRADS 对 107 个结节(大小为 5-40mm,27 个恶性)进行分类,由专家放射科医师和 CAD 软件完成。具有基本成像培训的三年级住院医师和医学生,在不知道先前发现的情况下,回顾性地估计 K-TIRADS 分类。计算了诊断性能,包括敏感性、特异性、阳性和阴性预测值以及受试者工作特征曲线下面积。
CAD 系统和专家的敏感性分别为 70.37%(95%CI,49.82%-86.25%)和 81.48%(61.92%-93.7%),特异性分别为 87.50%(78.21%-93.84%)和 88.75%(79.72%-94.72%)。学生的特异性明显较低(76.25%[65.42%-85.05%],p=0.02)。
在我们看来,CAD 对甲状腺结节分层风险的评估在教学领域具有潜在作用,但在临床领域没有实际和有效的作用,因为不仅需要图像,还需要专业医学实践,以基于家族史、遗传学、实验室检查等来做出诊断。CAD 系统对于经验较少的操作人员可能有用,因为其特异性明显更高。