Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China,.
Department of Medical Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Med Ultrason. 2020 Nov 18;22(4):415-423. doi: 10.11152/mu-2501. Epub 2020 Jul 6.
To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.
Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect. Four radiologists with different experience on thyroid ultrasound (Radiologist 1, 2, 3, 4 with 1, 4, 9, 20 years, respectively) analyzed the conventional ultrasound images of each thyroid nodule and made a diagnosis of "benign" or "malignant" based on the TI-RADS category. After referring to S-Detect results, they re-evaluated the diagnoses. The diagnostic performance of radiologists was analyzed before and after referring to the results of S-Detect.
The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of S-Detect were 77.0, 91.3, 65.2, 68.3 and 90.1%, respectively. In comparison with the less experienced radiologists (radiologist 1 and 2), S-Detect had a higher area under receiver operating characteristic curve (AUC), accuracy and specificity (p <0.05). In comparison with the most experienced radiologist, the diagnostic accuracy and AUC were lower (p<0.05). In the less experienced radiologists, the diagnostic accuracy, specificity and AUC were significantly improved when combined with S-Detect (p<0.05), but not for experienced radiologists (radiologist 3 and 4) (p>0.05).
S-Detect may become an additional diagnostic method for the diagnosis of thyroid nodules and improve the diagnostic performance of less experienced radiologists.
比较 S-Detect（一种使用深度学习的计算机辅助诊断系统）在区分不同经验水平的放射科医生甲状腺结节方面的诊断价值，并评估 S-Detect 是否可以提高放射科医生的诊断性能。
2018 年 2 月至 2019 年 10 月，共纳入 181 例患者的 204 个甲状腺结节。一名经验丰富的放射科医生对甲状腺结节进行超声检查，并获得 S-Detect 的结果。4 名甲状腺超声经验不同的放射科医生（放射科医生 1、2、3、4 分别具有 1、4、9、20 年的经验）分析了每个甲状腺结节的常规超声图像，并根据 TI-RADS 类别做出“良性”或“恶性”的诊断。参考 S-Detect 结果后，他们重新评估了诊断。分析了放射科医生在参考 S-Detect 结果前后的诊断性能。
S-Detect 的准确性、敏感性、特异性、阳性预测值和阴性预测值分别为 77.0%、91.3%、65.2%、68.3%和 90.1%。与经验较少的放射科医生（放射科医生 1 和 2）相比，S-Detect 的曲线下面积（AUC）、准确性和特异性更高（p<0.05）。与最有经验的放射科医生相比，诊断准确性和 AUC 较低（p<0.05）。在经验较少的放射科医生中，结合 S-Detect 可显著提高诊断准确性、特异性和 AUC（p<0.05），但对经验丰富的放射科医生（放射科医生 3 和 4）无显著影响（p>0.05）。
S-Detect 可能成为甲状腺结节诊断的一种附加诊断方法，可以提高经验较少的放射科医生的诊断性能。
