Choi Young Jun, Baek Jung Hwan, Park Hye Sun, Shim Woo Hyun, Kim Tae Yong, Shong Young Kee, Lee Jeong Hyun
1 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
2 Department of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine , Seoul, Korea.
Thyroid. 2017 Apr;27(4):546-552. doi: 10.1089/thy.2016.0372. Epub 2017 Feb 28.
An initial clinical assessment is described of a new, commercially available, computer-aided diagnosis (CAD) system using artificial intelligence (AI) for thyroid ultrasound, and its performance is evaluated in the diagnosis of malignant thyroid nodules and categorization of nodule characteristics.
Patients with thyroid nodules with decisive diagnosis, whether benign or malignant, were consecutively enrolled from November 2015 to February 2016. An experienced radiologist reviewed the ultrasound image characteristics of the thyroid nodules, while another radiologist assessed the same thyroid nodules using the CAD system, providing ultrasound characteristics and a diagnosis of whether nodules were benign or malignant. The diagnostic performance and agreement of US characteristics between the experienced radiologist and the CAD system were compared.
In total, 102 thyroid nodules from 89 patients were included; 59 (57.8%) were benign and 43 (42.2%) were malignant. The CAD system showed a similar sensitivity as the experienced radiologist (90.7% vs. 88.4%, p > 0.99), but a lower specificity and a lower area under the receiver operating characteristic (AUROC) curve (specificity: 74.6% vs. 94.9%, p = 0.002; AUROC: 0.83 vs. 0.92, p = 0.021). Classifications of the ultrasound characteristics (composition, orientation, echogenicity, and spongiform) between radiologist and CAD system were in substantial agreement (κ = 0.659, 0.740, 0.733, and 0.658, respectively), while the margin showed a fair agreement (κ = 0.239).
The sensitivity of the CAD system using AI for malignant thyroid nodules was as good as that of the experienced radiologist, while specificity and accuracy were lower than those of the experienced radiologist. The CAD system showed an acceptable agreement with the experienced radiologist for characterization of thyroid nodules.
本文描述了一种新的、可商购的、使用人工智能(AI)的甲状腺超声计算机辅助诊断(CAD)系统的初步临床评估,并评估了其在诊断甲状腺恶性结节及对结节特征进行分类方面的性能。
2015年11月至2016年2月连续纳入甲状腺结节诊断明确(无论良性或恶性)的患者。一位经验丰富的放射科医生审查甲状腺结节的超声图像特征,另一位放射科医生使用CAD系统评估相同的甲状腺结节,提供超声特征并诊断结节是良性还是恶性。比较经验丰富的放射科医生与CAD系统之间的诊断性能及超声特征的一致性。
共纳入89例患者的102个甲状腺结节;其中59个(57.8%)为良性,43个(42.2%)为恶性。CAD系统显示出与经验丰富的放射科医生相似的敏感性(90.7%对88.4%,p>0.99),但特异性较低,且受试者操作特征(AUROC)曲线下面积较小(特异性:74.6%对94.9%,p=0.002;AUROC:0.83对0.92,p=0.021)。放射科医生与CAD系统之间对超声特征(成分、方向、回声性和海绵状)的分类具有高度一致性(κ分别为0.659、0.740、0.733和0.658),而边界显示出一般一致性(κ=0.239)。
使用AI的CAD系统对甲状腺恶性结节的敏感性与经验丰富的放射科医生相当,而特异性和准确性低于经验丰富的放射科医生。CAD系统在甲状腺结节特征描述方面与经验丰富的放射科医生显示出可接受的一致性。