Lee Jeongmin, Kang Bong Joo, Kim Sung Hun, Park Ga Eun
Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
Diagnostics (Basel). 2022 Feb 24;12(3):583. doi: 10.3390/diagnostics12030583.
The present study evaluated the effectiveness of computer-aided detection (CAD) system in screening automated breast ultrasound (ABUS) and analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 846 women who underwent ABUS for screening from January 2017 to December 2017 were included. Commercial CAD was used in all ABUS examinations, and its diagnostic performance and efficacy in shortening the reading time (RT) were evaluated. In addition, we analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 1032 CAD marks were displayed based on the patient and 534 CAD marks on the lesion. Five cases of breast cancer were diagnosed. The sensitivity, specificity, PPV, and NPV of CAD were 60.0%, 59.0%, 0.9%, and 99.6% for 846 patients. In the case of a negative study, it was less time-consuming and easier to make a decision. Among 530 false-positive marks, 459 were identified clearly for pseudo-lesions; the most common cause was marginal shadowing, followed by Cooper's ligament shadowing, peri-areolar shadowing, rib, and skin lesions. Even though CAD does not improve the performance of ABUS and a large number of false-positive marks were detected, the addition of CAD reduces RT, especially in the case of negative screening ultrasound.
本研究评估了计算机辅助检测（CAD）系统在自动乳腺超声（ABUS）筛查中的有效性，并分析了CAD标记的特征及假阳性标记的原因。纳入了2017年1月至2017年12月期间接受ABUS筛查的846名女性。所有ABUS检查均使用商用CAD，并评估其诊断性能及缩短阅片时间（RT）的效果。此外，我们分析了CAD标记的特征及假阳性标记的原因。基于患者共显示了1032个CAD标记，基于病变显示了534个CAD标记。诊断出5例乳腺癌。对于846名患者，CAD的敏感性、特异性、阳性预测值和阴性预测值分别为60.0%、59.0%、0.9%和99.6%。在检查结果为阴性的情况下，耗时更少且更容易做出诊断。在530个假阳性标记中，459个被明确识别为伪病变；最常见的原因是边缘阴影，其次是库珀韧带阴影、乳晕周围阴影、肋骨和皮肤病变。尽管CAD并未提高ABUS的性能且检测到大量假阳性标记，但添加CAD可缩短阅片时间，尤其是在筛查超声结果为阴性的情况下。
