Yongping Liang, Zhou Ping, Juan Zhang, Yongfeng Zhao, Liu Wengang, Shi Yifan
The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
JMIR Med Inform. 2020 Mar 2;8(3):e16334. doi: 10.2196/16334.
Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed.
The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size.
We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions>2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis.
For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P<.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions>2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P<.001) at the combined-CAD mode, respectively.
S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer.
Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094.
计算机辅助诊断（CAD）在超声检查中作为一种辅助工具，供放射科医生用于乳腺病变诊断。既往研究表明，CAD可提高放射科医生的诊断效能。然而，尚未评估根据大小（2 cm以下或以上）对乳腺病变进行CAD的最佳应用方式。
本研究旨在比较不同放射科医生使用CAD检测大小小于和大于2 cm的乳腺肿瘤的效能。
我们前瞻性纳入了261例连续患者（平均年龄43岁；年龄范围17 - 70岁），这些患者以乳腺肿块为突出症状，共包含398个病变（148个病变>2 cm，其中79个为恶性，69个为良性；250个病变≤2 cm，其中71个为恶性，179个为良性）。一名有1年超声检查经验的新手放射科医生和一名有5年超声检查经验的经验丰富放射科医生分别被分配阅读无CAD辅助的超声图像，然后在第二次阅读时应用CAD的S-Detect软件再次阅读。随后，我们将两位阅读者在两次阅读（无CAD和结合CAD）时的诊断效能与乳腺影像进行比较。采用配对数据的McNemar检验进行统计分析。
对于新手阅读者，在≤2 cm的病变中，受试者操作特征曲线（AUC）下面积从无CAD模式下的0.74（95%可信区间0.67 - 0.82）提高到结合CAD模式下的0.88（95%可信区间0.83 - 0.93；P<0.001）。对于经验丰富的阅读者，AUC从0.84（95%可信区间0.77 - 0.90）提高到0.90（95%可信区间0.86 - 0.94；P = 0.002）。在>2 cm的病变中，新手阅读者的AUC从0.81适度降至0.80，经验丰富的阅读者从0.90降至0.82。新手和经验丰富的阅读者在≤2 cm病变中的敏感度分别从无CAD模式下的61.97%和73.23%提高到结合CAD模式下的90.14%和97.18%（均P<0.001）。
S-Detect是一种可行的诊断工具，可提高新手和经验丰富阅读者的敏感度，同时也提高了≤2 cm病变的阴性预测值和AUC，在乳腺癌临床诊断中显示出重要应用价值。
中国临床试验注册中心ChiCTR1800019649；http://www.chictr.org.cn/showprojen.aspx?proj=33094 。
