探讨常规超声与S-Detect技术在乳腺病灶良恶性鉴别诊断中的效能比较。

选取2018年6月至7月在中国医科大学附属第一医院经手术病理证实的367例乳腺病灶患者，共468个病灶。所有病灶分别由3名不同年资（1、4、7年）乳腺超声医师进行二维超声成像（静态图像及动态图像）的两次乳腺超声影像报告与数据系统（BI-RADS）分类以及计算机S-Detect分类，通过绘制不同BI-RADS分类诊断组的ROC曲线，确定最佳诊断界值，以进行各组BI-RADS分类的良恶性统计，以病理结果为"金标准"，应用诊断试验四格表分别计算不同BI-RADS分类诊断组及S-Detect分类组对乳腺病灶良恶性诊断的敏感度、特异度、准确性、阳性预测值及阴性预测值，采用χ²检验分别将各BI-RADS分类组诊断效能与S-Detect分类组进行比较。绘制各组的ROC曲线，应用Z检验分别将各BI-RADS分类组ROC曲线下面积与S-Detect分类组进行比较。

468个乳腺病灶术后病理诊断良性313个，恶性155个。通过绘制不同BI-RADS分类诊断组的ROC曲线，确定最佳诊断界值为BI-RADS 4a类。S-Detect分类诊断敏感度93.5%明显高于低年资医师静态图像BI-RADS分类诊断69.0%及低年资医师动态录像BI-RADS分类诊断72.3%，差异有统计学意义（χ²=30.627、24.785，P均<0.001），S-Detect分类诊断特异度83.7%，明显低于中年资医师动态图像BI-RADS分类诊断92.0%，差异有统计学意义（χ²=10.124，P=0.001），其余各诊断效能差异均无统计学意义（P均>0.05）。S-Detect分类诊断曲线下面积0.917高于低年资医师两次（静态图像及动态图像）BI-RADS分类0.790、0.803，差异均有统计学意义（Z=5.271、4.693，P均<0.0001）；S-Detect分类诊断曲线下面积与中年资医师静态BI-RADS分类0.917比较，差异无统计学意义（P>0.05），低于中年资医师动态BI-RADS分类0.941，差异有统计学意义（Z=4.327，P<0.0001）；S-Detect分类诊断曲线下面积均低于高年资医师两次BI-RADS分类0.946、0.959，差异均有统计学意义（Z=4.225、5.477，P均<0.0001）。

S-Detect分类技术可以达到中年资医师静态图像BI-RADS分类的诊断水平，但低于其动态图像的诊断水平。

To compare the efficacy of conventional ultrasound and S-Detect in the differential diagnosis of benign and malignant breast lesions.

A total of 468 lesions were identified from 367 patients with breast lesions confirmed by surgery and pathology from June to July in 2018. Both the man-made BI-RADS classifications (still images and dynamic videos identified by three specialist physicians with 1, 4, and 7 years of experience, respectively) and computer S-Detect classification were performed. By plotting the ROC curves of different BI-RADS classification groups, the optimal diagnostic cutoff values were determined. Using pathological results as the gold standard, and the diagnostic test four grids were used to calculate the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of different BI-RADS classifications and S-Detect classification in the diagnosis of benign and malignant breast lesions, and chi-square tests were used to compare the diagnostic efficacy between groups. The ROC curves of each group were plotted, and the area under the ROC curve (AUC) of each BI-RADS classification group was compared with the S-Detect classification group by the Z-test.

Of 468 breast lesions, 313 were confirmed to be benign lesions and 155 confirmed to be malignant lesions by pathological biopsy. The optimal diagnostic cut-off value was determined to be BI-RADS 4a by plotting the ROC curves for different BI-RADS classification diagnostic groups. The sensitivity of S-Detect classification diagnosis was 93.5%, which was significantly higher than those (69.0% and 72.3%) of the 1-year physician by BI-RADS classifications (still images and dynamic videos, respectively, χ²=30.627 and 24.785, respectively, P=0.000 for both). The specificity of S-Detect classification diagnosis was 83.7%, which was significantly lower than that (92.0%) of the 4-year physician by BI-RADS classification (dynamic videos, χ²=10.124, P=0.001). The differences in other diagnostic comparisons were not statistically significant (P>0.05). The AUC of S-Detect classification was 0.917, significantly higher than those (0.790 and 0.803) of the 1-year physician by BI-RADS classifications (still images and dynamic videos, respectively, Z=5.271 and 4.693, respectively, P<0.0001 for both). The difference between the AUC of S-Detect and that of 4-year physician by BI-RADS classification (still images) was not statistically significant (P>0.05). The AUC of S-Detect was lower than that (0.941) of 4-year physician by BI-RADS classification (dynamic videos, Z=4.327, P<0.0001). The AUC of S-Detect classification was lower than those (0.946 and 0.959) of the 7-year physician by BI-RADS classifications (still images and dynamic videos, respectively, Z=4.225 and 5.477, respectively; P<0.0001 for both).

The S-Detect classification can achieve the diagnostic level of the 4-year physician by BI-RADS classification of still images, but is lower than that by dynamic videos.